Each article summary must list the citation(APA format) for the article being summarized and provide 800-1000 words i which the student summarizes the main questions and conclusions of the paper and explains how the conclusions of the paper are useful to someone working in financial management of a corporation in the students current or proposed career path. The text of each summary must be 12-point Times New 

Roman font, with one-inch top, bottom, left, and right margins, and each summary must begin on a new page.

Journal of Corporate Finance 66 (2021) 10183

3

Available online 10 December 2020
0929-1199/© 2020 Elsevier B.V. All rights reserved.

The role of institutional investors in corporate and
entrepreneurial finance

Thomas J. Chemmanur a, Gang Hu b, *, K.C. John Wei c

a Carroll School of Management, Boston College, Fulton Hall 336, Chestnut Hill, MA 02467, USA
b School of Accounting and Finance, Hong Kong Polytechnic University, M1038, Li Ka Shing Tower, Hung Hom, Kowloon, Hong Kong
c School of Accounting and Finance, Hong Kong Polytechnic University, M1043, Li Ka Shing Tower, Hung Hom, Kowloon, Hong Kong

A R T I C L E I N F O

Keywords:
Institutional investor
Corporate finance
Trading
Abel Noser data

A B S T R A C T

Institutional investors, collectively the majority shareholders of most publicly traded corpora-
tions, play important roles in almost all aspects of corporate finance. This special issue puts
together sixteen papers covering a wide range of topics, such as M&As, capital structure, bonds
and loans, corporate governance, IPOs, VCs, SEOs, broker/underwriter relationships, behavioral
finance, corporate disclosure, and regulation. These special issue papers demonstrate that insti-
tutional investors, a traditional focus of investments research, are worthy of continued and
further academic inquiry in many corporate finance topics. In terms of directions for future
research, we believe that the availability of new datasets (or existing datasets not yet widely used
in corporate finance) and the application of new or unique research methodologies could bear
fruit for researchers, as demonstrated by some papers in this special issue. In terms of datasets, the
success of Abel Noser institutional trading data serves as a good example.

1. Introduction

Over the last several decades, institutional investors, such as mutual funds, hedge funds, endowment funds, retirement or pension
funds, insurance companies, sovereign wealth funds, and private equity firms, have come to dominate the global financial markets. In
the U.S., institutional investors’ stake in the average firm rose from 20% in 1980 to 60% in 2014 (see, e.g., Bai et al., 2016). Hence,
institutional investors play an increasingly important role in all aspects of the financial markets.

If institutional investors are collectively the majority shareholders of most publicly traded corporations, what roles do they play in
corporate financial decision- making, corporate governance, and corporate events? Building on the literature and seminal works such
as Gillan and Starks (2000), this special issue of the Journal of Corporate Finance seeks to further our understanding of the fundamental
role institutional investors play in all aspects of corporate finance in a contemporary and global setting. This collection of papers was
further selected from those presented at the JCF Special Issue Conference held at the Hong Kong Polytechnic University in December
2018. Because of a policy change by the publisher, special issue papers were not published in one printed volume, but were instead
published in scattered volumes as they were accepted. Therefore, we have organized and put all of the special issue papers into context
to highlight the overall theme: the important roles of institutional investors in corporate finance.

In his keynote address at the conference, “State pricing, effectively complete markets, and corporate finance,” Mark Grinblatt
discussed how event study, panel regression, and difference-in-difference techniques, although widely used in corporate finance, may

* Corresponding author.
E-mail addresses: chemmanu@bc.edu (T.J. Chemmanur), gang.hu@polyu.edu.hk (G. Hu), john.wei@polyu.edu.hk (K.C.J. Wei).

Contents lists available at ScienceDirect

Journal of Corporate Finance

journal homepage: www.elsevier.com/locate/jcorpfin

https://doi.org/10.1016/j.jcorpfin.2020.101833
Received 18 November 2020; Accepted 8 December 2020

mailto:chemmanu@bc.edu

mailto:gang.hu@polyu.edu.hk

mailto:john.wei@polyu.edu.hk

www.sciencedirect.com/science/journal/09291199

https://www.elsevier.com/locate/jcorpfin

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Journal of Corporate Finance 66 (2021) 101833

2

be inappropriate if corporate events are anticipated to some degree, as most events are. Grinblatt and Wan (2020) propose options as
an additional model-free source of information to identify the likelihood and impact of corporate events. The other special issue papers
are organized under six topics based on the different roles institutional investors play in corporate finance.

2. Institutional investors and corporate finance

2.1. Institutional investors, M&a, and capital structure

M&As are perhaps one of the most significant corporate events and tend to be highly information intensive and sensitive periods for
both acquirers and targets. Ismail et al. (2019) find that institutional investors tend to accumulate shares of firms that announce
acquisitions, especially when the acquirer discloses synergy forecasts. Such accumulations of shares are stronger when the disclosed
synergies are greater. The authors interpret these findings as evidence consistent with the idea that institutional investors are attracted
to situations in which their superior access to management and analysts provides an informational advantage. They also find that these
patterns are stronger for hedge funds, which are typically believed to possess a greater informational advantage. In addition, stock
prices respond favorably in the quarter following an acquisition announcement when higher institutional holding is revealed.

Also in the context of M&As, Chang et al. (2020) identify an important channel, the acquisition of public targets, through which
governance through trading (GTT) by institutional investors improves firm value via the better acquisition of public targets. The
authors further find that the effect of GTT is stronger when managers’ contractual incentives are better aligned with shareholders, that
firms with higher GTT also have better operational performance and valuation and lower default risk, and that the effect of GTT only
exists for less financially constrained firms and non-all-cash M&As.

In a rather unique survey study, Brown et al. (2019) report that more than 82% of institutional investors believe they influence
corporate capital structure decisions, especially for smaller, younger, and more financially constrained firms. Unlike corporate
managers, institutional investors consider the agency costs of free cash flow to be important drivers of capital structure. Institutional
investors’ responses also support pecking order and market timing theories. Most institutional investors find financial constraints
important, with components of the Kaplan–Zingales and Whited–Wu indexes dominating other proxies. Overall, their findings suggest
a first-order effect of institutional investor preferences on corporate capital structure decisions.

2.2. Institutional investors, bonds, and loans

Fixed-income securities such as bonds and loans, although less studied compared with equity, are just as important for firms’
financing. Institutional investors also play an active role in the corporate bond and loan markets. Using Abel Noser (ANcerno)
institutional trading data, Bhattacharya et al. (2019) find that a set of small institutional investors consistently follow credit ratings
issued by an investor-paid rating agency in their trading decisions.1 Although rating information is credit related, the authors find that
these institutional followers often respond more strongly to investor-paid ratings than to other influential trading signals, such as
earnings announcements, analysts’ earnings forecast revisions, and recommendation changes. Institutional followers outperform non-
followers, and show improved trading performance after becoming followers. Based on this evidence, the authors conclude that
investor-paid rating agencies offer small institutional investors a cost-effective alternative to in-house research.

Dahiya et al. (2020) examine whether banks worry about expropriation when an activist hedge fund targets their borrowers, or
alternatively, whether banks are reassured that their borrowers will perform better after such targeting. The authors find that target
firms pay higher spreads on post-activism loans and are more likely to post collateral on post-activism loans. Target firms experience
tighter loan terms compared with similar, non-target firms. Targets with high stock returns around activism face tighter loan terms
afterward. The authors argue that higher interest rates and greater collateral requirements reflect the increased credit risk of these
borrowers partly because of the possibility of wealth expropriation by their shareholders. The evidence suggests that an increase in
equity value because of an activist’s targeting may be partially based on wealth expropriation by creditors.

In the setting of insurance companies, Chen et al. (2020) examine the hypothesis that investors facing more operating risk may be
more risk averse in their investment decisions. Specifically, the authors study how operating risk from underwriting insurance policies
affects insurers’ risk-taking behavior in their portfolio investments. The authors find that insurers with greater operating risk have
lower credit risk exposure in their bond investments and invest less in risky bonds and equities. They also find that insurer portfolio risk
exposure is highly sensitive to permanent operating risk but insensitive to transitory operating risk, and that transitory operating risk is
significantly related to portfolio risk when insurers face tight financial constraints. Their findings suggest a substitutive effect of
operating risk on investment decisions by financial institutions.

2.3. Institutional investors and corporate governance

Following seminal works such as Gillan and Starks (2000), a stream of academic literature has developed that examines the
important role institutional investors play in corporate governance. Fu et al. (2020) examine the effects of shareholder investment
horizons on insider trading. The authors find that long shareholder investment horizons reduce the propensity for informed insider

1 For details about Abel Noser (ANcerno) institutional trading data, please see the “Abel Noser data paper,” Hu et al. (2018), and the Abel Noser
(ANcerno) data page: http://ganghu.org/an.

T.J. Chemmanur et al.

http://ganghu.org/an

Journal of Corporate Finance 66 (2021) 101833
3

trades, and that this effect is stronger in firms with higher litigation risk, for insider sales, and in firms that are poorly monitored by
other agents. Long-horizon shareholders are likely to impose policies that restrain insider trading and tend to foster a more transparent
information environment.

Ho et al. (2020) examine the relation between large shareholder ownership and board governance in firms. Using a dataset of
Taiwanese firms, they find that greater family ownership is associated with a more advisory board, whereas greater institutional
ownership is associated with a more monitoring board. They also find that types of institutional ownership influence board governance
in different ways. Their study provides evidence of the multidimensional nature of the relation between large shareholder ownership
types and board governance.

2.4. Institutional investors and entrepreneurial finance

Institutional investors have been playing an increasingly important role in the early-stage financing of entrepreneurial and private
firms. Using Abel Noser institutional trading data, Nefedova and Pratobevera (2020) find results indicating that some institutions hide
their sell trades and break their laddering agreements with their underwriters. The authors find that institutions buy IPO shares
through lead underwriters but sell them through other brokers in the aftermarket, and that this behavior is pronounced in cold IPOs
and is limited to the first month after the issue. They also find that the intention to flip IPO allocations is not an important motive for
hiding sell trades from underwriters. Hiding sell trades is an effective strategy to circumvent underwriters’ monitoring mechanisms:
the more institutions hide their sell trades, the less they are penalized in subsequent IPO allocations.

Li et al. (2020) examine whether the certification effect of venture capitalists’ (VCs) extends to firm’s potential customers and
whether, by certifying firms’ values to potential customers, VCs provide value to firms. Using weekly trading data from P2P lending
platforms in China, the authors find that lenders and facilitated loans increase after VC investment announcements, and that this effect
increases with VCs’ reputation and the extent of information asymmetry. They also find that this effect is beyond the effect of news,
advertising, and funding, and that VC-backed platforms are less likely to default.

Using a manually collected dataset of VCs’ political connections, Wang and Wu (2020) examine the impact of VCs’ political
connections on their portfolio companies and investigate the potential benefits and costs that politically connected VCs bring to their
portfolio companies. On the benefits side, the authors find that companies backed by politically connected VCs are more likely to
obtain IPO approval. However, these VCs are more likely to acquire equity in the company at a significant discount and to invest
shortly before the IPO application. They also find that politically connected VCs do not play a greater role in monitoring. Politically
connected VC-backed companies experience more underpricing at IPO, and politically connected VCs exit earlier and their companies
underperform after IPO.

In recent years, institutional investors have started to invest increasingly in private firms, thus making private equity capital
increasingly cheaper for such firms. This, along with the removal of regulatory barriers for raising venture capital at the state level that
occurred in the late nineties (see, e.g., Ewens and Farre-Mensa, 2020), have had important consequences for the reduction in volume of
IPOs in the U.S. post-2000 (see, e.g., Chemmanur et al., 2020). We expect the role of institutional investments in private firms to be an
increasingly important research topic in the future as new datasets useful for this research become available.

2.5. Institutional investors and broker/underwriter relationships

Using Abel Noser institutional trading data, Anand et al. (2019) investigate the influence of institutional trading on the likelihood
of winning the lead underwriting mandate for a large sample of secondary offerings. The authors find that the intensity of the un-
derwriting bank’s trading on the likelihood of winning the lead underwriting mandate is positive and significant. Analyst coverage is
an effective complement to trading intensity in winning the underwriting mandate, and bank reputation is a significant substitute.
Lead bank trading intensity has a significant beneficial effect on the SEO pricing discount. Banks that do not have a high level of trading
in the issuer’s stock can effectively compete by adding a co‑lead underwriter to the underwriting syndicate.

Chen et al. (2019) use earnings announcements to analyze the trading behavior and associated price effects of institutions that have
a lending or underwriting relationship with client firms and also hold client firms’ shares. The authors find that buying support from
relationship institutions mitigates the negative impact of earnings surprises on client firms’ stock prices, predicts subsequent negative
earnings surprises, and is also associated with less selling by independent institutions holding the same firms’ shares. Price reactions
for firms without relationship institutions are significantly larger, and price support from relationship institutions appears to help
resolve the uncertainty accompanying clients’ temporary earnings shocks, thus reducing noise in the capital markets.

2.6. Institutional investors, behavior, disclosure, and regulation

Using Abel Noser institutional trading data, Chakravarty and Ray (2020) examine the short-term trading performance of institu-
tional investors using a marked-to-market based fair-value method. Their findings differ significantly from and fall in between those
that use the historical cost method. The authors find that managers do not have superior skill after including transaction costs for trades
with a holding period of four weeks or less. Institutional investors engage in short-term trading despite losses primarily for liquidity
reasons, and pension fund and mutual fund managers have different trading behaviors. The authors also provide evidence of potential
behavioral biases by institutional traders, such as the disposition effect and overconfidence.

Cheng et al. (2020) examine institutional investors’ responses to corporate disclosure quality conditional on market states. The
authors find that market states influence institutions’ reactions to corporate disclosure quality, and that this influence is stronger when

T.J. Chemmanur et al.

Journal of Corporate Finance 66 (2021) 101833

4

investors’ access to inside information is limited. They also find that corporate disclosures reduce information asymmetry to a greater
extent in downturns, and that transient institutional ownership in downturns provides price support and stabilizes volatility.

Also using Abel Noser institutional trading data, Duong and Meschke (2020) examine how increased regulatory attention affects
the trading behavior of U.S. mutual funds, leading to the rise and fall of portfolio pumping. The authors find that increased regulatory
attention reduces portfolio pumping by U.S. mutual funds, and causes spikes in fund indices, fund holdings, and institutional trading to
decline. Such declines are largest around year-ends and for small-cap and better-performing funds, and occur faster for funds head-
quartered near SEC regional offices. These findings are consistent with and reconcile the findings in both Carhart et al. (2002) and Hu
et al. (2014).

3. Conclusion and directions for future research

In conclusion, this JCF special issue includes a keynote address and sixteen papers examining the role of institutional investors in
various aspects of corporate finance, such as M&As, capital structure, bonds and loans, corporate governance, IPOs, VCs, SEOs, broker/
underwriter relationships, behavioral finance, corporate disclosure, and regulation.

Our sincere hope is that, by putting together a collection of such papers, we have demonstrated that institutional investors, a
traditional focus of academic research in investments, play important roles in almost all areas of corporate finance that are worthy of
continued and further academic inquiry. In terms of directions for future research, we believe the availability of new datasets (or
existing datasets not yet widely used in corporate finance) and the application of new or unique research methodologies could bear
fruit for researchers, as successfully demonstrated by some of the papers in this special issue. For example, Brown et al. (2019) conduct
a survey analysis to examine the role of institutional investors in corporate capital structure decisions.

In terms of datasets, the success of Abel Noser (ANcerno) institutional trading data serves a good example. Originally a market
microstructure dataset, it is now widely used in all areas of finance: corporate finance, investments, and market microstructure. In
addition, the data are increasingly used in accounting. The Abel Noser data page (http://ganghu.org/an) currently lists 94 (and
counting) publications using Abel Noser data thus far, compared with the 55 publications listed in Hu et al. (2018). In other words,
there have been 39 new publications using Abel Noser data within the last two years, compared to 55 publications during the previous
26 years, 1993–2018. We anticipate that this strong publication trend will continue in the foreseeable future, as many research topics,
in corporate finance and otherwise, can be reexamined more in-depth and/or from fresh angles with the data. For example, both
Chemmanur et al. (2010), and Nefedova and Pratobevera (2020) study institutional trading around IPOs; both Chemmanur et al.
(2009), and Anand et al. (2019) examine institutional trading and SEOs; both Hu et al. (2014), and Duong and Meschke (2020)
investigate the quarter- and year-end trading activities of institutional investors, all using Abel Noser data.

Acknowledgements

We thank the editor, Douglas Cumming, for comments and tremendous support for the JCF Special Issue Conference on “The Role
of Institutional Investors in Corporate and Entrepreneurial Finance” held at the Hong Kong Polytechnic University in December 2018.
Special thanks go to Agnes Cheng and JC Lin for supporting the conference. We thank conference participants and special issue authors
for their contribution and suggestions. All remaining errors or omissions are our own.

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• The role of institutional investors in corporate and entrepreneurial finance

1 Introduction
2 Institutional investors and corporate finance
2.1 Institutional investors, M&a, and capital structure
2.2 Institutional investors, bonds, and loans
2.3 Institutional investors and corporate governance
2.4 Institutional investors and entrepreneurial finance
2.5 Institutional investors and broker/underwriter relationships
2.6 Institutional investors, behavior, disclosure, and regulation
3 Conclusion and directions for future research
Acknowledgements
References

Journal of Corporate Finance 66 (2021) 10181

3

Available online 3 December 2020
0929-1199/© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Which criteria matter when impact investors screen
social enterprises?

Joern H. Block a, b, c, *, Mirko Hirschmann a, Christian Fisch a, b

a Trier University, Faculty of Management, 54296 Trier, Germany
b Erasmus School of Economics, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, the Netherlands
c Universität Witten/Herdecke, Wittener Institut für Familienunternehmen, Germany

A R T I C L E I N F O

JEL codes:
G24
L31
G11
M13
G30

Keywords:
Impact investing
Entrepreneurial finance
Social enterprise
Conjoint analysis
Screening criteria

A B S T R A C T

Impact investors pursue both financial and social goals and have become an important source of
funding for social enterprises. Our study assesses impact investor criteria when screening social
enterprises. Applying an experimental conjoint analysis to a sample of 179 impact investors, we
find that the three most important criteria are the authenticity of the founding team, the
importance of the societal problem targeted by the venture, and the venture’s financial sustain-
ability. We then compare the importance of these screening criteria across different types of
impact investors (i.e., donors, equity investors, and debt investors). We find that donors pay more
attention to the importance of the societal problem and less attention to financial sustainability
than do equity and debt investors. Additionally, equity investors place a higher value on the large-
scale implementation of the social project than do debt investors. We contribute to the nascent
literature on impact investing by documenting how impact investors make investment decisions
and by providing a nuanced view of different investor types active in this novel market. Practical
implications exist for both impact investors and social enterprises.

1. Introduction

Impact investors pursue financial and social goals. Similar to traditional investors, impact investors aim for market-rate financial
returns through the provision of financial assets (e.g., Brest and Born, 2013; Louche et al., 2012). However, in addition to these
financial goals, impact investors aim for a positive environmental or social impact of their investment (e.g., Brest and Born, 2013; Harji
and Jackson, 2012). Impact investing has grown in importance, and impact investors are an increasingly important source of funding
for social enterprises (SE) (e.g., Geczy et al., 2019; The Economist, 2017). Since the advent of impact investing in 2007 (Rodin and
Brandenburg, 2014), the market has grown to include 1340 impact investment organizations, with USD 502 billion in assets worldwide
(Global Impact Investing Network (GIIN), 2019a). In addition, the increasing importance of impact investing has been accompanied by
a surge in scholarly interest (e.g., Barber et al., 2020; Bugg-Levine and Emerson, 2011; Chowdhry et al., 2019).

Thus far, however, we know little about the investment process of impact investors. In particular, we do not know which criteria
matter when impact investors screen SEs. This is an important gap in the literature that needs to be closed because SEs looking for
funding require knowledge about the criteria they should focus on when applying for funding from impact investors. Since the goals of
traditional investors differ from those of impact investors, the investment selection processes and the screening criteria of impact

* Corresponding author at: Trier University, Faculty of Management, 54296 Trier, Germany.
E-mail addresses: block@uni-trier.de (J.H. Block), m.hirschmann@uni-trier.de (M. Hirschmann), cfisch@uni-trier.de (C. Fisch).

Contents lists available at ScienceDirect

Journal of Corporate Finance

journal homepage: www.elsevier.com/locate/jcorpfin

https://doi.org/10.1016/j.jcorpfin.2020.101813
Received 7 February 2020; Received in revised form 26 November 2020; Accepted 29 November 2020

mailto:block@uni-trier.de

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investors and traditional investors likely differ as well (e.g., Chowdhry et al., 2019; Hartzmark and Sussman, 2018). Hence, the
findings of the established literature on the screening criteria of traditional entrepreneurial finance investors (e.g., Block et al., 2019;
Gompers et al., 2020; Kaplan and Strömberg, 2004) cannot be applied to the context of impact investing. To close this gap in the
literature, our study assesses the following three research questions: Which screening criteria do impact investors use, what is their
relative importance, and how do they differ between different types of impact investors?

We use a multimethod design to investigate these research questions. First, we conduct 12 qualitative interviews with experts to
identify impact investors’ most important screening criteria. These criteria relate to the social impact (i.e., the importance of the
societal problem that is addressed by the SE and the large-scale implementation of the solution), the founding team (i.e., authenticity
and professional background), and the business (i.e., financial sustainability, degree of innovation, and proof of concept) of the SE.1

Based on these screening criteria, we then conduct a conjoint experiment to quantitatively assess which of these criteria are most
important for impact investors. Our conjoint experiment covers 4296 investment decisions made by 179 impact investors who invest
directly in SEs.

We find that the authenticity of the founding team, the importance of the societal problem, and financial sustainability are the most
important screening criteria for investors. The least important criterion is the professional background of the founding team. Hence,
our results show that impact investors generally consider a mixed set of attributes when screening investment targets and making
investment decisions. Focusing on differences between different types of impact investors, we show that purely philanthropic impact
investors who provide SEs with donations differ in their selection processes compared to equity and debt investors. For example,
donors attach a higher weight to the importance of the addressed societal problem and less importance to financial sustainability.
Comparing the screening criteria of equity and debt impact investors, we find that equity investors place more importance on SEs’
scalability.

We contribute to different strands of the entrepreneurial finance literature. First, we contribute to the small but growing literature
on impact investing (e.g., Barber et al., 2020; Chowdhry et al., 2019; Geczy et al., 2019). Prior research is silent regarding the in-
vestment process of impact investors. Based on an experiment with a tightly controlled information environment, we identify how
much importance impact investors assign to each investment criterion. Our study provides an important first step towards a better
understanding of the screening and investment criteria of impact investors when selecting SEs for their portfolio. In this way, our study
is not only of theoretical importance but also has practical implications for both impact investing organizations and SEs that are
looking for funding. In addition to comparing the importance of particular screening criteria, our study also sheds light on the het-
erogeneity that exists within the group of impact investors. Equity providers, debt providers, and donors differ as investors and attach
different weights to specific screening criteria reflecting differences in their investment goals. By focusing on this within-group het-
erogeneity, our study connects to prior research on the screening process of debt and equity investors (e.g., Berger and Udell, 1998;
Mason and Stark, 2004; Ueda, 2004).

Second, our study contributes to the entrepreneurial finance literature that assesses the importance of both the funding team and its
characteristics for attracting funding from entrepreneurial finance investors (e.g., Bernstein et al., 2017; Block et al., 2019; Gompers
et al., 2020; Kaplan and Strömberg, 2004). Our results provide a mixed picture. While the authenticity of the founding team is critical
for the impact investors in our sample, the professional background of the founding team is of low importance. This finding seems to be
unique to the context of impact investing and is especially intriguing since prior research has found the professional background of the
funding team to be an important criterion of VC investors (e.g., Franke et al., 2008). In this way, our study connects to the ongoing
discussion of whether it is the ‘horse’ or the ‘jockey’ that matters when applying for funding with risk capital investors (e.g., Block
et al., 2019; Kaplan et al., 2009). Specifically, we extend this debate to the context of impact investing and show that a cut-and-dry
answer about the importance of the funding team in contrast to business and social impact characteristics is difficult to make.

Finally, our study has practical implications for the group of impact investors and for SEs that seek funding. An understanding of
impact investors’ screening criteria enables impact investors to benchmark themselves against both the industry as a whole and
important subgroups. Also, our results support SEs in their search for funding from impact investors by identifying the key attributes of
their projects that should be highlighted in an application process, particularly in the early stages of the fundraising process.
Furthermore, our results provide tips on how SEs can adjust and customize their applications for different types of impact investors. For
example, SEs seeking funding from an equity impact investor should emphasize their financial sustainability, whereas SEs seeking
funding from donors should emphasize the importance of the social problem.

2. Conceptual background

2.1. Venture philanthropy and impact investing

The concept of venture philanthropy encompasses investments that seek to achieve social goals by fostering socially-oriented
organizations (e.g., Bugg-Levine and Emerson, 2011). It distinguishes itself from conventional philanthropy by going beyond the
mere allocation of donations. Venture philanthropists are active investors who provide grants as well as high-engagement, long-term,
nonfinancial support to their portfolio companies (e.g., Grossman et al., 2013; Letts et al., 1997). This nonfinancial support leads to a
more intense relationship with funded organizations compared to that of traditional philanthropy (Van Slyke and Newman, 2006).

1 The screening criteria used in this study are described in detail in Table 5.

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Examples of major venture philanthropy organizations are the Bill and Melinda Gates Foundation and the Keywell Foundation.
Impact investing is a domain of venture philanthropy that is closely connected to traditional venture finance (e.g., Geczy et al.,

2019; Grossman et al., 2013). Similar to traditional investors, impact investors provide various types of capital and funding (e.g.,
Barber et al., 2020; Gray et al., 2015). Channels of impact capital include, for example, impact investment funds, social banks, or
crowdfunding platforms (Social Impact Investment Taskforce, 2014). Furthermore, impact investors resemble traditional investors
with regard to their financial return expectations and their investment selection process and by providing value-added services and
access to networks to their portfolio companies (e.g., Brest and Born, 2013; Gordon, 2014).

2.2. Goals and types of impact investors

Despite these similarities, the goals of impact investors and traditional investors differ. In particular, impact investments strive to
create a social or environmental impact in addition to seeking financial returns (e.g., Chowdhry et al., 2019; Lee et al., 2020).2 For
example, impact investors often invest in sectors that address global challenges, such as those that aim to reduce poverty or mitigate
climate change (e.g., Gray et al., 2015; Geczy et al., 2019). Therefore, impact investors do not solely assess the potential financial
return of portfolio ventures but also consider the social impact resulting from their investments. Prior research has also shown that
impact investors are willing to sacrifice financial returns to achieve social objectives (e.g., Chowdhry et al., 2019). This further dis-
tinguishes them from traditional investors, who are predominantly interested in financial returns.

Like traditional investors, impact investors are a heterogeneous group of investors who provide a wide range of investment types
(Bugg-Levine and Emerson, 2011). The GIIN (2019b) classifies different types of impact investments in a return-rate spectrum that
ranges from “below market” to “market rate”. Regarding the different forms of capital invested, impact investors can be subclassified as
investors who provide equity, debt, and donations. This heterogeneity likely influences the screening process of these investors.
Indeed, prior research on traditional investors has shown that debt investors, equity investors, and other types of investors differ
significantly in their selection processes (e.g., Block et al., 2019; Lerner et al., 2007). We extend these findings to the heterogeneous
field of impact investors since each impact investor type has distinctive return expectations and obligations for portfolio companies,
which are likely reflected in their screening criteria.

Equity investors are the most popular impact investor type and primarily invest through impact investment funds that seek
market-rate returns. These funds typically provide portfolio companies with equity and comprise entities such as venture capital or
growth equity funds (e.g., Barber et al., 2020; Bugg-Levine and Emerson, 2011). Equity investors have a clear financial interest since
their objective is to achieve market-rate financial returns through exit proceeds similar to traditional venture capital funding (e.g.,
Barber et al., 2020; Brest and Born, 2013; Gray et al., 2015).

Debt investors provide debt to portfolio companies. Typically, social banks grant this type of impact investment to SEs. Since the
financial crisis, these financial institutions have grown strongly worldwide. Other impact investors of this type are foundations or
public institutions. For example, the Calvert Foundation offers debt financing to nonprofits or small businesses in underserved
communities (Brest and Born, 2013). Although debt investors seek financial returns, their investments are often characterized by
below-market return expectations (Brest and Born, 2013).

Donors provide SEs with philanthropic donations or grants. Many SEs need this funding type to survive (Bugg-Levine et al., 2012).
Philanthropic donations are provided mainly by governments, foundations, or philanthropists. Impact investors of this type are not
concerned with market returns but rather concentrate on social goals. Thus, they are particularly attractive to SEs that are fully
committed to the social goals of their hybrid organization (Chowdhry et al., 2019).

2.3. Selection process and screening criteria in impact investing

The selection process is of major importance for the long-term success of venture finance investors (Gompers et al., 2020). In the
initial screening stage of the selection process, investment opportunities are evaluated based on a diverse set of criteria (e.g., Hall and
Hofer, 1993; Warnick et al., 2018). The initial screening decision is typically very fast (e.g., Cumming et al., 2010; Fried and Hisrich,
1994; Zacharakis and Meyer, 2000), while the subsequent due diligence phase takes months (e.g., Cumming and Zambelli, 2017;
Gompers et al., 2020). Gompers et al. (2016) argue that for every hundred opportunities, only 15 pass the initial screening stage and
are thus evaluated more deeply. Therefore, the main task in the initial screening phase is to identify “investment-ready” ventures based
on several screening criteria (e.g., Hall and Hofer, 1993; Mason and Harrison, 2004). Often, business plans are used to screen in-
vestment opportunities in the first step (Fried and Hisrich, 1994). Therefore, prior research has investigated which investment criteria
are most relevant to venture finance investors when screening a business plan (e.g., Gompers et al., 2020; Kaplan and Strömberg, 2004;
Zacharakis and Meyer, 2000). These criteria vary across investor types (e.g., Block et al., 2019; Lerner et al., 2007; Ueda, 2004). For
example, Gompers et al. (2020) suggest that the management team is of major relevance in a selection process of venture capitalists,
whereas Block et al. (2019) indicate that it is less important to leveraged buyout funds.

The structure of the investment process of impact investors is similar to that of traditional venture finance investors (e.g., venture
capitalists) (Miller and Wesley, 2010). Impact investors usually screen a large number of investment opportunities to identify a small

2 Due to the active searching by impact investors for positive changes, they also differ from socially responsible investors. Socially responsible
investors initially try to do no harm with their investments and therefore exclude negatively connotated sectors (e.g., Galema et al., 2008; Hong and
Kostovetsky, 2012; Renneboog et al., 2008; Riedl and Smeets, 2017).

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number of ventures for further consideration. However, the investment criteria of impact investors partly differ from those of tradi-
tional investors since they follow not only financial but also social objectives (Chowdhry et al., 2019; Hartzmark and Sussman, 2018).
Impact investors’ goal of having a social impact through their investments is reflected in their selection process. Thus, while the team-
related criteria and business-related criteria between venture finance investors and impact investors might overlap (e.g., the profes-
sional background of a team or profitability) (e.g., Gompers et al., 2020; Kaplan and Strömberg, 2001), the social impact-related
criteria represent a particularity of impact investors (Miller and Wesley, 2010). Due to the even more diverse set of relevant selec-
tion criteria, a recent study by Lee et al. (2020) shows that impact investors face particular challenges in their decision-making, and the
authors identify the need for further empirical research to better understand the selection processes of impact investors. For example,
there is a gap of knowledge regarding how specific investment criteria might be more or less important to impact investors compared to
traditional venture finance investors. Since Barber et al. (2020) show that impact investors are accepting lower IRRs, business-related
criteria might therefore also be of less importance for them. Furthermore, GIIN’s investor survey (2018) indicates that the amount of
high-quality investment opportunities is limited, which emphasizes the need to identify promising portfolio companies in the initial
screening decision.

3. Hypotheses

3.1. The importance of specific investment criteria

Against this background, we investigate the investment criteria of impact investors in the initial screening phase. Since impact
investors (and their portfolio ventures) pursue a hybrid goal set, we argue that these hybrid goals are reflected in their investment
criteria, and we distinguish between social impact, founding team, and business criteria.

3.1.1. Social impact criteria
Impact investors aim to address societal issues and strive for societal impact with their investments. Thus, the societal impact of

their portfolio ventures is an important precondition for achieving their own impact. However, the societal impact of investment
opportunities differs because not all SEs that are considered potential investments have the same potential to create societal impact (e.
g., Zahra et al., 2009). For example, an SE promoting musical education in a specific region arguably has a lower societal impact than
an SE that addresses climate change or global poverty. Accordingly, prior research has shown that the importance of the societal
problem addressed by the SE leads to a higher level of attention from stakeholders (Zahra et al., 2008). Thus, we postulate the
following hypothesis:

H1a. : Impact investors are more likely to select an SE that addresses a highly important societal problem than an SE that addresses a societal
problem of medium or low importance.

The scalability of an SE determines the societal impact that can be achieved. The different forms of social scalability have received
ample attention in prior research (e.g., Bloom and Chatterji, 2009; Dees et al., 2004; Shepherd and Patzelt, 2020; Tracey and Jarvis,
2007; Zahra et al., 2009). This research shows that societal needs can be regional, national, or even global. Hence, the potential to scale
the societal impact of an SE from a regional level to a global level may be an important criterion for impact investors (e.g., Grossman
et al., 2013). Thus, the following hypothesis should apply:

H1b. : Impact investors are more likely to select an SE with a high degree of scalability than an SE with a medium or low degree of scalability.

3.1.2. Founding team criteria
Prior research on entrepreneurial finance documents that investors consider management team characteristics as important in-

vestment criteria (e.g., Gompers et al., 2020; Kaplan and Strömberg, 2004). Typically, the characteristics considered refer to the
management team’s experience or educational background. For SEs, an important founding team characteristic is the authenticity with
which a founding team pursues its idea. In our case, authenticity refers to how credible a founding team is in solving a certain societal
problem. An explanation for the importance of authenticity is that authenticity often correlates with passion in the context of SEs (e.g.,
Radoynovska and King, 2019), which is an important motivational driver of venture success that investors typically seek in founding
teams (e.g., Chen et al., 2009). Additionally, authenticity is an important prerequisite that helps ventures obtain commitment from
other stakeholders, such as employees or customers (e.g., Radoynovska and King, 2019), thereby leading to growth. Indeed, prior
research has shown that a lack of authenticity can impede SE growth (e.g., Davies et al., 2019). Being an authentic founder sends a
strong and difficult to imitate signal to impact investors. Based on these arguments, we suggest the following hypothesis:

H2a. : Impact investors are more likely to select an SE which has a highly authentic founding team than an SE with a medium or low authentic
founding team.

The educational background of SE founders varies greatly. In addition to educational backgrounds in a technical field or business,
many social entrepreneurs have an educational background that is based in a social sector. This is the case because many social en-
trepreneurs identify their business opportunities through their own personal experiences (e.g., Renko, 2013; Yitshaki and Kropp,
2016). We shall argue that impact investors attribute more industry or field experience to social entrepreneurs with an education based
in a social sector and trust them to be better able to identify important societal problems and build an impactful social venture. We
propose the following hypothesis:

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H2b. : Impact investors are more likely to select an SE that has a founding team with an educational background in a social sector compared to
an SE that has a founding team with a business or technical educational background.

3.1.3. Business criteria
In addition to social impact goals, impact investors also pursue financial goals. Hence, SEs need to build a financially sustainable

business model. This is especially important due to the threat of grant and donation dependency, which SEs need to avoid (e.g., Chell,
2007). In line with this argument, earlier research in entrepreneurial finance has shown that economic or business criteria generally
constitute important investment criteria (e.g., Block et al., 2019; Gompers et al., 2020). Therefore, we argue that impact investors also
look for investments that can demonstrate financial sustainability, and we suggest the following hypothesis:

H3a. : Impact investors are more likely to select an SE that has a high degree of financial sustainability than an SE with a medium or low degree
of financial sustainability.

Innovation is an important characteristic of SEs. Almost by definition, SEs strive to solve societal problems in a new way. Prior
research by Grossman et al. (2013) notes that venture philanthropists support SEs that use innovations to break outdated patterns and
achieve social change. We argue that this preference for innovative solutions also applies to impact investors and expect that a higher
degree of innovativeness increases the likelihood of an investment by an impact investor. This leads us to our next hypothesis:

H3b. : Impact investors are more likely to select an SE that has a high degree of innovation than an SE with a medium or low degree of
innovation.

Due to their hybrid goals, SEs often have complex business models, which creates uncertainty. Hybrid business models can lead to
contradictions and create tensions within the organization (Smith et al., 2013). Like all investors, impact investors aim to reduce their
investment risk and, ceteris paribus, would like to invest in SEs with low levels of uncertainty. Achieving a proof of concept reduces this
uncertainty and marks an important milestone for an SE, as it indicates that both financial and social objectives can be aligned and
long-term impact can be achieved. Thus, we hypothesize the following:

H3c. : Impact investors are more likely to select an SE that can provide a proof of concept than an SE that cannot provide a proof of concept.

3.2. Differences across different types of impact investors

As mentioned above, the group of impact investors is very heterogeneous and consists of many different types. We distinguish
between equity investors, debt investors, and donors. These investor types differ in their financial return expectations and the
importance attached to social impact. We, therefore, assume that these differences are already reflected in the screening criteria of
impact investors. This is in line with previous research, which shows that the diverse goals of investors are reflected in their decision-
making and in the criteria used (Block et al., 2019). Equity and debt impact investors will emphasize business criteria as they also
expect a financial return (rather than only a social impact) for their investment. Donors, in turn, do not expect a financial return for
their investment but pursue primarily social goals. Accordingly, we expect donors to put comparatively more weight on social impact
criteria and less weight on business criteria compared to equity and debt impact investors. Hence, the following two hypotheses should
apply:

H4a. : In contrast to donors, equity and debt impact investors put more weight on business criteria.

H4b. : In contrast to donors, equity and debt investors put less weight on social impact criteria.

4. Research design

4.1. Data and sample

To assess impact investors’ screening criteria, we conducted a survey-based conjoint analysis. To construct our sample, we iden-
tified impact investors in the central European D/A/CH region (Germany, Austria, and Switzerland) in two steps. First, we conducted a
computerized search strategy since an established database of impact investors does not exist. Using the keywords “impact investing”,
“social investing”, “philanthropic investing” and “social entrepreneurship”, we identified impact investors from the social network
platforms LinkedIn and XING (which is a German professional social networking site). We provided the impact investors with indi-
vidual links to our experiment and survey.3 In this step, we identified 763 individuals (67.6%) for our sample population. In the second
step, we identified an additional set of 366 (32.4%) investors through a manual search of impact investors’ and SEs’ websites. As an
incentive for participation, we donated 10 EUR from each participant to an SE (e.g., Africa GreenTec).4 In total, we were able to

3 The translated survey is included in the Online Appendix (OA.III).
4 Information about the donations was provided on the introductory page of the experiment.

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identify a population of 1129 impact investors, out of which 1795 (response rate = 11.4%) participated in our experiment.
We conducted several tests to assess the representativeness of our sample. First, we compared the gender, age, and educational level

of our respondents with those of the nonrespondents. For the nonrespondents, we collected information for all variables manually. The
results of the nonrespondents’ test are displayed in Table 1, which reports the mean values of both populations and a z-test for equality

Table 1
Assessment of a potential nonresponse bias and equality of distribution.

Variable (1) Non-respondents (N = 478) (2) Final sample (N = 179) (1) vs. (2) Kolmogorov-Smirnov test

Gender
Male 0.508 0.581 0.072 –

Age
<30 0.165 0.263 0.098 – 30–40 0.453 0.397 − 0.056 – 40–50 0.243 0.223 − 0.019 – >50 0.140 0.117 − 0.023 –

Level of education
High school graduation 0.023 0.045 0.022 –
Bachelor degree 0.138 0.117 − 0.021 –
Master degree 0.677 0.670 − 0.007 –
PhD 0.160 0.162 0.002 –

Age (categorical) 2.351 2.207 – − 0.102*
Level of education (categorical) 4.987 4.939 – − 0.031

To assess whether a nonresponse bias potentially influences our results, we compare non-respondents of our sample (N = 950) to our final sample (N
= 179) along with several characteristics. Because of missing values for the variables age and level of education, the sample of non-respondents is
reduced to N = 486. The first column reports the mean values in the initial population. The second column reports the mean values of our final sample.
The third column reports the difference between the mean values along with the significance of z-tests for proportions. The final column reports the
difference between the mean values along with the significance of a two-sided Kolmogorov-Smirnov test for equality of distribution. Significant values
indicate statistically significant differences. * < 0.10, ** p < 0.05, *** p < 0.01.

Table 2
Assessment of a potential late-response bias.

Variable (1) First half (N = 90) (2) Second half (N = 89) (1) vs. (2)

Gender
Male 0.656 0.517 0.139 (0.353)

Age
<30 0.267 0.270 − 0.003 (0.984) 30–40 0.411 0.371 0.040 (0.787) 40–50 0.244 0.202 0.042 (0.778) >50 0.078 0.157 − 0.080 (0.595)

Level of education
High school graduation 0.044 0.056 − 0.012 (0.937)
Bachelor degree 0.100 0.157 − 0.057 (0.702)
Master degree 0.644 0.674 − 0.030 (0.842)
PhD 0.200 0.112 0.088 (0.558)

Educational background
Business/economics 0.556 0.629 − 0.074 (0.622)
Natural sciences 0.067 0.101 − 0.034 (0.818)
Social sciences 0.300 0.202 0.098 (0.513)

Entrepreneurial experience 0.344 0.382 − 0.038 (0.802)

Type of investment
Donations 0.689 0.607 0.082 (0.491)
Equity 0.344 0.404 − 0.060 (0.688)
Debt 0.267 0.348 − 0.082 (0.585)

To assess whether our results are affected by a late-response bias, we compare the first half of our respondents (N = 90) to the second half of our
respondents (N = 89) along with their individual characteristics. The first column reports the mean values in the first half of the participants. The
second column reports the mean values of the second half of the participants. The last column reports the difference between the mean values along
with the significance of z-tests for proportions. All variables are defined in Table 3. P-values are reported in brackets in the last column.

5 Compared with previous conjoint studies, the sample size of our experiment is appropriate (e.g., Franke et al., 2006, 2008; Shepherd and
Zacharakis, 2002). Particularly, the high amount of observations (4296), due to the amount of decisions that had to be taken by each participants,
further strengthens the reliability our results.

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of proportions. No statistically significant differences emerge across our variables, which suggests that no major differences exist
between the respondents and the nonrespondents. Furthermore, we considered listed members of the European Venture Philanthropy
Association (EVPA) in our experiment. In total, 45 respondents in our final sample work for EVPA organizations. These employees
represent 17 of the 31 EVPA member organizations located in the DACH region (54.8%). The remaining participants originate from
other organizations that invest in SEs, such as the Purpose Foundation, GLS Bank, or Invest in Visions.

Furthermore, we conducted a late-response bias test to determine whether the early respondents differed from the late respondents
(Graham and Harvey, 2001). We assessed this bias by splitting our sample into two samples—the first half of the respondents (N = 90)
and the second half (N = 89)—and we compared the mean values of their individual characteristics using a z-test. Table 2 shows the
results. In summary, we find no statistically significant differences between the characteristics of early and late respondents; thus, a
late-response bias is unlikely.

Table 3
Descriptive statistics and definitions of the variables.

Variable Mean S.D. Min. Max. Description

Panel A: Characteristics of the individual impact investor
Male 0.59 – 0 1 Participant’s gender (dummy; 1 = male, 0 = female)
Age 3.19 0.96 1 5 Participant’s age (categorical; 1 < 20, 2 = 20–29, 3 = 30–39, 4 = 40–49, 5 > 49)
Level of education 3.91 0.73 1 5 Participant’s level of education (categorical; 1 = less than high school graduation, 2 = high

school graduation, 3 = bachelor degree, 4 = master degree, 5 = PhD)
Education: business/

economics
0.59 – 0 1 Participant has an educational background in business or economics (dummy; 1 = yes, 0 =

no)
Education: humanities 0.22 – 0 1 Participant has an educational background in humanities (dummy; 1 = yes, 0 = no)
Education: social sciences 0.25 – 0 1 Participant has an educational background in social science (dummy; 1 = yes, 0 = no)
Entrepreneurial experience 0.57 – 0 1 Participant has experience as an entrepreneur (dummy; 1 = yes, 0 = no)
Experience as investor 3.75 1.75 1 5 Participant’s experience as an investor (categorical; 0 = No decision made, 1 = 1 decision

made, 2 = 2–4 decision made, 3 = 5–10 decision made, 4 > 10 decision made)

Panel B: Characteristics of the impact investment organization
Number of employees 2.43 2.43 1 5 Impact investor company’s number of employees (categorical; 1 < 10; 2 = 10–49, 3 = 50–99,

4 = 100–249, 5 > 249)
Impact investing as core

activity
0.56 – 0 1 Impact investing is the main activity of the impact investor company (dummy; 1 = yes, 0 =

no)
Investment type: Equity 0.37 – 0 1 Impact investor company invests equity in portfolio companies (dummy; 1 = yes, 0 = no)
Investment type: Debt 0.31 – 0 1 Impact investor company invests debt in portfolio companies (dummy; 1 = yes, 0 = no)
Investment type: Donations 0.65 – 0 1 Impact investor company provides donations to portfolio companies (dummy; 1 = yes, 0 =

no)
Motive: Stakeholder

expectations
3.34 1.05 1 5 Impact investor company opinion on stakeholder expectations (ordinal; 1 = unimportant, 5

= very important)
Motive: Financial interests 2.68 1.17 1 5 Impact investor company opinion on financial interests (ordinal; 1 = unimportant, 5 = very

important)
Motive: Reputation 3.25 0.99 1 5 Impact investor company opinion on reputation (ordinal; 1 = unimportant, 5 = very

important)
Motive: Employer Branding 2.82 1.03 1 5 Impact investor company opinion on employer branding (ordinal; 1 = unimportant, 5 = very

important)
Stage of development: Idea

development
0.48 – 0 1 Impact investor company invests in the idea development stage (dummy; 1 = yes, 0 = no)

Stage of development: Seed
stage

0.53 – 0 1 Impact investor company invests in the seed stage (dummy; 1 = yes, 0 = no)

Stage of development: Startup
stage

0.68 – 0 1 Impact investor company invests in the startup stage (dummy; 1 = yes, 0 = no)

Stage of development:
Expansion stage

0.47 – 0 1 Impact investor company invests in the expansion stage (dummy; 1 = yes, 0 = no)

Stage of development:
Establishment stage

0.27 – 0 1 Impact investor company invests in the establishment stage (dummy; 1 = yes, 0 = no)

Stage of development: Exit
stage

0.03 – 0 1 Impact investor company invests in the exit stage (dummy; 1 = yes, 0 = no)

Social area: Environment 0.67 – 0 1 Impact investor company focuses on environmental-oriented companies (dummy; 1 = yes, 0
= no)

Social area: Health 0.36 – 0 1 Impact investor company focuses on health-oriented companies (dummy; 1 = yes, 0 = no)
Social area: Poverty reduction 0.41 – 0 1 Impact investor company focuses on poverty reduction-oriented companies (dummy; 1 = yes,

0 = no)
Social area: Education 0.57 – 1 1 Impact investor company focuses on education-oriented companies (dummy; 1 = yes, 0 = no)
Social area: Social inclusion 0.44 – 0 1 Impact investor company focuses on social inclusion-oriented companies (dummy; 1 = yes, 0

= no)
Social area: Others 0.08 – 0 1 Impact investor company focuses on other-oriented companies (dummy; 1 = yes, 0 = no)

This table provides an overview of the full sample used in our analysis and displays descriptive statistics along with variable definitions. Panel A
describes variables related to characteristics of the individual impact investor. Panel B describes variables related to characteristics of the impact
investment organization. Panel C describes variables related to characteristics of the social ventures. The sample comprises of 179 participants.

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4.2. Descriptive statistics

Each participant filled out a questionnaire containing individual-level and organizational-level questions. The following sub-
sections report the descriptive statistics for our sample and explore the particularities of the different impact investor types (i.e.,
donors, equity investors, and debt investors). Table 3 shows the descriptive statistics and describes each variable.

Regarding individual-level characteristics, the impact investors in our sample are mostly male (59%), between 30 and 40 years old
and have a master’s degree. This is in line with the results of Lee et al. (2020), who report that their impact investors are mostly male
(60–70%), have the same age range and have a high level of education. Furthermore, the average impact investor in our sample made
between 5 and 10 investment decisions, and more than half of the respondents (57%) had entrepreneurship experience.

Regarding organizational-level characteristics, Table 3 shows that the majority of impact investor organizations (56%) see impact
investments as their core business and are mostly motivated by stakeholders’ expectations. Additionally, the startup stage is the most
common investment phase (68%), and most impact investor companies focus on environmental-oriented or education-oriented SEs.

Table 4 shows an initial comparison of the impact investor types based on the variables described in Table 3. Table 4 reports the
mean values of our full sample (N = 179) in comparison to the mean values of each investor type. The brackets behind the mean values
(+/− ) indicate the results of a t-test that shows whether the respective mean values of a certain impact investor type are significantly
larger (+) or smaller (− ) than the respective mean values of the full sample. The final column demonstrates the results of an analysis of
variance (ANOVA), which indicates statistically significant differences across the three groups of impact investors.

4.2.1. Equity investors
Our sample contains 67 (37%) impact investors who provide SEs with equity. This investor group differs significantly from the

other investor types in many individual and organizational variables. For example, Table 4 reports that more equity investors are male,
and fewer of them have an educational background in humanities compared to debt investors and donors. Furthermore, we find that
69% of the equity investors have an entrepreneurship background, which is significantly higher compared to the other groups.
Furthermore, equity impact investors provide their investees with “smart money”. That is, in addition to capital, they typically provide
a range of value-adding activities to their portfolio companies. These activities are often based on past entrepreneurial experience (e.g.,

Table 4
Descriptive statistics across different types of investors.

Variable Full sample (N = 179) Equity (N = 67) Debt (N = 55) Donations (N = 116) ANOVA

Panel A: Characteristics of the individual impact investor
Male 0.59 0.68 (+) 0.60 0.57
Age 3.19 3.33 3.21 3.20
Level of education 4.91 4.93 4.93 4.94
Education: business/economics 0.59 0.63 0.72 (+) 0.56 *
Education: humanities 0.22 0.15 (− ) 0.07 (− ) 0.29 (+) ***
Education: social sciences 0.25 0.21 0.18 0.28
Entrepreneurial experience 0.57 0.69 (+) 0.62 0.54 *
Experience as investor 3.75 3.97 (+) 3.67 3.74

Panel B: Characteristics of the impact investment organization
Number of employees 2.43 1.95 (− ) 2.44 2.69 (+) ***
Impact investing as core activity 0.44 0.36 (− ) 0.27 (− ) 0.49 (+) **
Motive: Stakeholder expectations 3.34 3.36 3.36 3.28
Motive: Financial interests 2.68 3.14 (+) 3.18 (+) 2.41 (− ) ***
Motive: Reputation 3.25 3.28 3.51 (+) 3.21
Motive: Employer Branding 2.82 2.87 2.91 2.83
Stage of development: Idea development 0.48 0.34 (− ) 0.42 0.52 **
Stage of development: Seed stage 0.53 0.51 0.53 0.55
Stage of development: Startup stage 0.68 0.67 0.65 0.72 (+)
Stage of development: Expansion stage 0.47 0.54 0.64 (+) 0.45 **
Stage of development: Establishment stage 0.27 0.25 0.18 (− ) 0.32 (+)
Stage of development: Exit stage 0.03 0.04 0.02 0.04
Social area: Environment 0.67 0.76 (+) 0.70 0.67
Social area: Health 0.36 0.42 0.43 0.34
Social area: Poverty reduction 0.41 0.42 0.53 (+) 0.40
Social area: Education 0.57 0.47 (− ) 0.55 0.63
Social area: Social inclusion 0.44 0.36 0.40 0.50 (+)
Social area: Others 0.08 0.03 (− ) 0.11 0.08 (+) *

This table reports differences in the mean values across the different impact investor types included in our sample. While the first column demon-
strates the mean values of the full sample (N = 179 individuals), the following columns report descriptive statistics for impact investors providing
donations, equity, and debt. Panel A outlines differences across variables related to characteristics of the individual impact investor. Panel B outlines
differences across variables related to characteristics of the impact investment organization. The signs in brackets (+/− ) demonstrate whether the
respective mean value is significantly larger (+) or smaller (− ) than the mean value of the remaining sample. We conducted a t-test to calculate the
significance for each individual mean value. The final column outlines the significance level obtained from an analysis of variance (ANOVA),
indicating statistically significant differences across groups. All variables are defined in Table 3. * < 0.10, ** p < 0.05, *** p < 0.01.

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Sapp and Tiwari, 2004; Sørensen, 2007). Finally, equity investors in our sample have more investment experience as impact investors
than do other types of investors.

Regarding organizational characteristics, equity investor organizations are significantly smaller than other investor organizations
(i.e., lower number of employees). Additionally, only 36% of the equity investor organizations pursue impact investing as their core
activity, which suggests that to most equity investors, impact investing might only be a recently established segment (e.g., Höchstädter
and Scheck, 2015) that is treated as a side business. Because equity investors strive for financial returns through exit proceeds (e.g.,
Brest and Born, 2013; Louche et al., 2012), they have higher financial interests than those of investors without financial objectives.
Regarding the stage of development of funded SEs, equity investors differ only in terms of the first stage, “idea development,” in the
sense that this investor group invests much less in projects that are still outlining their idea. Finally, with an average of 76%, equity
investors focus on SEs that tackle environmental issues more often than debt investors and donors. In contrast, they invest less in
ventures active in the field of education.

4.2.2. Debt investors
Impact investors who provide SEs with debt represent the smallest group of investors in our sample (N = 55, 31%). Table 4

demonstrates that they differ substantially from equity investors and donors. First, individuals in this group more often have an
educational background in economics (72%). This result is in line with prior research that suggests that debt investors are more
interested than other investors in the financial aspects of a funded venture (e.g., Mason and Stark, 2004) and therefore need to have a
more sophisticated understanding of economics.

Debt investors try to achieve financial returns, similar to equity investors. In addition, debt investors rate reputational motives as
more important than the other investor groups. Combined with the result that only 27% of the debt investors regard impact investing
as their core activity, this suggests that several debt investors began to pursue impact investing as a side business due to reputational
reasons. Approximately 53% of the debt investors invest in SEs that try to solve poverty issues, which is significantly higher than the
percentage of equity investors and donors. Additionally, 64% of the debt investors in SEs are expanding their businesses. This finding is
in line with the findings of previous research, which shows that debt is provided at later stages than equity investments (e.g., Berger
and Udell, 1998).

4.2.3. Donors
Our subsample of impact investors who provide investments in the form of donations encompasses 116 individuals (65%). Table 4

documents that donors differ substantially from equity and debt investors. Most of these differences occur at the organizational level.
Specifically, donors differ with regard to company size, alignment, motives, investment time, and the social area on which they focus.
In contrast to other types of impact investors, donors’ organizations have more employees on average. Furthermore, 49% of these
impact investors state that impact investing is their core business, which is significantly higher than for the other types. Since donors
typically do not expect financial returns; their financial motives are weaker than those of equity and debt impact investors who are
seeking financial compensation. The donors in our sample tend to invest later than the other groups of investors. Thus, they invest in
more ventures that are active in the startup and establishment phase. Finally, donors invest more actively in SEs that operate in the
field of social inclusion. On average, half of the donors sampled invest in this field.

4.3. Design of the choice-based conjoint experiment

We conducted a survey-based conjoint experiment6 to quantitatively assess the decision behavior of impact investors. Conjoint
analysis has been introduced in the marketing field to assess the relative importance of product attributes (e.g., Green and Srinivasan,
1990). Shepherd and Zacharakis (1999) transferred the experimental design to the assessment of venture capitalists’ decision making.
Conjoint experiments complement post hoc approaches (e.g., questionnaires or interviews), which have several limitations when
analyzing decision behavior (Shepherd and Zacharakis, 2018). For example, post hoc methodologies use past information that can
suffer from recall or rationalization biases (Zacharakis and Meyer, 2000). Thus, more valid results can be achieved through conjoint
analysis (Franke et al., 2006, 2008). Additionally, conjoint experiments are real-time experiments since information is collected while
decisions are being made, whereas other approaches collect data only after this process is complete. Therefore, conjoint studies are
more similar to the real decision-making behaviors of investors. Because investment decisions are made holistically by investors (e.g.,
Dane and Pratt, 2007), conjoint analyses are a useful tool for evaluating these decisions since the investment criteria can be measured
conjointly. This situation leads to an accurate representation of investors’ decision-making behavior (Block et al., 2019). Hence, every
decision for or against an investment involves making trade-offs between different criteria, which can be represented within a conjoint
experiment.

In light of these advantages, several studies in entrepreneurial finance have analyzed decision behaviors via conjoint experiments
(e.g., Block et al., 2019; Franke et al., 2006, 2008). Moreover, the studies of Bernstein et al. (2017) and Block et al. (2019) show that
experiments are gaining increasing popularity within the finance audience over post hoc approaches such as surveys (e.g., Bonini et al.,
2018; Gompers et al., 2016, 2020).

We used a discrete choice-based conjoint (CBC) experiment. Specifically, we asked impact investors to decide between two

6 The experiment was designed with “Sawtooth”. Sawtooth is a widely used tool to conduct and host conjoint analyses (e.g., Lohrke et al., 2010).
See https://www.sawtoothsoftware.com/.

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hypothetical investment opportunities that differ in specific attribute levels (e.g., the authenticity of the founding team, financial
sustainability, and the degree of innovation). The idea behind this form of conjoint experiment is that participants (here defined as
impact investors) always have to make several choices between two different hypothetical investments. Because our experiment
presents hypothetical ventures to respondents, external validity may suffer. We address this issue in detail in the final section.

The experiment was explained to the participants in an introductory slide to ensure that they were evaluating the same SEs during
their decision (see the Online Appendix, OA.I). For example, prior research has suggested that investors look for a strategic fit between
the portfolio company and their investment strategy in early screenings (e.g., Fried and Hisrich, 1994; Gompers et al., 2020; Zacharakis
and Meyer, 2000). Therefore, the introductory slide clarified that the geographical and strategic orientation of each hypothetical
venture matches the interests of the investor (Franke et al., 2008). Furthermore, we explained to participants that the experiment
addresses the initial preselection of investment opportunities. This clarified that the experiment focuses on the screening stage of the
selection process of impact investors. In this initial screening phase, investors generally assess proposals in a very short time (Hall and
Hofer, 1993). Supporting this idea, previous literature has shown that applicants for venture finance have to pass an initial screening
followed by months of due diligence, with a low approval rate of approximately 20% of all requests (e.g., Cumming et al., 2010; Fried
and Hisrich, 1994; Zacharakis and Meyer, 2000).

The participants in our experiment were forced to make a discrete choice for each investment opportunity (investment: “yes” or
“no”). The advantages of this approach are that the investment criteria can be measured conjointly and that investors can be provided
with very detailed descriptions of the investment possibilities. As in any conjoint experiment, each participant made a series of de-
cisions on hypothetical investments (14 in our case) based on fixed screening criteria. Next to a description of their task, the infor-
mation presented to respondents included a description of the seven investment criteria used: (1) the importance of the societal
problem, (2) the scalability, (3) the authenticity of the founding team, (4) the professional background of the founding team the
importance of the societal problem, (5) the financial sustainability, (6), the degree of innovation, and (7) the proof of concept. Each
decision criterion has two or three different attribute levels.

Table 5
Attributes and levels of the conjoint analysis.

Attribute Levels Label used in the experiment Definition and rationale for inclusion

Social impact criteria
Importance of the societal

problem (3 levels

– ordinal)

Low
Medium
High

Describes the relevance and urgency of
solving the societal problem

The importance of the societal problem describes the extent of an
issue which a SE aims to solve. Thus, next to the attribute scalability
this attribute covers the social impact which the SE wants to
achieve.

Scalability (3 levels – ordinal) Low
Medium
High

Describes the possibility of transfer and
large-scale implementation of the
project

The scalability covers the social impact the SE wants to achieve.
Hence, it shows the extent to which the social project of the SE can
be scaled to achieve a greater impact and reach more stakeholders
of the social part of the business.

Founding team criteria
Authenticity of the founding

team (3 levels – ordinal)
Low
Medium
High

Describes the authenticity or credibility
of the founders.

Davies et al. (2019) argue that SEs have to maintain identity
authenticity; otherwise, barriers to growth could arise.
Furthermore, several interview partners mentioned authenticity of
the founding team as an important attribute in their screening
criteria. It describes how credible the founding team is in solving a
certain societal problem. Finally, our attribute encompasses how
authentic or credible the founders present their business idea to
solve a societal problem.

Professional background of the
founding team (3 levels –
nominal)

Technical
Business
Social

Represents the educational and
professional background
of the founders

Previous literature has shown that the professional background of
the founding team influences the selection processes in venture
finance (e.g., Franke 2006, 2008; Kaplan and Strömberg, 2004).
Therefore, this attribute describes whether the founding team has a
technical, social, or business educational and professional
background.

Business criteria
Financial sustainability (3 levels

– ordinal)
Low
Medium
High

Represents the extent to which the SE
will be
able to finance itself in the foreseeable
future

Describes how likely the SE will achieve financial goals in the future
and not be dependent on external sources. Because of the hybrid
nature of SEs, many of them are dependent on external support over
the long term (e.g., Chell, 2007). Therefore, becoming financially
sustainable represents a great obstacle they need to overcome.

Degree of innovation (3 levels –
ordinal)

Low
Medium
High

Describes the novelty of the way to
solve the societal problem

The innovativeness is an important component of the definition of
an SE (e.g., Dart, 2004). It covers how the SE tackles a societal
problem in a new way. Social innovation has become a large
governmental topic to tackle global problems and achieve the
Sustainable Development Goals (SDGs) (Eichler and Schwarz,
2019).

Proof of concept (2 levels –
ordinal)

Not
provided
Provided

Proof of the feasibility of the project Describes whether a proof of concept is provided for the SEs’
business model. Thus, it proofs the feasibility of the social and
financial part of the SE.

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The seven investment criteria were identified through expert interviews with impact investors who had investment experience in
all three impact investing areas (donations, equity, and debt). In total, we conducted 12 interviews with decision makers from impact
investing organizations located within the D/A/CH region. For our explorative interviews, we selected experts from the impact
investing field to obtain initial insights into the impact of investors’ investment criteria and selection processes. All the participants
hold a high position in their organization and have a high level of knowledge of the field (see the Online Appendix, OA. IV). We chose
this approach because research on the screening criteria of impact investors is still limited. The interviews were conducted until we
reached a sufficient number of different views that showed converged agreement between the participants on investment criteria. We
used a semistructured interview guideline to give impact investors the possibility to freely answer open-ended questions. The in-
terviews were conducted between November 2017 and March 2018. The interviews were transcribed and coded by two researchers to
ensure the reliability of the criteria.

Based on the results of previous research and based on our explorative interviews, we identified a set of investment criteria that
have a high level of importance in the selection processes of impact investors. Each attribute was explicitly explained by a brief label to
the participants (see Table 5). The different attribute labels were always visible throughout the experiment by a hover effect.
Furthermore, Table 5 illustrates the different levels of each attribute as well as our rationale for inclusion. For example, the attribute
levels of “financial sustainability” are “low”, “medium”, and “high”. These levels represent the extent to which the social venture will
be able to finance itself in the foreseeable future.

To ensure that the impact investors could assess the hypothetical investments in SEs holistically, we used a full-profile CBC, which
included all attributes listed in Table 5 (Franke et al., 2008). Based on our different attributes and attribute levels, we created a set of
500 unique experimental designs, in which each design presented a unique choice task consisting of different attribute level

Table 6
Main effects of the conjoint analysis.*, **

Model (1) (2) (3) (4)

Sample Full sample Equity Debt Donations

Attributes and levels Log-odds (SE) Log-odds (SE) Log-odds (SE) Log-odds (SE)

Social impact criteria
H1a: Importance of the societal problem: high 1.684 (0.123)*** 1.383 (0.193)*** 1.259 (0.215)*** 1.858 (0.153)***

Importance of the societal problem: medium 1.044 (0.097)*** 0.864 (0.173)*** 0.808 (0.162)*** 1.144 (0.129)***
(reference group: low)

H1b: Scalability: high 0.999 (0.103)*** 1.245 (0.194*** 0.629 (0.184)*** 0.946 (0.123)***
Scalability: medium 0.525 (0.088)*** 0.518 (0.151)*** 0.210 (0.143)*** 0.645 (0.106)***
(reference group: low)

Founding team criteria
H2a: Authenticity of the founding team: high 1.804 (0.131)*** 1.968 (0.211)*** 1.914 (0.251)*** 1.789 (0.165)***

Authenticity of the founding team: medium 1.379 (0.112)*** 1.530 (0.179)*** 1.377 (0.205)*** 1.267 (0.139)***
(reference group: low)

H2b: Founding team background: social 0.096 (0.100) − 0.044 (0.162) 0.227 (0.194) 0.088 (0.125)
Founding team background: technical − 0.035 (0.090) 0.054 (0.135) 0.113 (0.175) − 0.097 (0.109)
(reference group: business)

Business criteria
H3a: Financial sustainability: high 1.185 (0.122*** 1.105 (0.149)*** 1.622 (0.204)*** 1.411 (0.232)***

Financial sustainability: medium 0.771 (0.107)*** 0.725 (0.133)*** 0.934 (0.176)*** 0.956 (0.204)***
(reference group: low)

H3b: Degree of innovation: high 0.578 (0.100)*** 0.625 (0.125)*** 0.524 (0.128)*** 0.509 (0.170)***
Degree of innovation: medium 0.380 (0.092)*** 0.404 (0.111)*** 0.244 (0.147)*** 0.426 (0.193)***
(reference group: low)

H3c: Proof of Concept: provided 0.684 (0.095)*** 0.702 (0.118)*** 0.818 (0.149)*** 0.656 (0.185)***
(reference group: not provided)
N (decisions) 4.296 1.608 1.320 2.784
N (decision makers) 179 67 55 116

Notes: Estimated with robust standard errors.
The following table demonstrates the results of a clustered multilevel logistic regression with random intercepts and random slopes. The preference of
the decision maker serves as the dependent variable the independent variables are the attribute levels described in Table 5. Log-odds and standard
errors (clustered at the decision maker level) are displayed. Model 1 explores the full sample and shows that all attribute levels except the professional
background of the founding team are significantly influencing the decision of an impact investor (p < 0.001). The log-odds of each attribute level indicate the importance impact investors attach to each criterion. For example, the attribute levels of the criterion authenticity of the founding team have particularly high effect sizes. Models 2–4 use each impact investor type separately and enable an initial comparison of the investment criteria’s importance for each investor type. We analyze impact investors providing donations, equity, and debt. For example, the Log-odd of 1.622 for equity investors with regard to SEs showing high financial sustainability is much higher than the log-odd of 1.185 for the whole sample. This highlights that being financial sustainable profitable is much more important for equator providers than for other types of impact investor. * < 0.10, ** p < 0.05, *** p < 0.01.

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1.37%

8.22%

9.73%

14.21%

16.86%

23.95%

25.66%

0% 5% 10% 15% 20% 25% 30%

Professional background of

the

founding team

Degree of innovation

Proof of Concept

Scalability

Financial sustainability

Importance of the society

issue

Authenticity of the

founding team

Relative importance (proportion of total utility explained

)

Fig. 1. Relative importance of attributes.
Notes: Calculated based on the coefficients of the main model (Table 6). Reading example: With a relative importance of 25.66%, social investors consider the authenticity of the founding team to be over
18 times as important as the attribute professional background of the founding team (relative importance: 4.2%). This value also signifies that the attribute authenticity of the founding team accounts for
25.66% of the decision maker’s total utility.

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combinations. In every design, the seven attributes (Table 5) were presented to the participants with randomly assigned attribute levels
for two different investment opportunities. The impact investors then had to decide in which of the SEs they would like to invest.
However, to ensure that the participants were not overwhelmed, we employed a reduced conjoint design (Chrzan and Orme, 2000),
which prevents participants from facing too many different task decisions. Thus, every participant had to make 14 decisions, which
included 12 randomly assigned tasks and two fixed tasks that were hold constant across all participants. “Fixed tasks” serve as a proxy
to estimate the test-retest reliability of respondents’ choices. In line with previous research, the average response time for a choice task
was 23 s, although the first task per respondent took over a minute (Johnson and Orme, 1996).

Since CBC studies are based on a specific order of investment criteria, they can suffer from diverse order effects (e.g., Block et al.,
2019; Chrzan, 1994). To account for these effects, we employed three different tests. First, to account for biases due to the order of
choice tasks, we randomly ordered the choice tasks within each of the 500 different experimental designs. Second, the two investment
opportunities within the 500 designs were also randomly ordered within each choice task to overcome negative order of options
effects. Third, to avoid an impact of the order of attributes within one respective choice task, we randomly crossed the presented order
of attributes to the participants but kept it stable for each respective participant. This approach eliminated the effect in which the
attribute presented at the top of the list achieves the highest individual importance. Furthermore, we conducted a pretest with 12
impact investors and four researchers to ensure the face validity of our experimental design (complexity, attributes, and number of
choices).

We analyzed the relative importance of impact investors’ investment criteria by applying a multilevel logistic regression. The
individual decisions (investment: “yes” or “no”) therefore served as our binary dependent variable, and the attribute levels represented
our independent variables. Because we have two levels in our data, we conducted a multilevel regression, which allowed us to nest
each participant (first level) with multiple decision observations (second level) (Aguinis et al., 2013). This step was necessary because
the two levels cannot be treated as unconnected and independent. We estimate the following regression equation:

log
(

φij
1 − φij

)

= β0j + βijxij

with βij = γi0 + uij

In this equation φij represents the probability of a positive decision that is conditional on βj, for the choice i for respondent j. xij
represents the independent variables x for the choice i for respondent j. In the base models (Table 6), the independent variables are the
attributes used in the conjoint experiment that were displayed to the participants (see Table 5). One attribute is used as a benchmark
category.

The multilevel analysis enabled us to also assess cross-level interaction effects when the observations of investment decisions were
nested. Finally, we conducted multiple subsample analyses to compare the different types of impact investors.

5. Results and discussion

5.1. Relative importance of impact investors’ investment criteria

Table 6 shows the results of our clustered multilevel regression analysis. While Model 1 shows the results of our full sample of
impact investors, Models 2–4 present the results of each respective type of impact investor. The log-odds coefficients illustrate the
importance that impact investors attach to each investment attribute or attribute level.

Model 1 assesses the relative importance of the respective investment attribute levels. To enable a more accessible comparison
between the screening criteria and their perceived importance, we estimate the relative importance of each attribute by zero-centering
the utility values to reach 100 as the sum of all importance values (e.g., Block et al., 2019; Franke et al., 2008). Fig. 1 displays the
relative importance of each investment criterion. The higher the value of an investment criterion is, the higher its impact on the
decision of an impact investor. For example, the two most important investment criteria (i.e., authenticity of the founding team and
importance of the societal problem) explain almost 50% of the impact investors’ decisions. Thus, the opportunity for an SE to be
selected by an impact investor increases if an SE demonstrates high values in these two investment criteria.

Our results show that both social impact criteria have a significant impact on the investment decision of an impact investor. These
results support Hypothesis 1a and Hypothesis 1b. Moreover, the importance of the societal problem is the second most important
attribute overall. This finding reflects the goals of impact investors because they can only achieve their own social impact if their
investees have a decisive impact (e.g., Geczy et al., 2019; Gray et al., 2015).

We also show that impact investors value social impact criteria higher than business criteria when screening SEs. Barber et al.
(2020) indicate that impact investors accept lower returns to achieve a social impact. Our findings confirm this finding and show that
impact investors value social impact criteria higher than business criteria when screening SEs. This finding is in line with research by
Barber et al. (2020) and Chowdhry et al. (2019), who indicate that impact investors have higher stakes in investments with higher
levels of social output. Additionally, this finding confirms the assumptions of Miller and Wesley (2010), who suggest that impact
investors initially evaluate social criteria and only assess other criteria when a certain threshold is met by the social criteria.

Regarding the founding team criteria, we find that impact investors attach the highest relative importance to the authenticity of the
founding team. These findings document the importance of the founding team’s authenticity in the SE context (e.g., Chen et al., 2009;
Davies et al., 2019; Radoynovska and King, 2019) and support Hypothesis 2a. Additionally, this finding is in line with prior research

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that identifies founding team characteristics as critical determinants of investment decisions (e.g., Gompers et al., 2020; Kaplan and
Strömberg, 2004). In contrast, we do not find support for Hypothesis 2b. We find that the field of background of the founding team has
no significant impact on the selection of an impact investor, indicating that impact investors do not favor a social, technical, or
economic background. Since the majority of impact investors from our experiment have an economic background (59%), our findings
contradict earlier research by Franke et al. (2006), who illustrate that venture capitalists preferably invest in teams that possess a
background similar to themselves. Overall, our findings suggest that the founding team is regarded differently in the screening process
of impact investors than it is during the screening process of traditional venture finance investors.

Our findings on business screening criteria are threefold. First, we show that higher financial sustainability increases an SE’s chance
to receive funding by impact investors. This supports Hypothesis 3a and is in line with research that highlights the importance of
economic criteria in the selection process of impact investors (e.g., Miller and Wesley, 2010; Yang et al., 2020). Furthermore, our
finding suggests that impact investors often pursue investments that jointly optimize economic and social returns (e.g., Barber et al.,
2020). Without financial sustainability, competitive financial returns are not achievable. Second, we find that SEs with a high degree of
innovativeness are more likely to be selected by impact investors than are SEs with a medium or low degree of innovativeness, which
supports Hypothesis 3b. This indicates impact investors see SEs’ innovativeness as a relevant condition for achieving financial and
social objectives. Third, we show that proof of concept is an important screening criterion of impact investors. A proof of concepts
shows that the SE is able to combine the sometimes conflicting social and economic goals to achieve a long-turn impact. A direct
comparison between the three business criteria shows that impact investors attach the highest importance to financial sustainability.

Table 7
Results of the conjoint analysis with comparison across two types of impact investment.

Model (1) (2) (3) (4)

Sample Donors vs. equity and debt Equity vs. debt Donors vs. equity Donors vs. debt

Hypotheses H4a & H4b – – –

Interactions Log-odds (SE) Log-odds (SE) Log-odds (SE) Log-odds (SE)

Social impact criteria
Importance of the societal problem: high 0.661 (0.235)*** − 0.085 (0.396) 0.433 (0.255)* 0.909 (0.256)***
Importance of the societal problem: medium 0.351 (0.185)* − 0.201 (0.302) 0.352 (0.198)* 0.407 (0.213)*
(reference group: low)

Scalability: high − 0.079 (0.209) 1.116 (0.325)*** − 0.272 (0.218) 0.081 (0.251)
Scalability: medium 0.385 (0.175)** 0.489 (0.273)* 0.301 (0.197) 0.403 (0.205)**
(reference group: low)

Founding team criteria
Authenticity of the founding team: high − 0.145 (0.258) − 0.214 (0.452) − 0.023 (0.302) − 0.226 (0.302)
Authenticity of the founding team: medium − 0.315 (0.215) 0.056 (0.363) − 0.264 (0.236) − 0.311 (0.241)
(reference group: low)

Founding team background: social − 0.073 (0.208) − 0.739 (0.335)** 0.107 (0.210) − 0.182 (0.256)
Founding team background: technical − 0.237 (0.191) − 0.110 (0.301) − 0.082 (0.189) − 0.418 (0.224)*
(reference group: business)

Business criteria
Financial sustainability: high − 0.480 (0.228)** 0.282 (0.383) − 0.588 (0.249)** − 0.331 (0.270)
Financial sustainability: medium − 0.289 (0.211) − 0.181 (0.354) − 0.162 (0.223) − 0.357 (0.259)
(reference group: low)

Degree of innovation: high 0.120 (0.208) − 0.131 (0.328) 0.137 (0.206) 0.158 (0.237)
Degree of innovation: medium 0.092 (0.186) − 0.579 (0.317)* 0.299 (0.187) − 0.165 (0.234)
(reference group: low)

Proof of Concept: provided 0.033 (0.196) 0.292 (0.318) − 0.170 (0.210) 0.082 (0.240)
(reference group: not provided)
N (decisions) 3.600 1.488 3.456 3.168
N (decision makers) 150 62 144 132

Notes: Estimated with robust standard errors.
The following table demonstrates the results of a clustered multilevel logistic regression with random intercepts and random slopes. The preference of
the decision maker serves as the dependent variable. The independent variables are the attribute levels described in Table 5. Log-odds and standard
errors (clustered at the decision maker level) are displayed. In each Model attribute levels are interacted with a respective type of impact investor. For
example, Model 1 compares donors with equity and debt impact investors. Therefore, we interact every attribute level with a dummy variable which
has the value 1 if the impact investor provides donations and 0 if not. Although the main effects are included in the analysis, they are omitted for
reasons of brevity so that the coefficients displayed here only refer to interaction effects. Exploring interaction effects enables us to identify whether
significant differences between two types of impact investors exist. Model 1 presents the results with regard to Hypotheses 4a & 4b and Model 2–4
shows our additional analysis and robustness checks. * < 0.10, ** p < 0.05, *** p < 0.01.

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5.2. Differences between equity investors, debt investors, and donors

Models 2–4 in Table 6 show the relevance of the respective investment attribute levels for each subsample of impact investors. A
comparison of the models suggests possible differences between the three impact investor types.

To assess these differences in an econometrically sound way, we compute interaction terms to compare the different types of impact
investors. Since the preferences of investors differ based on the form of capital they provide (Ueda, 2004), the relative importances
attached to the screening criteria might differ as well. Table 7 shows the results of these separate multilevel regressions. Each model
represents a comparison between two types of impact investors. For example, Model 2 shows differences between debt and equity
investors. The log-odd coefficients indicate whether significantly different criteria have a higher or lower importance for a particular
type of impact investor, thereby allowing us to identify outstanding impact investors. The following subchapters outline each model in
Table 7 in detail.

5.2.1. Particularities of donors
The first model in Table 7 compares the investment criteria of donors with those of equity and debt impact investors. We find that

donors attach a higher importance to the importance of the societal problem and a lower importance to the SEs’ financial sustain-
ability. No significant differences emerge with regard to the other investment criteria. Thus, these results partially support Hypotheses
4a and 4b. We show that donors in fact attach less weight to the business screening criteria of financial sustainability. However, we do
not find any significant differences concerning the criteria degree of innovativeness and proof of concept. Furthermore, we show that
donors place more weight on the social impact criteria importance of the societal problem, whereas the scalability of the SE is not
perceived as significantly more important by donors than by equity and debt investors.

An explanation for this pattern is that donors typically do not try to achieve any kind of financial return. Since the economic aspect
of investment is not important, the focus shifts towards social criteria. This is in line with the findings of Chowdhry et al. (2019), who
highlight that SEs should particularly seek donors that are fully committed to the social goals of their organizations as a source for
funding. Furthermore, donations are essential to the funding of nonprofit organizations that only focus on the importance of the
societal problem and do not follow any economic objectives. Overall, SEs often rely on this type of impact investment, particularly in
early company stages (Bugg-Levine et al., 2012). Finally, these results confirm the heterogeneous landscape of impact investors and
outline considerable heterogeneity among the investment criteria of impact investors.

5.2.2. Additional analysis and robustness checks
Model 2 of Table 7 compares the investment criteria of equity and debt investors. Both types of investors aim for financial returns

on their investments. However, we find that the two types differ with regard to two investment criteria. First, equity investors put less
value on the social background of a founding team than do debt investors. Second, equity investors consider the scalability of an SE to
be more important than do debt investors.

These results are in line with the finding of previous research on traditional debt and equity investments (e.g., Black and Gilson,
1998; Puri and Zarutskie, 2012). Even though equity and debt investors both seek financial returns, the way in which they achieve
these returns differs. While debt investors obtain regular interest payments on their investments, equity investors profit from exit
proceeds from scaled investments that end in an IPO, for instance. Thus, equity investors (e.g., VCs) aim for highly and easily scalable
ventures to achieve fast exits (Black and Gilson, 1998; Cochrane, 2005). Our results indicate that scalability is important for impact
investors, but more from a social than financial perspective. As shown in our descriptive results, debt investors care more about the
financial part of an investment, and equity investors more highly evaluate the social scalability. Furthermore, Franke et al. (2006) show
that equity investors are affected by similarity biases, which means that they tend to invest in venture teams that show high similarities
to themselves in terms of professional experience or other factors. Since VCs mainly have an educational background in business or
technology (e.g., Bottazzi et al., 2008; Franke et al., 2006, 2008), the similarity bias explains why equity investors attach less value to a
social educational background of a founding team compared to debt investors.

Model 3 and Model 4 of Table 7 display robustness checks. Model 3 shows that the differences between donors and the group of
equity and debt impact investors with regard to the criterion financial sustainability are especially driven by equity investors. A reason
for this result might be the type of impact organization. Thus, debt impact investors are, for example, social banks, which have low
financial return expectations (Brest and Born, 2013). Nevertheless, our findings contrast with the literature that indicates that equity
investors should be more risk-prone since their exit strategy is much riskier due to their continuation strategy (e.g., Ueda, 2004; Winton
and Yerramilli, 2008). Debt investors usually attach a very high value to the financial plans of a venture to ensure repayment (e.g.,
Mason and Stark, 2004). Therefore, it would be obvious to expect them to evaluate the financial sustainability of SEs higher than equity
investors.

6. Conclusion

6.1. Summary

Impact investing has transformed from a niche market into a global movement (e.g., Geczy et al., 2019; The Economist, 2017). This
study is one of the first to explore impact investors’ investment criteria when screening social enterprises. We analyze the screening
criteria of impact investors and compare their relative importance among three types of impact investors based on a CBC experiment
with 179 individual impact investors. This approach enables us to identify distinctive differences between donors as well as equity and

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16

debt investors. This study extends the knowledge within the financial literature since the research thus far lacks an understanding of
the investment criteria of impact investors within the screening phase of their selection processes, as well as an understanding of how
impact investors differ across specific types of investors.

Table 8 summarizes our main results. Impact investors attach the highest relative importance to the team-related criterion of the
authenticity of the founding team, the social impact criterion of the importance of the societal problem, and the business criterion of the
financial sustainability. Further, we compare different types of investors (i.e., equity investors, debt investors, and donors). For example,
we find that the investment criteria of impact investors are particularly different when the group is separated in terms of its financial
return expectations. Hence, impact investors with return expectations (equity investors and debt investors) evaluate the financial
sustainability of an SE higher and the importance of the societal problem lower than those without return expectations (donors). In
addition, we find that further differences exist between equity and debt providers. For equity investors, it is more important that an SE
is scalable and less important that the founding team has a social background.

Our study also has implications for practice, particularly policy-makers, impact investors, and SEs that seek funding. For policy
makers, a better understanding of the heterogeneous field of impact investing is beneficial since public authorities need to adopt
policies or guidelines for own impact investment programs. For example, hybrid fund approaches such as the recently developed
European Social Innovation and Impact Fund (ESIIF) that provides equity impact investments to SEs can use our results to compare
their clearly defined screening criteria with those of other equity impact investors. Impact investing organizations can use our findings
to benchmark their own organizational policies with those of other impact investors. Finally, for SEs that seek funding, we demonstrate
the key attributes of their ventures that should be highlighted when seeking funding from impact investors.

6.2. Limitations and future research

A first set of limitations relates to our CBC experiment. Since the investment criteria used in the experiment needed to be defined in
advance, we were unable to consider additional attributes after the experiment was launched. Thus, our study disregards other at-
tributes that could be of additional importance to impact investors. In general, conjoint studies can therefore suffer regarding construct
validity and can have a preselection bias (Shepherd and Zacharakis, 1999). However, to minimize the risk of selecting the inappro-
priate criteria, we conducted expert interviews before selecting the criteria. Furthermore, since conjoint experiments confront par-
ticipants with hypothetical ventures, external validity may be an issue. However, previous research has provided evidence for the
external validity of conjoint studies under certain conditions (e.g., Shepherd and Zacharakis, 2018). One condition is that the tasks
given to the participants should be as representative as possible for the participant’s real-life tasks (Shepherd and Zacharakis, 2018).
Prior studies show that real decision-making behavior often correlates strongly with the estimated decision behavior. To address
external validity, we conducted a pretest with experienced impact investors to confirm our selection of attributes and attribute levels.
Another limitation of the forced CBC experiment is that decision-makers might sometimes perceive two investment opportunities as
equally attractive but are still forced to choose one of them. This weakness could be eliminated through other conjoint models, such as
a rating-based conjoint experiment. However, previous research has indicated that the results between both approaches are highly
similar (Elrod et al., 1992).

Since we investigate impact investors from the D/A/CH region, future research could test whether our results hold for impact
investors globally. Previous research has suggested that traditional investments differ between Europe and the US (Hege et al., 2009).
Such differences might similarly exist between impact investors from Europe and the US, especially because the concept of social
entrepreneurship differs between both markets (Defourny and Nyssens, 2010). Future research might find it interesting to explore such
differences between Europe and the US. Furthermore, research on impact investing is still in the early stages, which allows for many

Table 8
Summary of the main findings.

Rank Attribute Relative
importance

Lowest relative
importance

Highest relative
importance

Main results (qualitative summary)

1 Authenticity of the
founding team

25.7% 25.2% (DOs) 29.0% (DEs) No major differences across the three types of impact investors.

2 Importance of the
societal problem

24.0% 18.2% (EQs) 26.1% (DOs) Major differences across impact investor types: less important to
equity and debt investors, and more important to donors.

3 Financial
sustainability

16.9% 15.5% (DOs) 21.4% (DEs) Minor differences across impact investor types: less important to
donors, and more important to equity investors.

4 Scalability 14.2% 9.5% (DEs) 16.4% (EQs) Minor differences across impact investor types: less important to
debt investors, and more important to equity investors.

5 Proof of Concept 9.7% 9.9% (DOs) 10.7% (EQs) No major differences across the three types of impact investors.
6 Degree of innovation 8.2% 6.9% (EQs) 8.8% (DOs) No major differences across the three types of impact investors.
7 Founding team

background
1.4% 0.7% (EQs) 3.4% (DEs) Minor differences across impact investor types: social

background is less important to equity investors, and more
important to debt investors.

This table demonstrates the summary of our main findings. We rank the attributes according to the results of Table 6. Column 3 shows the mean values
across all types of impact investors, while columns 4 and 5 display the investor type with the lowest and highest importance. The final column is based
on Table 7 and contains a brief qualitative summary of the main findings of our comparison across the three types of impact investors. All attributes
are defined in Table 5. We consider donors (DOs), equity investors (EQs), and debt investors (DEs).

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Journal of Corporate Finance 66 (2021) 101813

17

future research directions. For example, based on our study, future research could investigate whether selection processes differ among
impact investors with regard to the investment stage in which they invest.

Funding

Deutsche Forschungsgemeinschaft (DFG). The title of the grant is “Entscheidungskriterien von Risikokapitalgebern in der
Spätphasenfinanzierung von Wachstumsunternehmen”. The grant ID is 400567894. The grant was received by Joern Block, who is first
author.

Declaration of Competing Interest

The authors declare that they have no conflict of interest.

Acknowledgments

We thank Steve Kaplan and participants of the 2019 4th ENTFIN Conference in Trier, Germany, for helpful comments on earlier
versions of the manuscript.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jcorpfin.2020.101813.

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• Which criteria matter when impact investors screen social enterprises?

1 Introduction
2 Conceptual background
2.1 Venture philanthropy and impact investing
2.2 Goals and types of impact investors
2.3 Selection process and screening criteria in impact investing
3 Hypotheses
3.1 The importance of specific investment criteria
3.1.1 Social impact criteria
3.1.2 Founding team criteria
3.1.3 Business criteria
3.2 Differences across different types of impact investors
4 Research design
4.1 Data and sample
4.2 Descriptive statistics
4.2.1 Equity investors
4.2.2 Debt investors
4.2.3 Donors
4.3 Design of the choice-based conjoint experiment
5 Results and discussion
5.1 Relative importance of impact investors’ investment criteria
5.2 Differences between equity investors, debt investors, and donors
5.2.1 Particularities of donors
5.2.2 Additional analysis and robustness checks

6 Conclusion
6.1 Summary
6.2 Limitations and future research
Funding
Declaration of Competing Interest
Acknowledgments
Appendix A Supplementary data
References

Contents lists available at ScienceDirect

Journal of Corporate Finance

journal homepage: www.elsevier.com/locate/jcorpfin

Smart investments by smart money: Evidence from acquirers’
projected synergies
Ahmad Ismaila, Samer Khalila, Assem Safieddinea, Sheridan

T

itmanb,⁎,1
a American University of Beirut, Olayan School of Business, Bliss Street, P.O. Box: 11-0236, Beirut, Lebanon
b McCombs School of Business, University of Texas, Austin, CBA 6.266, United States

A R T I C L E I N F O

Keywords:
Stock picking
Mergers
Acquisitions
Institutional investors

JEL classification:
G23
G140

A B S T R A C T

Institutional investors tend to accumulate the shares of firms that announce acquisitions. The
tendency to accumulate shares is stronger when the acquirer discloses synergy forecasts, and it is
especially strong when the disclosed synergies are higher. This evidence is consistent with the
idea that institutional investors are attracted to situations where their superior access to man-
agement and analysts provides an information advantage. Indeed, this tendency to accumulate
information sensitive shares is especially strong for hedge funds, which tend to have the greatest
information advantage. Moreover, stock prices respond favorably in the quarter following the
acquisition announcement when higher institutional holdings are revealed.

1. Introduction

Institutional investors devote considerable resources to their stock selection efforts. Starting with Grinblatt and Titman (1989),
researchers have used data from the SEC filings of institutional stock holdings and find evidence consistent with the hypothesis that
the efforts of these institutions do in fact lead to superior stock selections. Moreover, recent evidence suggests that hedge funds,
which are incentivized to devote the most resources to these efforts, tend to outperform other categories of institutional investors.2

A plausible explanation for this superior performance is that institutions, in particular hedge funds, tend to have better access to
management, and as a result, have an information advantage over retail investors. If this is indeed the case, one might expect these
investors to do particularly well when their information advantage is likely to be strongest, i.e., during periods when firms experience
some sort of transition.

To understand this, consider how different types of investors may be influenced by equity issue announcements. Equity issues can
be interpreted as good news, because they signal favorable investment opportunities, or bad news, (e.g., Myers and Majluf (1984))
because they signal that the firm’s stock may be overpriced. Hence, having access to soft information from management is likely to be
particularly valuable when firms are raising external equity. Indeed, Gibson et al. (2004) find that institutions do tend to outperform
around seasoned equity issues (SEOs). Specifically, those SEO issuers experiencing the greatest increase in institutional ownership

https://doi.org/10.1016/j.jcorpfin.2019.03.003
Received 11 February 2019; Accepted 21 March 2019

⁎ Corresponding author.
E-mail addresses: ai05@aub.edu.lb (A. Ismail), sk61@aub.edu.lb (S. Khalil), as57@aub.edu.lb (A. Safieddine),

titman@mail.utexas.edu (S. Titman).
1 We would like to thank participants at the 2018 JCF Hong Kong Poly conference. We would especially like to thank Bing Han, Jie Cao, Douglas

Cumming and two anonymous reviewers.
2 See Agarwal et al., 2016for a review of the hedge fund literature. Swem (2016) provides further information about how hedge funds generate

superior performance. Specifically, he finds that hedge fund trades tend to anticipate analyst upgrade and downgrade reports, while mutual funds
tend to trade after analyst reports are released.

Journal of Corporate Finance 56 (2019) 343–

363

Available online 22 March 2019
0929-1199/ © 2019 Published by Elsevier B.V.

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https://doi.org/10.1016/j.jcorpfin.2019.03.003

mailto:ai05@aub.edu.lb

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mailto:as57@aub.edu.lb

mailto:titman@mail.utexas.edu

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around the offer date outperform their benchmark portfolio.
In this paper we explore the possibility that access to management is especially important around M&A announcements. As in the

case of SEOs, acquisitions can be interpreted in multiple ways. Acquisition announcements can be viewed as negative news, in-
dicating that managers are either empire builders or that they think their core business is struggling, necessitating a need to diversify
into another line of business. Alternatively, acquisition announcements can be viewed as positive news, indicating that management
has identified a target with attractive synergies. Hence, soft information about the quality and intentions of management may put
institutional investors in an advantageous position when they interpret these announcements.

Our research design consists of two parts: The first part of our research design examines whether institutions tend to accumulate
shares around announcements that increase the importance of soft information. Of course, institutions can potentially exploit this
information by selling or shorting shares as well as buying shares. However, given short sale restrictions (and the fact that we only
observe long transactions) we expect to see an increase in observed institutional holdings around these events. Of course, over-
confident institutional investors may simply act as though they have special information around these events. The second part of our
research design addresses whether market participants believe they are informed by examining whether the revelation of the in-
stitutional trades convey information. We do this by analyzing the stock returns in the future quarter when the changes in institu-
tional holdings are publicly revealed.

As we show, institutions do in fact have a tendency to accumulate shares in companies in both the contemporaneous quarter and
the quarter following acquisition announcements. This tendency is stronger for hedge funds, which are more likely to be informed,
than for other institutions, and is stronger for both hedge funds and other institutions when the acquisitions are larger, and pre-
sumably more important.3 We also find that the effect is stronger when the acquiring firm reveals that the acquisition is likely to
generate significant synergies. We conjecture that soft information is likely to be more important for mergers that are expected to
generate greater synergies, since these combinations require more integration, suggesting that their success is likely to be less certain.

We then show that the market reaction in the quarter following the acquisition announcement is consistent with the hypothesis
that the trades were in fact generated by special information. Specifically, we find positive returns for those deals where institutions
increase their holdings in the previous quarter. The returns are higher when the hedge funds increase their holdings and it is higher
for those deals where high synergies are projected.

As we mentioned at the outset, we are not the first to suggest that institutions may have a comparative advantage selecting the
stocks of firms in a state of transition. Gibson et al. (2004) find that issuers experiencing the greatest increase in institutional
ownership around seasoned equity issues outperform their benchmark portfolios in the first post-issue year. Similarly, Field (1995)
and Field and Lowry (2009) find that Initial Public Offerings (IPOs) with high institutional ownership perform better in the three-year
post IPO period than those with little or no such ownership. Likewise, Krigman et al. (1999) find that IPOs with heavy institutional
first-day selling perform the worst in the following year. More recently, Gucbilmez (2015) finds that while many institutions bid for
shares in cold IPOs as well as hot ones, a small proportion of institutions successfully cherry-pick hot IPOs and earn higher returns
than uninformed investors. We are also not the first to examine institutional holdings around merger announcements. For example,
there are a number of studies that link post-merger performance to the presence of institutional investors.4 However, relative to these
earlier studies, we use synergy forecasts as a proxy for the importance of soft information and explicitly look at stock returns around
the time when the institutional holdings are revealed to the market.

The paper proceeds as follows. Section 2 presents our methodology and data set. Section 3 discusses the empirical findings, while
Section 4 provides our conclusions.

2. Data description

We extract our sample from Thomson Financial SDC Database for all the M&A deals completed in the U.S. market between
January 1st, 1990 and Dec. 31st, 2013, where the acquiring and target firms are both publicly listed on the US stock markets.5 We
collect share price data from the Centre for Research in Security Prices (CRSP) database and accounting data from COMPUSTAT.
Additionally, we retrieve institutional shareholdings (13f) data for 1989–2014 from Thomson Reuters Ownership Database, which
reports institutional shareholdings as of the end of each calendar quarter.

The subsample of institutions that are classified as hedge funds are identified in the Swem (2016) study. Specifically, the funds are
identified by manually matching over 2500 hedge fund names listed in the FactSet LionShares holdings data from 2004 to 2015
against each of the over 14,000 names of 13-F filings institutions from the Thomson Reuters S34 file over the same period.6

3 In theory, the less informed investors are less likely to trade in situations where they are at an information disadvantage. In most cases, it is easy
for an uninformed investor to avoid acquiring a stock when they are at an information disadvantage, but it may be the case that an uninformed
investor has a liquidity event that forces it to sell. As a result, we expect to see more informed buys and uninformed sells when asymmetric
information is high.

4 Demiralp et al. (2011) also find a relation between post-merger performance and institutional holdings. In addition, Gasper et al. (2005) find that
acquirers held by institutions with low turnover rates outperform those held by short-term institutional investors after merger, Chen et al. (2007)
show that concentrated holdings of independent long-term institutions (ILTIs) are positively related to post-merger performance and Nain and Yao
(2013) find that mutual fund stock selection skill predict the post-merger performance. In a related finding, Fich et al. (2015) find that holdings of
monitoring institutions in the target firm results in higher final premiums and lower acquirer returns.

5 We exclude from these deals Privatizations, Leveraged Buyouts, Spinoffs, Recapitalizations, Self-Tenders Repurchases, and Exchange Offer
6 See Swem (2016) for further details. We thank Nathan Swem for generously sharing his data.

A. Ismail, et al. Journal of Corporate Finance 56 (2019) 343–363

344

The initial sample of acquisitions includes 3380 deals of which 3108 have complete information on Thomson Financial. We
further refine the sample following standard refinement criteria as follows:

(i) Percentage of shares held by the acquirer six months prior to announcement is < 50%. (ii) Percentage of shares owned after the transaction (completed deals) is > 50%.7

These two criteria are meant to ensure that the deals result (when completed) in a transfer of control. Following previous studies
on mergers and acquisitions and/or on institutional investors (e.g. Chunga and Zhanga, 2011; Hovakimian and Hu, 2016), we exclude
financial companies (Standard Industrial Classification (SIC) codes 6000–6999) and utilities firms (SIC codes 4900–4949) from the
sample.

One of the main variables in this study is the managerial forecasts of incremental cash flows for each acquisition. To obtain this
data and calculate synergy, we follow Houston et al. (2001), Dutordoir et al. (2014), and Ismail (2011) and collect managerial
forecasts reported in Form 8-K filings and proxy statements DEF14, DEFM14A, and S-4 filed with the SEC, in addition to the business
press. Ultimately, our sample of 3108 deals consists of 607 completed deals with available merger synergy forecasts and 2501 without
such forecasts.8

Panel A of Table 1 describes our sample of 3108 acquisitions. Specifically, we report the method of payment and other deal
characteristics, e.g., industry-related acquisition, hostile, competing offer, and deals with acquirer toehold. The table shows that cash
is slightly more frequently used as a method of payment (in 1063 deals) than equity (in 1034 deals) and mixed offers (in 1011 deals).
Around 62% (1939) of the acquisitions are industry related. In a small percentage of the deals the acquirer had a toehold, the deal
was hostile, and there were competing offers.

Panel B of Table 1 presents the distribution of the total sample of acquirers and targets according to the Fama-French 12 in-
dustries’ classification. The largest percentage of acquirers and targets (31.72% and 32.53% respectively) operates in Business
Equipment and the smallest percentage (1.83% of acquirers and targets) in Consumer Durables.

Table 2 reports descriptive statistics for the acquirer, target, and deal characteristics of the two deal sub-samples. A glance at the
table reveals that Forecast and No-Forecast sub-samples are significantly different. Firms that forecast synergies tend to be larger,
slightly more leveraged, and have lower Market to Book ratios, lower Tobin’s Q ratios and have higher institutional holdings as
evidenced by a mean (median) of 67.96% (73.76%) relative to 52.56% (55.95%) for No-Forecast firms. Forecast deals are also larger
on average.9

The evidence in the table also indicates that acquirers are more likely to announce synergy forecasts when the deal is expected to
have a more significant impact on the acquirer’s performance; the mean relative size of the target to acquirer of 68.81% for Forecast
deals is high relative to 37.39% for No-Forecast deals. Equally important, the evidence indicates that firms that forecast synergies pay
a lower premium as demonstrated by a mean (median) of 39.19% (34.03%) compared to 49.34% (43.13%). In fact, these statistics are
qualitatively similar to those reported in Bernile and Bauguess (2011) and Dutordoir et al. (2014). For instance, Dutordoir et al.,
2014also report a lower takeover premium paid by forecasting acquirers relative to non-forecasting firms. Additionally, both Bernile
and Bauguess (2011) and Dutordoir et al. (2014) show that forecasting firms have lower valuation ratios (M/B and/or Tobins Q),
larger size and higher leverage among other statistics. Finally, the statistics of forecasted synergies (the present value of synergies,
ratio of reported synergies-to-acquirer equity and premium-to-synergy), reported in Table 2 are similar to the figures reported by
Bernile and Bauguess (2011) and Dutordoir et al. (2014).

Table 3 sorts the sample of synergy disclosers into terciles based on the level of the estimated disclosed synergy relative to the
acquirer’s value. The mean (median) synergy percentage is 2.46% (2.2%) for deals in the Low tercile relative to 44.87% (34.67%) for
deals in the High tercile. The table reveals that acquirers reporting high synergies are smaller, more highly leveraged, have sig-
nificantly lower market to book ratios and lower institutional holdings, relative to their counterparts in the low forecasted synergies’
tercile. High synergy targets are also more leveraged, have weaker operating performance as measured by operating cash flow (OCF-
to-Assets), and have lower market to book ratios by the market relative to their counterparts in the low synergy tercile. We also find
that cash financing is used less for high forecasted synergy deals (18.37% for the High tercile relative to 37.76% for the Low tercile).

7 It should be noted, that like Houston et al. (2001), Ismail (2011), Bernile and Bauguess (2011), Dutordoir et al. (2014) and Netter et al. (2011),
we include only completed deals, so there is some selection bias. We only focus on completed deals since our hand collected data on forecasted
synergies is obtained mostly from SEC filings that occur after the merger is completed, and is thus available only from completed deals. Thomson
Financial primary data shows that during our sample period, out of 11,343 announced acquisitions in all industry sectors in the USA, 8

345

deals
were completed regardless of whether these have any data available on CRSP, Compustat or on 13F filings. On the other hand, Thomson Financial
also report the management forecasted synergy for a very small number of deals. For instance, out of 11,343 announced deals, 258 acquisitions have
synergy data reported by Thomson Financial; while only15 deals of these (5.8% of the total sample) were not completed, which implies that for
deals with disclosed synergy forecasts, the probability of not completing the deal is only around 6%.

8 It is also worth noting that the frequency of voluntarily disclosing incremental cash flow forecasts has increased substantially over time,
especially among larger deals. In fact, we present in Appendix B a table containing the frequency of disclosure in our sample and we notice that the
percentage of deals associated with synergy forecasts exceeded 60% (70%) for medium (large) deals recently and that the disclosure for small deals
has also increased significantly reaching > 20% in some instances as well.

9 Variables definitions are in Appendix A

A. Ismail, et al. Journal of Corporate Finance 56 (2019) 343–363
345

3. Empirical results

3.1. Announcement returns and post-acquisition cash flows

In this section we examine the returns of the acquiring firm and target on the announcement of the acquisition as well as their
combined cash flows after the acquisition. If the synergy forecasts are credible, they should influence announcement returns and they
should correspond to actual changes in cash flows.

Table 4 reports the stock returns of both the targets and the acquirers around the acquisition announcements. Consistent with the
prior literature, the acquirers in our sample tend to have modest negative returns on the acquisition announcements. The negative
returns are somewhat larger for deals that disclose synergies, perhaps, reflecting the fact that these deals are larger on average.
However, conditioned on disclosing synergies, those that disclose higher synergies tend to have less negative announcement returns.
The target returns, as expected, are largely positive, and the combined announcement returns of the acquirer and the target are

Table 1
Sample Summary: The table presents the number of acquisitions for the whole sample during each year partitioned by the method of payment: Pure
Cash, Pure Shares, or Mixed offers. We also report the numbers for Industry-Related, Hostile, Competing Offer, for deals with acquirer Toehold,
during the Financial Crisis and Dot Com bubble periods and during Bear Market periods in each announcement year. The sample comprises the
acquisitions announced by US acquirers between January 1990 and December 2013 as reported by the SDC, where the acquirer completes a deal and
gains control of a public target firm. we exclude financial companies (Standard Industrial Classification (SIC) codes 6000–6999) and utilities (SIC
codes 4900–4949) from the sample. In Panel B we report the distribution of acquirers and target firms based on the Fama-French 12 Industry groups.

Panel A

Year Cash Shares Mixed Industry Related Toehold Hostile Competing Total

1990 22 25 20 33 7 2 2 67
1991 11 23 35 37 5 0 3 69
1992 14 20 28 34 8 1 2 62
1993 19 27 34 54 7 0 3 80
1994 31 65 48 88 12 5 6 144
1995 48 87 45 108 13 8 11 180
1996 48 83 71 120 10 4 5 202
1997 57 98 76 138 5 4 12 231
1998 83 126 90 194 10 1 9 299
1999 82 115 71 168 11 4 5 268
2000 60 101 76 145 1 2 5 237
2001 51 70 65 125 9 1 7 186
2002 50 29 34 70 3 1 9 113
2003 38 27 45 77 5 2 3 110
2004 44 26 35 69 3 1 1 105
2005 53 20 44 75 5 1 9 117
2006 68 16 30 66 3 0 3 114
2007 64 13 34 68 0 0 2 111
2008 38 15 30 56 3 0 5 83
2009 30 17 34 55 4 0 3 81
2010 50 11 22 54 0 0 3 83
2011 22 7 19 29 2 0 1 48
2012 46 8 13 39 3 0 0 67
2013 34 5 12 37 0 0 1 51
Total 1063 1034 1011 1939 129 37 110 3108

Panel B: Distribution of sample acquires and targets by Fama-French 12 Industries.

Fama-French Industry Codes and Description Acquirer industry Target industry

Frequency Percent Frequency Percent

1 Consumer Non-Durables – Food, Tobacco, Textiles, Apparel, Leather, Toys 140 4.50 138 4.44
2 Consumer Durables – Cars, TV’s, Furniture, Household Appliances 57 1.83 57 1.83
3 Manufacturing 294 9.46 270 8.69
4 Energy 185 5.95 181 5.82
5 Chemicals and Allied Products 58 1.87 53 1.71
6 Business Equipment 986 31.72 1011 32.53
7 Telephone and Television Transmission 244 7.85 195 6.27
9 Shops Wholesale, Retail, and Some Services 270 8.69 279 8.98
10 Healthcare, Medical Equipment, and Drugs 489 15.73 490 15.77
12 Other 385 12.39 434 13.96
Total 3108 100 3108 100

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346

positive, and are highest for deals that announce the highest synergies. Specifically, the mean (median) merged entity CAR (−1, +1)
is 3.8% (3.59%) for the high tercile relative to 0.34% (0.21%) for the low tercile with the difference in mean (median) being
significant at the 1% level. This observation provides evidence that the synergy forecasts are in fact credible.

To further examine the accuracy of these synergy forecasts we examine the change in the merged firms’ abnormal performance
from pre- to post-acquisition; i.e. the difference between the abnormal operating performance in year −1 and the median of years 1,
2, and 3 relative to the acquisition year. To conduct this analysis, we follow Powell and Stark (2005), Ghosh (2001), and Healy et al.
(1992) and measure abnormal operating performance as the operating performance of the firm minus the operating performance of a
matched sample of firms, where firms are matched by SIC codes and firm size.10

Panel A of Table 5 reports the change in operating performance for the Low Synergy subsample. The results suggest that the
change in operating performance from pre- to post-acquisition for firms announcing low synergies is not significantly different from
that of their matched firms. In other words, we cannot reject the hypothesis that these acquisitions generate zero synergies. In

Table 2
Sample Descriptive Statistics for Forecast and No-Forecast Firms.

The table reports descriptive statistics of the sample containing mean, median for various deal, acquirer and target characteristics split by Forecast
and No-Forecast deals. Forecast deals are those in which the acquiring firm’s management disclosed cost saving estimates and/or other incremental
cash flow estimates of the merger deal, where this information is collected from SEC filings and various press releases. In addition to the accounting
variables for acquires and target firms, and to deal characteristics, the table reports statistics of the Institutional ownership and of the ownership
concentration (Herfindahl Index) of the acquirer and target during quarter −1 relative to the merger announcement quarter. These two variables
are collected from 13-F filings. All acquirer and target characteristics are taken at the end of the fiscal year prior to the acquisition. Variables
definitions are in Appendix A. Dollar values are in millions.

Forecast No-Forecast P-Value Mean Diff.

607 2501

Mean Median Std Mean Median Std

Acquirer characteristics
Equity MV 9

350

2

348

17,264 8086 939 17,255 0.1129
Assets MV 14,578 3787 24,969 11,912 1430 24,837 0.0200
Debt-to-Assets MV 0.3321 0.2989 0.1903 0.2672 0.2319 0.1885 < 0.0001 OCF-to-Assets MV 0.0732 0.0746 0.0509 0.0563 0.0650 0.0612 < 0.0001 M / B 3.5880 2.4725 3.2516 4.2750 2.9677 3.7690 < 0.0001 Tobins' Q 2.0864 1.6335 1.3503 2.6881 1.9769 1.9418 < 0.0001 Hedge Fund Ownership 0.0472 0.0304 0.0442 0.0298 0.0165 0.0342 < 0.0001 Institutional Ownership 0.6796 0.7376 0.2192 0.5256 0.5595 0.2587 0.0001

Target characteristics
Equity MV 1159 663 1179

355

96 656 < 0.0001 Assets MV 2026 1132 2082 526 145 1029 < 0.0001 Debt-to-Assets MV 0.3699 0.3482 0.2177 0.3280 0.2764 0.2391 < 0.0001 OCF-to-Assets MV 0.0603 0.0784 0.0876 0.0169 0.0520 0.1240 < 0.0001 M / B 2.8385 2.1398 2.3556 2.8035 1.9340 2.5873 0.7574 Tobins' Q 1.8568 1.5013 1.1206 2.0718 1.5293 1.4109 < 0.0001 Institutional Ownership 0.6000 0.6484 0.2605 0.3484 0.2992 0.2608 0.0001

Deal characteristics
PV of Synergy 1263.40 183.51 6020 NA NA NA NA
Synergy/Acq.Eq. 0.1928 0.1019 0.2393 NA NA NA NA
Premium-to-Synergy 1.6727 0.8307 2.3383 NA NA NA NA
Industry Related 0.6985 1.0000 0.4593 0.5936 1.0000 0.4913 < 0.0001 Deal Value 1841 967 1851 512 130 1012 < 0.0001 Relative size 0.6881 0.5368 0.5790 0.3739 0.1500 0.5067 < 0.0001 Premium relative to day −40 0.3919 0.3403 0.3488 0.4934 0.4313 0.4336 0.0001 Cash 0.2801 0.0000 0.4494 0.3246 0.0000 0.4683 0.0286 Shares 0.2784 0.0000 0.4486 0.3426 0.0000 0.4747 0.0016 Mixed 0.4415 0.0000 0.4970 0.3329 0.0000 0.4713 < 0.0001 Hostile 0.0231 0.0000 0.1502 0.0083 0.0000 0.0907 0.0200

10 Operating Cash Flow is sales minus cost of goods sold, selling and general administrative expenses, and working capital change. Market Value of
Assets is calculated as total book value of assets minus the book value of equity plus the market value of equity. Pro-forma data of merged firms for
pre-acquisition years are created by aggregating acquiring and target firms’ data. The matching procedure is in line with Powell and Stark (2005)
and Ghosh (2001). That is, matched non-merging firms are selected if they have the same three-digit SIC codes as the merging firms and their size
(book value of assets) is within 25%–200% of the size of the merging firms. Furthermore, in cases where we do not find at least 10 matching firms,
we repeat the matching procedure on two-digit SIC codes and size and then on one-digit SIC code and size.

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contrast, Panel B presents evidence that the high synergy mergers lead to an increase in operating performance of the combined entity
of 1.14%, which is significant at the 5% level. The difference in the change in abnormal performance between the Low and High
Synergy subsamples is −1.04%, which is significant at the 10% level. These results suggest that the firms announcing the highest
synergy forecasts exhibit improved post-acquisition performance relative to their matched firms and their counterparts in the Low
Synergy subsample.

3.2. An analysis of institutional holdings

In this section we examine how acquisition announcements and synergy forecasts influence institutional holdings. We start with a
univariate analysis. As we note, the univariate results can be influenced by characteristics of acquirers that are correlated with their
incentives to reveal synergy forecasts. Most notably, larger acquirers are much more likely to forecast synergies. We then provide a
multivariate analysis that controls for these characteristics.

3.2.1. A univariate analysis
Table 6 reports the level and changes of the institutional holdings of acquirers in the quarters surrounding the merger an-

nouncement. Panel A reveals that institutions have significantly lower ownership stakes in acquirer firms that do not disclose the
synergy forecasts. This is at least partly due to the fact that firms that do not disclose synergies tend to be smaller (their average total
assets are $1430 million vs. $3787 million). In addition, firms that disclose synergies may be more transparent in general, which may

Table 3
Sample Statistics by Low, Medium and High (Synergy/Acq.Eq.)

The table reports sample statistics for three sub-samples based on the level of the estimated merger synergy (low, medium and high) and presents
analysis of the difference in mean between the Low and High synergy sub-samples. PV of Synergy is the after-tax present value of the incremental
cash flows where incremental cash flows are disclosed by the management of the acquiring firm. The calculation of the PV of Synergy follows a
procedure similar to Kaplan and Ruback (1995) and Gilson et al. (2000), Houston et al. (2001), Ruback (2002), Devos et al. (2009) and Ismail
(2011). The calculation of the discount rate is based on the Capital Asset Pricing Model (CAPM) where the equity beta is the weighted average equity
beta of the target and the acquirer. The weights are the market value of equity of the corresponding party taken two months prior to the acquisition
announcement. The beta is estimated from the market model where stock returns are regressed against CRSP value weighted returns in the (−210,-
21) window prior to the acquisition announcement. Synergy/Acq.Eq. is the PV of Synergy divided by the equity value of acquirer. Variables
definitions are in Appendix A.

Low Medium High

Mean Median Mean Median Mean Median P-value Mean difference (Low vs. High)

N 196 196 196

Acquirer characteristics
Equity MV 15,416 4840 8204 2434 4431 879 < 0.0001 Assets MV 22,006 7598 13,312 3993 8092 1731 < 0.0001 Debt-to-Assets MV 0.2753 0.2441 0.3176 0.2935 0.4009 0.3807 < 0.0001 OCF-to-Assets MV 0.0764 0.0713 0.0719 0.0735 0.0711 0.0789 0.3448 M / B 4.2109 2.9215 3.8886 2.9252 2.5856 1.7553 < 0.0001 Tobins' Q 2.4333 1.8508 2.2472 1.7709 1.5818 1.3191 < 0.0001 Hedge Fund Ownership 0.0378 0.0264 0.0483 0.0310 0.0568 0.0382 < 0.0001 Institutional Ownership 0.7296 0.7465 0.7011 0.7517 0.6042 0.6546 < 0.0001

Target characteristics
Equity MV 1237 776 1164 687 1107 530 0.2948
Assets MV 1923 1115 2062 1180 2178 1177 0.2470
Debt-to-Assets MV 0.3008 0.2545 0.3636 0.

353

1 0.4511 0.4663 < 0.0001 OCF-to-Assets MV 0.0721 0.0777 0.0629 0.0812 0.0445 0.0772 0.0043 M / B 3.3459 2.6449 2.8219 2.1027 2.3137 1.7311 < 0.0001 Tobin's Q 2.1612 1.8370 1.8478 1.5198 1.5449 1.2833 < 0.0001 Institutional Ownership 0.6396 0.7050 0.6179 0.6459 0.5488 0.5752 0.0011

Deal characteristics
PV of Synergy 371 95 1014 235 2405

360

0.0057
Synergy/Acq.Eq. 0.0246 0.0220 0.1052 0.1019 0.4487 0.3467 < 0.0001 Industry Related 0.6939 1.0000 0.6735 1.0000 0.7245 1.0000 0.5058 Deal Value 1880 1129 1895 1156 1806 797 0.6956 Relative size 0.4066 0.2208 0.6317 0.5202 1.0258 0.8875 < 0.0001 Premium relative to day −40 0.3668 0.3335 0.4166 0.3767 0.3994 0.3312 0.3652 Cash 0.3776 0.0000 0.2806 0.0000 0.1837 0.0000 < 0.0001 Shares 0.1990 0.0000 0.3010 0.0000 0.3316 0.0000 0.0029 Mixed 0.4235 0.0000 0.4184 0.0000 0.4847 0.0000 0.2245 Hostile 0.0153 0.0000 0.0

357

0.0000 0.0204 0.0000 0.7038

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make them more attractive to institutional investors for a number of reasons. Panel B reveals that in the announcement quarter,
institutions increase their holdings of all acquirers; those that disclose synergies as well as of those that do not disclose synergies.
However, they increase their holdings of acquirers that disclose synergies more aggressively.

Panels C and D of Table 6 replicate the analysis presented in the previous panels for our subsample of hedge funds. Consistent with
other institutional investors, hedge funds hold a greater fraction of the shares of the disclosing acquirers and also tend to increase
those holdings in the announcement quarter. However, the changes by hedge funds are much more significant. For example, the
change in hedge fund holdings for the forecast subsample from quarter −1 to 0 is 0.7%, which represents a 15% increase in holdings
(from a base of 4.7% in quarter −1), compared to a change of 2.1% by total institutions, which denotes a 3% increase (from a base of
68% in quarter −1).

Table 7 presents our analysis of institutional holdings for the subsample of acquisitions that disclose synergies. Panel A reveals
that institutions tend to have higher holdings in acquiring firms that forecast lower synergies. On average, in quarter −1 they hold
73% of the shares of the lowest tercile synergy acquirers and 60.4% of the highest tercile acquirers. Again, this may just be a size
effect, those that disclose lower synergies tend to be larger, and have nothing to do with the future synergy forecasts. However, as
shown in Panel B, between quarters 0 and 1 institutions decrease their ownerships in the Low tercile firms and increase them in the
High synergy tercile. Moreover, between quarters 0 and 3 institutional holding levels decrease by 1.8% in low synergy tercile firms
and increase by 0.8% in their high synergy tercile. The difference is significant at the 1% level.

Panels C and D examine the holdings and changes in holdings for hedge funds. In contrast to other institutions, hedge funds hold
more of the higher synergy acquirers prior to the acquisition announcement. In addition, they increase their holdings more in the
high synergy acquirers in subsequent quarters. For example, the level of holdings in quarter −1 is 3.8% for the low synergy sub-
sample compared to 5.7% in the high synergy sub-sample. The change in holdings from quarter −1 to 0 is 0.4% in low synergy
acquirers, (an increase of around 15% from a base of 3.8%) compared to 1% in high synergy firms, which represents an increase in
holding of 19% from a base of 5.7% in quarter −1. The difference is significant at the 1% level.11

Table 4
Announcement Returns by Forecast No-Forecast and Low, Medium and High (Synergy/Acq.Eq.)

The table reports announcement returns in two panels. Panel A reports announcement returns for two sub-samples: Forecast and No-Forecast deals
and presents analysis of the difference in mean between the two sub-samples. Panel B reports returns for three sub-samples based on the level of the
estimated merger synergy (low, medium and high) and presents analysis of the difference in mean between the Low and High synergy sub-samples.
PV of Synergy is the after-tax present value of the incremental cash flows where incremental cash flows are disclosed by the management of the
acquiring firm. The calculation of the PV of Synergy follows a procedure similar to Kaplan and Ruback (1995) and Gilson et al. (2000), Houston et al.
(2001), Ruback (2002), Devos et al. (2009) and Ismail (2011). The calculation of the discount rate is based on the Capital Asset Pricing Model
(CAPM) where the equity beta is the weighted average equity beta of the target and the acquirer. The weights are the market value of equity of the
corresponding party taken two months prior to the acquisition announcement. The beta is estimated from the market model where stock returns are
regressed against CRSP value weighted returns in the (−210,-21) window prior to the acquisition announcement. Synergy/Acq.Eq. is the PV of
Synergy divided by the equity value of the acquirer only. CAR (−2,+2) is the 5-day cumulative abnormal returns and CAR (−1,+1) is the 3-day
cumulative abnormal returns estimated using the market model. Abnormal returns are estimated using a standard event study methodology as in
Brown and Warner (1985) and employing the market model. The market model’s parameters are estimated over the (−210,-21) interval using the
CRSP value-weighted index returns as the benchmark. The statistical significance of the returns is tested using the Patell (1976) test corrected for
time-series and cross-sectional variation of abnormal returns.

Acquirer Target Combined Entity

Panel A N CAR (−2,+2) CAR (−1,+1) CAR (−2,+2) CAR (−1,+1) CAR (−2,+2) CAR (−1,+1)

Mean −0.0218 −0.0221 0.1783 0.1715 0.0228 0.0203
Forecast [Median] 607 [−0.0166] [−0.0174] [0.1676] [0.1561] [0.0175] [0.0162]

Mean −0.0095 −0.0079 0.1929 0.1943 0.0147 0.0135
No-Forecast [Median] 2501 [−0.0058] [−0.0039] [0.1834] [0.1819] [0.0115] [0.0096]
P-value Mean Difference (Forecast vs. No-Forecast) 0.0018 < 0.0001 0.0656 0.0029 0.0

359

0.0473

Panel B

Low Mean 196 −0.0245 −0.0294 0.1884 0.1773 0.0095 0.0034
[Median] [−0.0105] [−0.017] [0.1774] [0.172] [0.0073] [0.0021]

Medium Mean 196 −0.0243 −0.0224 0.1814 0.1763 0.02 0.0204
[Median] [−0.0234 [−0.0232] [0.1753] [0.1595] [0.0204] [0.0239]

High Mean 196 −0.016 −0.0143 0.1657 0.162 0.0394 0.038
[Median] [−0.0156 [−0.0131] [0.1436] [0.1452] [0.0374] [0.0359]

P-value Mean difference (Low vs. High) 0.3293 0.0545 0.1701 0.3444 0.0004 < 0.0001

11 We considered the possibility that part of the increase in institutional ownership is due to the acquirer absorbing the institutional ownership of
the target. This could potentially be an issue since, as we report in Table 2, deals with synergy forecasts tend to have larger targets (both absolute

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349

In Table 8, acquirer firms that disclose synergy forecasts are sorted into terciles according to the level of the bid Premium to
Synergy ratio. Panel A shows that institutions have significantly higher holdings in acquirers that pay more for targets (the High
Premium to Synergy Tercile). However, they tend to increase their holdings the most for those acquirers that offer lower premiums
relative to the synergies. Between quarters −1 and 0 institutional holdings increase by 2.5% and 1.5% in low premium and high
premium firms respectively. Between quarters −1 and + 4, institutions increase their holdings by 3.1% and 1% in low premium and
high premium firms respectively. The differences in the means are significant at the 10% level.

In Panels C and D of Table 8 we report the levels and changes sorted by premium to synergy for hedge funds. In contrast to other
institutions, hedge funds tend to have higher holdings in acquirers that offer lower premiums. But like other institutions, hedge funds
tend to increase their holdings of the low premium acquirers significantly more following the announcement. The change in holdings
from quarter −1 to 0 is 1.1% (an increase of 19% from a base of 5.2%) for the low premium acquirers compared to 0.5% (an increase
of 13% from a base of 3.8%) for the high premium acquirers. The difference is significant at the 5% level.

3.2.2. Multivariate analysis
As we mentioned in the last subsection, synergy forecasts are related to firm characteristics, like the size of the acquirer, which

may also influence the choices of institutional investors. In this section we provide a multivariate analysis that examines how synergy
forecasts influence the portfolio choices of institutional investors. Specifically, Table 9A and 9B report OLS regressions with year

Table 5
Operating performance for low versus high synergy samples.

Years Around Merger Merged Firms (MRGi) Matched Firms (MATi) Difference
(MRGi- MATi) (Abnormal Performance)

Mean Median Mean Median Mean P-value t-
statistics

Median P-value of the Signed Rank
test

Panel A. cash flow return on assets for low synergy
−1 7.86% 7.44% 7.88% 7.85% −0.15% 0.522 −0.44% 0.179
1 8.28% 7.74% 8.05% 8.02% 0.11% 0.696 −0.02% 0.886
2 8.16% 7.72% 8.15% 7.64% 0.04% 0.913 0.13% 0.851
3 7.80% 7.55% 8.36% 7.98% −0.45% 0.235 −0.43% 0.268
Abnormal Performance (MRGi- MATi) Post: Median of years 3,2, and 1 −0.02% 0.931 −0.13% 0.709
Change in Cash flow return = (MRGi- MATi) Post – (MRGi- MATi) Pre 0.09% 0.757 0.15% 0.359

Mean Median Mean Median Mean P-value t-
statistics
Median P-value of the Signed Rank
test

Panel B. Cash Flow Return on Assets for High Synergy
−1 6.21% 7.94% 6.86% 7.71% −0.64% 0.210 0.17% 0.793
1 6.75% 7.54% 6.90% 7.67% −0.12% 0.780 −0.29% 0.543
2 7.59% 8.74% 7.12% 7.58% 0.40% 0.450 0.86% 0.031
3 6.51% 7.75% 7.22% 7.41% −0.78% 0.221 0.36% 0.929
Abnormal Performance (MRGi- MATi) Post: Median of years 3,2, and 1 0.13% 0.770 0.46% 0.169
Change in Cash flow return = (MRGi- MATi) Post – (MRGi- MATi) Pre 1.14% 0.030 0.32% 0.064
Difference of Postmerger Abnormal CF Low minus High −0.15% 0.769 −0.59% 0.212
Difference of Change in CF LOW minus HIGH −1.04% 0.084 −0.17% 0.264

The table presents operating performance measured by cash flow return on assets relative to matched firms. Abnormal operating performance is the
operating performance for the firm minus the value for a matching firm. Firms are matched by SIC code, firm size. Operating performance is
measured as a firm’s ratio of operating cash flow to its market value of assets as in Powell and Stark (2005) and Ghosh (2001) and Healy et al.
(1992). OCF is the Operating Cash Flow that is sales minus cost of goods sold, selling and general administrative expenses, and working capital
change and Market Value of Assets is calculated as total book value of assets minus the book value of equity plus the market value of equity. Pro-
forma data of merged firms for pre-acquisition years are created by aggregating acquiring and target firms’ data. Pro-forma data of matched firms are
created by aggregating the data of the two matched samples of firms. The tests of significance are conducted using T-statistics for mean values and
signed-rank tests for median values. Panel A contains the results for the Low Synergy sub-sample, while Panel B contains the results for the High
Synergy sub-sample. We also report the difference in the operating performance between the Low and High Synergy sub-samples at the bottom of
the table.

(footnote continued)
and relative size), more institutional holdings and are more likely to use equity as the method of payment. This is not an issue for the change in
institutional ownership from quarter −1 to quarter 0, since the mergers have not yet been consummated in the announcement quarter. In our
sample, the average period between the announcement of an acquisition and its completion (when the actual exchange of shares actually takes
place) is around 5 months (0.395 years), so there is a potential effect in later quarters, but given that in most cases the target is much smaller than
the acquirer, the effect is likely to be small.

A. Ismail, et al. Journal of Corporate Finance 56 (2019) 343–363
350

fixed-effect that explain the change in holdings from the quarter prior to the acquisition announcement quarter. In the Panel A
regressions the dependent variable is the change in total institutional holdings; whereby in Models 1 and 3 it is the change in holding
from quarter −1 to quarter 0 (ΔIO (−1,0)) while in Models 2 and 4 the dependent variable is the change in holding up until quarter
1. In Panel B the dependent variables are changes in hedge fund holdings until quarter 0 (Models 1 and 3) and until quarter 1 (Model
2 and 4). The results reported in the two panels are qualitatively very similar.

The main independent variables in these regressions are two variables that measure the relative magnitude of the forecasted
synergy. The forecasted synergy scaled by the acquirer’s equity value (Synergy/Acq.Eq.) and the premium offered to the target scaled
by forecasted synergy (Premium-to-Synergy). The other independent variables include dummies for whether or not the deal is hostile
(that take the value 1 if the deal is hostile), industry relatedness (the deal is between firms that share the same two-digit SIC code),
share fraction in the method of payment. Other independent variables include the natural logarithm of the deal value (Ln(Deal)), and
the acquirer CAR (−1,+1) and market and accounting ratios of acquirers including the acquirer stock liquidity, the Debt-to-Assets
MV, OCF-to-Assets MV, the Tobin’s q ratio and Total Ownership by Institutional Block Holders.

The regression estimates, which are consistent with Tables 7 and 8, indicate that hedge funds and institutional investors tend to be
attracted to higher forecasted synergies and increase their holdings of acquirer firms that pay less relative to the estimated synergy.
Namely, the results in Panel A show that the coefficient of the Synergy/Acq.Eq. is positive and significant at the 10% (1%) level in
Model 1 (2). Specifically, a one standard deviation of forecasted percentage synergy causes the total institutional holding to increase
by 0.17% (0.45%) from quarter −1 to quarter 0 (quarter +1) relative to the acquisition announcement quarter. On ther other hand,
the coefficient of the Premium-to-Synergy is negative and significant at the 10% and 5% levels in Models 3 & 4 repectively, implying
that institutional investors are attracted more to underpaying acquirers.

We report in Panel B similar OLS regressions with the dependent variables being the change in hedge fund holdings from quarter
−1 to quarter 0 in Models 1 & 3 and to quarter +1 in Models 2& 4. Consistent with our univariate results, the change in hedge fund
holdings around the merger announcement is positively related to the synergy percentage. Hedge funds tend to increase their

Table 6
Institutional Holding for Firms with and without Synergy Forecasts: The table presents statistics in two panels; Panel A presents the level of
total institutional ownership in various quarters relative to the merger announcement quarter for US. acquiring firms that disclosed synergy
forecasts (Forecast sample) and those that did not (No-Forecast sample). Panel B presents the change in Institutional ownership holding between
quarters. Panels C and D replicates panels A and B for hedge funds respectively. The merger sample is for US completed acquisitions that were
announced between 1990 and 2013 where the merger parties are both publicly listed in the US market.

Panel A −2 −1 0 1 2 3 4

All institutional holding
Forecast 0.673 0.680 0.703 0.694 0.691 0.687 0.689
No-Forecast 0.525 0.526 0.537 0.536 0.536 0.534 0.532
P-value of Difference in Mean 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

Panel B −1 to 0 −1 to 1 −1 to 2 −1 to 3 −1 to 4 0 to 1 0 to 2 0 to 3 0 to 4

All institutional holding
Forecast 0.021*** 0.021*** 0.018*** 0.015*** 0.019** 0.001 −0.002 −0.006* −0.005***
No-Forecast 0.014*** 0.016*** 0.019*** 0.022*** 0.021** 0.002** 0.005*** 0.007*** 0.006***
P-value of Difference in Mean 0.005 0.1713 0.9238 0.1487 0.5722 0.6185 0.0647 0.0018 0.0114

Panel C −2 −1 0 1 2 3 4

Hedge Funds holding
Forecast 0.047 0.047 0.055 0.057 0.059 0.059 0.06
No-Forecast 0.030 0.030 0.033 0.034 0.034 0.035 0.035
P-value of Difference in Mean 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

Panel D −1 to 0 −1 to 1 −1 to 2 −1 to 3 −1 to 4 0 to 1 0 to 2 0 to 3 0 to 4

Hedge Funds holding
Forecast 0.007*** 0.009*** 0.01** 0.011** 0.012** 0.002*** 0.004*** 0.005*** 0.005***
No-Forecast 0.002*** 0.003*** 0.003*** 0.004*** 0.004*** 0.001 0.001* 0.002*** 0.002***
P-value of Difference in Mean 0.0001 0.0001 0.0001 0.0001 0.0001 0.0561 0.0173 0.0129 0.0501

***,**,* denote significance at the 1%, 5% and 10% level, respectively.

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holdings in the acquiring firm more when the disclosed synergies are higher. Similar to the results of the total institutional holdings,
in Models 3 & 4 we find that hendge funds increase their holding in underpaying acquirers as the coefficient on the Premium-to-
Synergy is negative and significant at the 10% (5%) level in Model 3 (Model 4).

Only two of the control variables reliably predict the increase in hedge fund ownership. The first is the share fraction in payment.
The second is the size of the deal. Our theory that hedge funds are more likely to accumulate shares when access to analysts and
management is more valuable provides an explanation for the significant coefficients of these variables if we believe that the larger
deals with mixed financing tend to be the more complicated deals.12

3.3. Synergy forecasts, institutional holdings and stock returns

Up to this point we have established that institutional investors tend to accumulate shares of firms that make acquisition an-
nouncements and that this tendency is especially strong for those events where large synergies are forecast. This observation is
consistent with our hypothesis that institutions have an information advantage when firms are involved in acquisitions and that this
advantage is especially important when firms are engaged in deals with larger synergies that are likely to be more complicated.
However, given that synergy forecasts tend to be chosen for endogenous reasons, these results should be interpreted with some
caution. In particular, it is possible that firms announce high synergies to attract the support of institutional investors.

To provide more direct evidence for our information hypothesis we examine the link between changes in institutional holdings

Table 7
Institutional Holding in forecast firms sorted by size terciles of the percentage synergy: The table presents the level (change) of institutional
ownership data in Panel A (Panel B) for US. acquiring firms that disclosed synergy forecasts (Forecast sample) whereby the data is sorted by the
level of percentage synergy Low, medium and high (the percentage synergy is the present value of synergy scaled by the market value of equity of
the acquiring firm). Panels C and D replicates panels A and B for hedge funds respectively.

Panel A −2 −1 0 1 2 3 4

All institutional holdings
Low Synergy 0.718 0.730 0.749 0.741 0.733 0.727 0.737
Medium 0.703 0.701 0.730 0.715 0.708 0.699 0.694
High Synergy 0.589 0.604 0.628 0.629 0.636 0.641 0.641
P-value of Difference in Mean 0.0001 0.0001 0.0001 0.0001 0.0001 0.0002 0.0001

Panel B −1 to 0 −1 to 1 −1 to 2 −1 to 3 −1 to 4 0 to 1 0 to 2 0 to 3 0 to 4

All institutional holdings
Low Synergy 0.016*** 0.008* 0.006 −0.001 0.006 −0.006* −0.011** −0.018*** −0.013***
Medium 0.025*** 0.027*** 0.022*** 0.016** 0.017** 0.005 0.001 −0.006 −0.007
High Synergy 0.025*** 0.033*** 0.028*** 0.033*** 0.036*** 0.006 0.004 0.008 0.006
P-value of Difference in Mean 0.1216 0.0026 0.0261 0.0017 0.0082 0.0587 0.0685 0.0047 0.0564

Panel C −2 −1 0 1 2 3 4

Hedge Fund holdings
Low Synergy 0.038 0.038 0.043 0.045 0.046 0.048 0.05
Medium 0.049 0.048 0.056 0.06 0.06 0.059 0.057
High Synergy 0.057 0.057 0.068 0.069 0.076 0.075 0.077
P-value of Difference in Mean 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

Panel D −1 to 0 −1 to 1 −1 to 2 −1 to 3 −1 to 4 0 to 1 0 to 2 0 to 3 0 to 4

Hedge Fund holdings
Low Synergy 0.004*** 0.006*** 0.007*** 0.008*** 0.011*** 0.002 0.003* 0.005** 0.006**
Medium 0.009*** 0.011*** 0.012*** 0.01*** 0.009*** 0.002* 0.003 0.003 0.002
High Synergy 0.01*** 0.011*** 0.013*** 0.016*** 0.017*** 0.003* 0.005** 0.009*** 0.009***
P-value of Difference in Mean 0.0023 0.0436 0.0601 0.0574 0.1079 0.5382 0.2967 0.2443 0.4049

***,**,* denote significance at the 1%, 5% and 10% level, respectively.

12 We replicate the analyses taking into account Regulation FD and examine whether results differ in the two sub-periods before and after Reg FD.
The results hold after Reg FD, while they are insignificant before Reg FD. We add these tables to the appendix. Moreover, because intangible
information can be more important for growth firms (Daniel and Titman, 2006), so we examine whether we get stronger results for high versus low
book to market firms. Indeed our results hold for acquirers with high book to market ratio, while they are insignificant for acquirers with low book
to market ratio.

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and realized stock returns around these merger announcements. In particular, we measure the stock returns in the quarter following
the acquisition announcement when the changes in institutional holdings are publicly revealed. Our hypothesis is that stock returns
will react favorably when it is revealed that “smart money” has accumulated the acquiring firm’s stock around the announcement
date. Our conjecture is that institutions tend to be “smart” and are likely to be particularly informed around mergers with high
projected synergies.

We start with a two by two independent sort of the stocks of the acquiring firms by whether or not they provide synergy forecasts
and whether or not the change in institutional ownership is above or below the median change. Based on these sorts we form four
equally weighted portfolios and calculate the excess returns of these portfolios using the Fama and French (1993) three factor model.
If institutional investors have no special information (i.e., our null hypothesis) the excess returns of each of the portfolios will be zero.
If, however, institutional investors have access to private information around these announcements (i.e., our alternative hypothesis),
the change in holdings of the institutions will convey information, i.e., the excess returns of the portfolios with the largest increases in
institutional ownership will be positive.

Panel A of Table 10, which reports these regressions, reveal that the change in institutional ownership does in fact convey
information. Acquiring firms that exhibited increases in institutional holdings realize positive excess stock returns and those with
decreases in holdings exhibit negative excess stock returns when the institutional holdings are revealed in the following quarter. This
is the case for both the synergy forecast subsample of acquirers as well as for the subsample that do not offer synergy forecasts.
However, the effect is twice as strong for the sample that provides synergy forecasts, suggesting that the information advantage of
institutional investors is in fact greater for acquisitions that are likely to be more complicated.

Table 8
Institutional Holding in forecast firms sorted by size terciles of the premium to synergy ratio: The table presents the level (change) of
institutional ownership data in Panel A (Panel B) for U.S. acquiring firms that disclosed synergy forecasts (Forecast sample) whereby the data is
sorted by the level of premium to synergy ratio Low, medium and high (the percentage synergy is the present value of synergy scaled by the market
value of equity of the acquiring firm). The premium used is the Final Offer Premium relative to day −40, that is (Final Offer price / P−40) -1. Panels C
and D replicates panels A and B for hedge funds respectively.

Panel A −2 −1 0 1 2 3 4

All institutional holding
Low Premium to Synergy (Underpaid) 0.619 0.632 0.661 0.660 0.660 0.660 0.661
Medium 0.682 0.683 0.703 0.695 0.691 0.689 0.689
High Premium to Synergy (Overpaid) 0.715 0.726 0.745 0.739 0.728 0.724 0.726
P-value of Difference in Mean 0.0001 0.0001 0.0006 0.0009 0.0015 0.0028 0.0025

Panel B −1 to 0 −1 to 1 −1 to 2 −1 to 3 −1 to 4 0 to 1 0 to 2 0 to 3 0 to 4

All institutional holding
Low Premium to Synergy (Underpaid) 0.025*** 0.029*** 0.026*** 0.025*** 0.031*** 0.0002 −0.0003 −0.004 0.0002
Medium 0.024*** 0.026*** 0.018** 0.012 0.015* 0.004 −0.001 −0.006 −0.008
High Premium to Synergy (Overpaid) 0.015*** 0.011** 0.008* 0.006 0.01 −0.002 −0.008* −0.011** −0.008
P-value of Difference in Mean 0.0818 0.0368 0.0627 0.0728 0.0575 0.7218 0.

361

6 0.4139 0.4168

Panel C −2 −1 0 1 2 3 4

Hedge Funds holding
Low Premium to Synergy (Underpaid) 0.050 0.052 0.062 0.066 0.066 0.067 0.069
Medium 0.053 0.052 0.060 0.061 0.065 0.063 0.062
High Premium to Synergy (Overpaid) 0.039 0.038 0.044 0.046 0.046 0.047 0.049
P-value of Difference in Mean 0.0159 0.0028 0.0007 0.0009 0.0008 0.0011 0.0019

Panel D −1 to 0 −1 to 1 −1 to 2 −1 to 3 −1 to 4 0 to 1 0 to 2 0 to 3 0 to 4

Hedge Funds holding
Low Premium to Synergy (Underpaid) 0.010*** 0.012** 0.011** 0.014** 0.016** 0.003* 0.004* 0.007** 0.007**
Medium 0.008*** 0.009*** 0.013** 0.011** 0.01*** 0.001 0.004** 0.003 0.003
High Premium to Synergy (Overpaid) 0.005*** 0.006*** 0.007*** 0.009*** 0.011** 0.002* 0.003* 0.005*** 0.005**
P-value of Difference in Mean 0.0225 0.0271 0.2526 0.1696 0.2435 0.6546 0.7004 0.5629 0.5582

***,**,* denote significance at the 1%, 5% and 10% level, respectively.
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353

Panel B considers these same regressions but instead of sorting the stocks into portfolios by the amount that total institutional
holdings increases, we sort by changes in hedge fund holdings. Our evidence on sorts based on changes in hedge fund ownership is
consistent with the results on changes in total institutional ownership, but the results are weaker. This may be due to the fact that our
hedge fund sample is much smaller, so the results using hedge fund ownership may have less power. In addition, it should be noted
that hedge funds may be realizing profits from taking short positions that they do not disclose.

Table 11 Panels A and B examine the subsample of acquisitions that include synergy forecasts. The regressions are essentially the
same as those estimated in Table 10 Panels A and B, however, rather than sorting on whether or not the acquirer provides a synergy
forecast we sort by whether the synergy forecast is high or low. The excess returns reported in Panel A indicate that the revelation of
the change in institutional holdings has a significant effect on stock returns regardless of whether the synergy forecast is high or low.
The differences between the returns for the low and high synergy forecasts are relatively small and are not statistically significant.
The results are again consistent, but weaker in Panel B that examines sorts based on hedge fund ownership. We find that when the
acquirer discloses high expected synergies the returns tend to be significantly higher when it is disclosed that hedge fund ownership
increases. The evidence in the subsample with low disclosed synergies is consistent, but not statistically significant.

In unreported regressions we examine the returns of these portfolios beyond the three months holding period. Consistent with a
relatively efficient market, the excess returns for these longer holding periods are relatively modest and are generally not statistically
significant.

Table 9A
Does Synergy or Over/Underpayment explain the change in Total Institutional Holdings around mergers?

This table presents OLS regressions that explain Changes in Total Institutional Holdings during the quarter the merger is announced. The
dependent variable is the Change in Total Institutional Holdings from quarter −1 to quarter 0 (or + 1) relative to the merger announcement
quarter. The independent variables include total synergy scaled by the acquirer or Premium-to-Synergy ratio, merger premium, dummies for the
deal attitude, Hostile (that take the value 1 if the deal is hostile), industry relatedness (the deal is between firms that share the same two-digit SIC
code), share fraction in the method of payment. Other independent variables include the natural logarithm of the deal value (Ln(Deal)), and the
acquirer CAR (−1,+1) and market and accounting ratios of acquirers including the acquirer stock liquidity, the Debt-to-Assets MV, OCF-to-Assets
MV, the Tobin’s q ratio and Total Ownership by Institutional Block Holders. Standard errors are in parentheses. Variables definitions are in Appendix
A.

Model 1 Model 2 Model 3 Model 4

ΔIO (−1,0) ΔIO (−1,1) ΔIO (−1,0) ΔIO (−1,1)

Intercept 0.00533 0.102 0.0396 0.138*
(0.0595) (0.0811) (0.0596) (0.0806)

Synergy/Acq.Eq 0.00720* 0.0189***
(0.0039) (0.0054)

Premium-to-Synergy −0.00628* −0.0107**
(0.0038) (0.0053)

Premium 0.00509 −0.0057
(0.0095) (0.0131)

Share Fraction in Payment 0.0297*** 0.0207* 0.0217*
(0.0082) (0.0112) (0.0114)

Ln (Deal) 0.00374 0.000848 0.0032 0.00117
(0.0025) (0.0034) (0.0025) (0.0034)

Hostile −0.00398 0.000169 0.00174 0.00332
(0.0223) (0.0304) (0.0227) (0.0306)

Industry-Related −0.00033 −0.00723 0.00204 −0.0072
(0.0064) (0.0088) (0.0065) (0.0089)

Tobin’s q −0.00625* −0.0009 −0.00463 −0.00088
(0.0032) (0.0045) (0.0033) (0.0045)

Debt-to-Assets MV −0.0275 −0.0249 −0.023 −0.0173
(0.0215) (0.0293) (0.0218) (0.0295)

OCF-to-Assets MV −0.0032 0.127 −0.0155 0.117
(0.0734) (0.1030) (0.0749) (0.1040)

CAR (−1,+1) −0.0403 −0.014 −0.0601 −0.00032
(0.0415) (0.0575) (0.0415) (0.0579)

Stock Liquidity −29.28* −14.16 −20.82 −2.492
(15.4100) (24.7000) (15.4900) (24.6200)

Block-holding −0.0593** −0.0937*** −0.0577** −0.0885**
(0.0254) (0.0350) (0.0259) (0.0353)

Year Fixed Effect YES YES YES YES
N 383 375 383 375
adj. R-sq 0.0400 0.0300 0.0010 0.0100

***,**,* denote significance at the 1%, 5% and 10% level, respectively.
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354

4. Conclusion

Institutional investors tend to have better access to both corporate executives and sell side analysts than other investors, and may
thus be better positioned to access and interpret firm specific information. We conjecture that this information advantage is especially
important when firms are making significant acquisitions. If this is the case, then one might expect to see institutional investors
accumulate the shares of firms when they are making acquisitions. Our evidence indicates that this is indeed the case. We also find
that when the trades of these investors are made public, the stock prices of the acquiring firms that they accumulate increase, and
consistent with the idea that access to management is more important in acquisitions with higher synergies, the magnitude of the
increase is higher when higher synergies are disclosed.

Our evidence is consistent with the idea that some institutions, e.g., hedge funds, have better access to corporate managers than
other institutions. While the distinction between hedge funds and non-hedge funds is of interest, it might be informative to drill
deeper into the characteristics of the institutions that are most likely to exploit the soft information that can be gained from better
access to corporate management. For example, one might look at an institution’s geographic proximity to the acquiring or target
firms, or alternatively, to common school ties between the portfolio managers and the corporate managers that are involved in the
acquisitions. Alternatively, one might look more carefully at characteristics of funds that are likely to have better access to the
relevant managers. Perhaps, for example, investors that owned the stock of either the acquirer or the target are better positioned to
benefit from soft information about the acquisition. While these questions are beyond the scope of this study, they do suggest
interesting avenues for future research.

Table 9B
Do Synergies and Over/Underpayment explain Changes in Hedge Fund Holdings around merger announcements?

This table presents OLS regressions that explain Changes in Hedge Fund Holdings during the quarter the merger is announced. The dependent
variable is the Change in hedge funds holdings from quarter −1 to quarter 0 (or + 1) relative to the merger announcement quarter. The
independent variables include total synergy scaled by the acquirer or Premium-to-Synergy ratio, merger premium, dummies for the deal attitude,
Hostile (that take the value 1 if the deal is hostile), industry relatedness (the deal is between firms that share the same two-digit SIC code), share
fraction in the method of payment. Other independent variables include the natural logarithm of the deal value (Ln(Deal)), and the acquirer CAR
(−1,+1) and market and accounting ratios of acquirers including the acquirer stock liquidity, the Debt-to-Assets MV, OCF-to-Assets MV, the Tobin’s q
ratio and Total Ownership by Institutional Block Holders. Standard errors are in parentheses. Variables definitions are in Appendix A.

Model 1 Model 2 Model 3 Model 4

ΔHF (−1,0) ΔHF (−1,1) ΔHF (−1,0) ΔHF (−1,1)

Intercept −0.0294 −0.0329 −0.0176 −0.0202
(0.0206) (0.0261) (0.0205) (0.0257)

Synergy/Acq.Eq 0.0036*** 0.0044**
(0.0014) (0.0018)

Premium-to-Synergy −0.0001* −0.0002**
(0.0001) (0.0001)

Premium 0.0021 0.0026
(0.0033) (0.0042)

Share Fraction in Payment 0.0069** 0.0069* 0.0066*
(0.0028) (0.0036) (0.0036)

Ln (Deal) 0.0030*** 0.0036*** 0.0028*** 0.0034***
(0.0009) (0.0011) (0.0009) (0.0011)

Hostile −0.0039 −0.0123 −0.0031 −0.0119
(0.0077) (0.0098) (0.0078) (0.0098)

Industry-Related 0.0023 −0.003 0.003 −0.003
(0.0022) (0.0029) (0.0023) (0.0029)

Tobin’s q −0.0019* −0.0019 −0.0016 −0.0017
(0.0011) (0.0014) (0.0011) (0.0014)

Debt-to-Assets MV −0.0034 −0.0089 −0.0005 −0.0065
(0.0075) (0.0095) (0.0075) (0.0094)

OCF-to-Assets MV 0.0035 0.0307 −0.0028 0.0285
(0.0258) (0.0336) (0.0261) (0.0337)

CAR (−1,+1) −0.0055 −0.0162 −0.009 −0.0121
(0.0144) (0.0186) (0.0144) (0.0186)

Stock Liquidity −5.5258 4.4981 −2.4602 6.9099
(5.3411) (7.9367) (5.3383) (7.8573)

Block-holding 0.013 0.0332*** 0.0131 0.0320***
(0.0089) (0.0114) (0.0090) (0.0115)

Year Fixed Effect YES YES YES YES
N 377 368 377 368
adj. R-sq 0.092 0.069 0.07 0.07

***,**,* denote significance at the 1%, 5% and 10% level, respectively.
A. Ismail, et al. Journal of Corporate Finance 56 (2019) 343–363
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Table 10
Post-event Monthly Abnormal Returns This table presents monthly abnormal returns for below/above median change in holdings (Low/High),
and for Forecast/No-Forecast sub-samples. The monthly abnormal return is calculated using a time-series regression, where the dependent variable
is the equally weighted portfolio return in each calendar month of all bidders within each subgroup that have an event during the 6 or12 months
prior to the measurement month. The independent variables are the Fama and French (1993) factors. The intercept of the time-series regression for
each group is the monthly abnormal return (in percentage). RMRF is the value-weighted market return on all NYSE/AMEX/ NASDAQ firms (RM)
minus the risk-free rate (RF), which is the one-month Treasury bill rate. SMB (small minus big) is the difference each month between the return on
small firms and big firms. HML (high minus low) is the difference each month between the return on a portfolio of high book-to-market stocks and
the return on a portfolio of low book-to-market stocks. Standard Errors are in parentheses.

Panel A ∆IO(−1,0) Rank

Low High Difference

3months 3months 3months

No Forecast Intercept −0.0075*** 0.0047* 0.0122***
(0.0025) (0.0026) (0.0035)

MKTRF 0.9874*** 0.9628*** −0.0246
(0.0599) (0.0618) (0.0834)

SMB 0.4874*** 0.4881*** 0.0007
(0.0790) (0.0815) (0.1100)

HML −0.0668 −0.3122*** −0.2454**
(0.0867) (0.0894) (0.1207)

Adj. R-sqd. 0.6052 0.6158 0.0063
Forecast Intercept −0.0137** 0.0118** 0.0255***

(0.0053) (0.0052) (0.0068)
MKTRF 0.9619*** 1.0889*** 0.1271

(0.1279) (0.1258) (0.1633)
SMB 0.5061*** 0.2112 −0.2949

(0.1734) (0.1705) (0.2213)
HML 0.2942 0.8047*** 0.5105**

(0.1940) (0.1908) (0.2477)
Adj. R-sqd. 0.5038 0.4949 0.0821

Panel B ∆HF(−1,0) Rank

Low High Difference
3months 3months 3months

No Forecast Intercept −0.0014 0.0008 0.0022
(0.0023) (0.0026) (0.0034)

MKTRF 1.0268*** 1.0438*** 0.0169
(0.0546) (0.0607) (0.0803)

SMB 0.4526*** 0.4151*** −0.0374
(0.0743) (0.0827) (0.1094)

HML −0.2256*** −0.1771*** 0.0486
(0.0781) (0.0868) (0.1149)

Adj. R-sqd. 0.6562 0.6010 −0.0091
Forecast Intercept −0.0099* 0.0067 0.0165**

(0.0055) (0.0051) (0.0076)
MKTRF 1.1321*** 1.2550*** 0.1229

(0.1272) (0.1168) (0.1739)
SMB 0.2886** 0.5311*** 0.2425

(0.1454) (0.1336) (0.1988)
HML 0.6420*** 0.7914*** 0.1493

(0.1717) (0.1578) (0.2348)
Adj. R-sqd. 0.4352 0.5560 −0.0079

**

*,**,*Denote significance at the 1%, 5%, and 10% levels, respectively.
***,**,*Denote significance at the 1%, 5%, and 10% levels, respectively.

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356

Table 11
Post-event Monthly Abnormal Returns This table presents monthly abnormal returns for below/above median change in holdings (Low/High),
and for below/above median synergy. The monthly abnormal return is calculated using a time-series regression, where the dependent variable is the
equally weighted portfolio return in each calendar month of all bidders within each subgroup that have an event during the 6 or12 months prior to
the measurement month. The independent variables are the Fama and French (1993) factors. The intercept of the time-series regression for each
group is the monthly abnormal return (in percentage). RMRF is the value-weighted market return on all NYSE/AMEX/ NASDAQ firms (RM) minus
the risk-free rate (RF), which is the one-month Treasury bill rate. SMB (small minus big) is the difference each month between the return on small
firms and big firms. HML (high minus low) is the difference each month between the return on a portfolio of high book-to-market stocks and the
return on a portfolio of low book-to-market stocks. Standard Errors are in parentheses.

Panel A ∆IO(−1,0) Rank

Synergy/Acquirer Equity Low High Difference

3months 3months 3months

Low Intercept −0.0174** 0.0103 0.0277***
(0.0071) (0.0068) (0.0096)

MKTRF 0.8834*** 0.9357*** 0.0523
(0.1725) (0.1643) (0.2307)

SMB 0.4996** 0.2876 −0.2120
(0.2337) (0.2226) (0.3127)

HML 0.4121 0.2888 −0.1233
(0.2616) (0.2491) (0.3499)

Adj. R-sqd. 0.3037 0.3213 −0.0316
High Intercept −0.0123 0.0186** 0.0309***

(0.0092) (0.0095) (0.011)
MKTRF 1.0545*** 1.2997*** 0.2453

(0.2212) (0.2313) (0.2653)
SMB 0.3742 0.0853 −0.2889

(0.2998) (0.3135) (0.3595)
HML 0.1774 1.4169*** 1.2395***

(0.3355) (0.3509) (0.4024)
Adj. R-sqd. 0.2639 0.2984 0.1292

Panel B ∆HF(−1,0) Rank
Synergy/Acquirer Equity Low High Difference
3months 3months 3months

Low Intercept −0.0199*** −0.0045 0.0154
(0.0071) (0.0071) (0.0103)

MKTRF 1.2649*** 1.0325*** −0.2325
(0.1630) (0.1642) (0.2367)

SMB 0.4707*** 0.6258*** 0.1551
(0.1863) (0.1877) (0.2706)

HML 0.7091*** 0.4540** −0.2551
(0.2201) (0.2218) (0.3196)

Adj. R-sqd. 0.3833 0.3339 −0.0096
High Intercept −0.0049 0.0206*** 0.0255**

(0.0076) (0.0081) (0.0112)
MKTRF 1.0792*** 1.3765*** 0.2973

(0.1753) (0.1866) (0.2572)
SMB 0.1156 0.5669*** 0.4513

(0.2005) (0.2133) (0.2940)
HML 0.6421*** 1.2419*** 0.5998*

(0.2368) (0.2519) (0.3473)
Adj. R-sqd. 0.2510 0.3725 0.0144

*,**,*Denote significance at the 1%, 5%, and 10% levels, respectively.
***,**,*Denote significance at the 1%, 5%, and 10% levels, respectively.
A. Ismail, et al. Journal of Corporate Finance 56 (2019) 343–363
357

Acknowledgments

We would like to acknowledge funding from the University Research Board at the American University of Beirut. Moreover, we
would like to thank participants in the 2016 Financial Management Association Meeting, and seminar participants at the University
of Surrey and the University of Cardiff.

Appendices

Appendix A. Variables Definitions

Assets MV This is Market Value of Assets and is defined as liabilities(Item LT) minus balance sheet deferred taxes and investment tax credit (Item
TXDITC) plus Preferred Stock (as defined below) plus Market Equity (Item CSHO*Item PRCC_F).

Book Debt This is Total Assets (Item AT) minus Book Equity
Book Equity This is Total Assets (Item AT) minus liabilities (Item LT) plus balance sheet deferred taxes and investment tax credit (Item TXDITC)

minus Preferred Stock.
Debt-to-Assets MV This is Book Debt over Market Value of assets (as defined above).
Debt-to-Assets BV This is Book Debt over Total Assets (Item AT).
Equity MV Market Equity is calculated as Item CSHO*Item PRCC_F.
Tobin’s Q Market Value or AssetsMV (as defined above) over book value of Total Assets (Item AT).
Premium relative to day

−40
This is final offer Pre run-up premium calculated as [(Final Offer price / P−40) -1]

CAR (−1,+1) CAR (−1, +1) is the 3-day cumulative abnormal returns estimated using the market model over the (−210,-21) interval using the
CRSP value-weighted index returns as the benchmark. The statistical significance of the returns is tested using the Patell (1976) test
corrected for time-series and cross-sectional variation of abnormal returns.

CAR (−2,+2) CAR (−2, +2) is the 5-day cumulative abnormal returns estimated using the market model over the (−210,-21) interval using the
CRSP value-weighted index returns as the benchmark. The statistical significance of the returns is tested using the Patell (1976) test
corrected for time-series and cross-sectional variation of abnormal returns.

OCF-to-AssetsMV Operating Cash flow to MV of Assets Ratio and the Operating cash flow is sales minus cost of goods sold, selling and general
administrative expenses, and working capital change, items (SALE-COGS-XSGA-WCAPCH).

Cash-to-AssetsBV Cash to Book value of Assets ratio item (CHE) over item (AT)
(M/B) Market to Book ratio: Market value of Equity calculated as share price multiplied by number of shares outstanding Divided by Book

value of shareholders equity.
Tobin’s Q Market Value or AssetsMV (as defined above) over book value of Total Assets (Item AT).
Deal value Deal Value is the total consideration paid as reported in SDC
Relative size Target market value of equity Divided by Acquirer market value of Equity
Industry-Related Dummy equal one if the acquisition is between firms with the same two-digit SIC code
Cash Dummy equal one if the Method of payment is Pure Cash
Shares Dummy equal one if the method of payment is Pure share
Mixed Dummy equal one if the Method of payment is a mixed offer of cash, equity and other forms
Share fraction in Payme-

nt
This is the percentage of stock payment in the consideration offered for the target firm, as reported in Thomson Reuters database.

Hostile Acquisition is Hostile as in SDC database
TOEHOLD Is a dummy equal one for deals where the acquirer had at least 5% ownership in the target firm prior to the acquisition
Herfindahl Index Ownership concentration (Herfindahl Index) during quarter −1 relative to the merger announcement quarter. This variable is

collected from 13-F filings
Institutional Ownership Ownership of common stocks by all institutional investors. This variable is collected from 13-F filings
Hedge Fund Ownership Ownership of common stocks by hedge funds. This variable is collected from 13-F filings
Acquirer’s stock illi-

quidity
This variable is calculated as in Amihud, Hameed, Kang & Zhang (2015)

Block-holding Block-holding is the total ownership by institutional block holders in quarter −1 as reported in 13-F filings.

Appendix B. The calculation of merger synergy

In order to calculate the present value of the synergies, we follow a procedure similar to Kaplan and Ruback (1995) and Gilson
et al. (2000), Houston et al. (2001), Ruback (2002), Devos et al. (2009) and Ismail (2011). We collect all merger-related forecasts and
other relevant information such as cost savings, revenue enhancements, and other merger costs, such as restructuring costs and
financial advisors fees. In some cases, the management predictions are comprehensive with well-defined timelines for realizing the
incremental cash flows. However, in most cases the management projections of incremental cash flows are of x dollars by year t and y
dollars by year t + i, where i > 1, we follow the exact procedure in Devos et al. (2009) and Houston et al. (2001) so that we
interpolate the expected cash flows for the intermediate years by assuming that the cash flows increase linearly over those inter-
mediate years. In all cases, we assume that incremental cash flows will be perpetual (will reach a steady state) after the last year of
projection as declared by management. Throughout, we assume a tax rate of 36%.

A. Ismail, et al. Journal of Corporate Finance 56 (2019) 343–363

358

=
+

+
+=

+
+PV Synergies

CF
Ks

CF
Ks Ks

( )
(1 0.36)

(1 )
(1 0.36)

(1 )t i

T
t

t

i T

i T

The annual incremental cash flows from the merger are then discounted back to the announcement day in order to calculate the
present value of the synergies as follows:where i = 1+ (number of days to completion/365). The number of days to completion is the
actual number of days to completion as all deals in my sample are completed deals. The reason for accounting for the time period for
completion is because we are essentially discounting the cash flows back to the announcement date since, in all cases, the cash flows
are forecasted to be generated in future years relative to the completion date not announcement date. The discount rate used to
estimate the present value (Ks) is the weighted average cost of equity capital of the acquirer and the target as determined from the
Capital Asset Pricing Model (CAPM), where the weights are the relative market capitalizations of the two companies’ equity two
months prior to the merger announcement. We use the cost of equity capital to discount cash flows based on the assumption that
these cash flows (cost savings and revenue enhancement) accrue to shareholders only.13 We estimate the CAPM betas from daily data
where we regress firm stock returns against CRSP value weighted returns in the time window from 210 to 21 trading days prior to the
merger announcement. We use a market risk premium of 7.5% p.a., in line with other similar investigations (e.g., Devos et al., 2009;
Houston et al., 2001 who use 7%, and Gilson et al., 2000 who use 7.4%). we use the 10-year U.S. government bond yield for the risk-
free rate. In cases where we obtain a negative beta, we set the beta equal to the average beta in the sample that is 1.036 for acquirers
and 0.975 for targets.

Appendix C. Frequency of synergy disclosure by deal size

The table reports percentage of deals that disclose (Forecast) and those that do not disclose (No Forecast) synergy forecasts by
Year and Deal size in our sample of M&A deals between 1990 and 2013 whereby the sample is divided into three terciles by Deal
value (Small, Medium and Large Deal).

Deal Size Tercile Small Small Medium Medium Large Large

Forecast NO YES NO YES NO YES
1990 100 0 100 0 100 0
1991 100 0 100 0 100 0
1992 100 0 100 0 100 0
1993 100 0 100 0 86.67 13.33
1994 100 0 97.96 2.04 69.57 30.43
1995 98.7 1.30 95.52 4.48 86.11 13.89
1996 95.89 4.11 97.59 2.41 71.74 28.26
1997 96.55 3.45 89.25 10.75 70.00 30.00
1998 95.05 4.95 95.41 4.59 57.30 42.70
1999 95.38 4.62 92.63 7.37 80.56 19.44
2000 95.08 4.92 92.75 7.25 68.22 31.78
2001 92.94 7.06 91.84 8.16 55.77 44.23
2002 95.74 4.26 86.36 13.64 54.55 45.45
2003 82.93 17.07 74.42 25.58 57.69 42.31
2004 93.1 6.90 81.08 18.92 20.51 79.49
2005 88.00 12.00 71.05 28.95 51.85 48.15
2006 96.15 3.85 66.67 33.33 49.18 50.82
2007 100 0.00 76.47 23.53 35.48 64.52
2008 78.79 21.21 78.26 21.74 51.85 48.15
2009 88.89 11.11 61.11 38.89 47.22 52.78
2010 81.25 18.75 83.33 16.67 59.46 40.54
2011 91.67 8.33 54.55 45.45 28.00 72.00
2012 83.33 16.67 47.37 52.63 47.22 52.78
2013 100 0.00 37.50 62.50 29.03 70.97
Total 94.79% 5.21% 87.36% 12.64% 59.27% 40.73%

13 The use of the cost of equity capital for cash flow discounting is also similar to the procedure used in Houston et al. (2001). Moreover, this is also
consistent with the procedure followed by Weston et al. (2001) in the valuation of ConAgra where in Table 9.15 they show that the hypothetical
increase in revenues results in a higher valuation for the equity of ConAgra.

A. Ismail, et al. Journal of Corporate Finance 56 (2019) 343–363
359

Appendix D. Analyses subject to regulation fair disclosure (Reg FD)

Table A
Does Synergy or Over/Underpayment explain the change in Total Institutional Holdings around mergers?

This table presents OLS regressions that explain Changes in Total Institutional Holdings during the quarter the merger is announced, AFTER
Regulation Fair Disclosure of October 2000. The dependent variable is the Change in Total Institutional Holdings from quarter −1 to quarter 0
(or + 1) relative to the merger announcement quarter. The independent variables include total synergy scaled by the acquirer or Premium-to-
Synergy ratio, merger premium, dummies for the deal attitude, Hostile (that take the value 1 if the deal is hostile), industry relatedness (the deal is
between firms that share the same two-digit SIC code), share fraction in the method of payment. Other independent variables include the natural
logarithm of the deal value (Ln(Deal)), and the acquirer CAR (−1,+1) and market and accounting ratios of acquirers including the acquirer stock
liquidity, the Debt-to-Assets MV, OCF-to-Assets MV, the Tobin’s q ratio and Total Ownership by Institutional Block Holders. Standard errors are in
parentheses. Variables definitions are in Appendix A.

Model 1 Model 2 Model 3 Model 4
ΔIO (−1,0) ΔIO (−1,1) ΔIO (−1,0) ΔIO (−1,1)

Intercept −0.0185 −0.032 0.0163 −0.00518
(0.0327) (0.0446) (0.0330) (0.0442)

Synergy/Acq.Eq 0.00803* 0.0189***
(0.0044) (0.0059)

Premium-to-Synergy −0.00779* −0.00974
(0.0044) (0.0060)

Premium 0.0122 0.00167
(0.0113) (0.0156)

Share Fraction in Payment 0.0374*** 0.0273** 0.0298**
(0.0093) (0.0126) (0.0128)

Ln (Deal 0.00490* 0.00138 0.00366 0.00145
(0.0029) (0.0039) (0.0030) (0.0040)

Hostile −0.0198 −0.000961 −0.0131 0.00943
(0.0286) (0.0385) (0.0293) (0.0387)

Industry-Related −0.00166 −0.0047 0.00201 −0.00425
(0.0074) (0.0100) (0.0076) (0.0101)

Tobin’s q −0.00414 0.0071 −0.00385 0.00644
(0.0039) (0.0054) (0.0040) (0.0054)

Debt-to-Assets MV −0.0236 −0.0237 −0.0196 −0.0209
(0.0226) (0.0306) (0.0235) (0.0312)

OCF-to-Assets MV 0.0194 0.236** 0.00256 0.221**
(0.0785) (0.1090) (0.0813) (0.1110)

CAR (−1,+1) −0.03 −0.0821 −0.0638 −0.0712
(0.0472) (0.0645) (0.0478) (0.0652)

Stock Liquidity −31.14** −24.8 −22.05 −14.69
(15.1700) (24.0400) (15.5100) (24.0800)

Block-holding −0.0690** −0.102*** −0.0623** −0.103***
(0.0276) (0.0376) (0.0285) (0.0381)

Year Fixed Effect YES YES YES YES
N 283 279 283 279
adj. R-sq 0.079 0.061 0.013 0.037

***,**,* denote significance at the 1%, 5% and 10% level, respectively.

Table B
Do Synergies and Over/Underpayment explain Changes in Hedge Fund Holdings around merger announcements?

This table presents OLS regressions that explain Changes in Hedge Fund Holdings during the quarter the merger is announced AFTER Regulation
Fair Disclosure of October 2000. The dependent variable is the Change in hedge funds holdings from quarter −1 to quarter 0 (or + 1) relative to the
merger announcement quarter. The independent variables include total synergy scaled by the acquirer or Premium-to-Synergy ratio, merger pre-
mium, dummies for the deal attitude, Hostile (that take the value 1 if the deal is hostile), industry relatedness (the deal is between firms that share
the same two-digit SIC code), share fraction in the method of payment. Other independent variables include the natural logarithm of the deal value
(Ln(Deal)), and the acquirer CAR (−1,+1) and market and accounting ratios of acquirers including the acquirer stock liquidity, the Debt-to-Assets
MV, OCF-to-Assets MV, the Tobin’s q ratio and Total Ownership by Institutional Block Holders. Standard errors are in parentheses. Variables defi-
nitions are in Appendix A.

Model 1 Model 2 Model 3 Model 4
ΔHF (−1,0) ΔHF (−1,1) ΔHF (−1,0) ΔHF (−1,1)
Intercept −0.0294 −0.0329 −0.0176 −0.0202
(0.0206) (0.0261) (0.0205) (0.0257)

Synergy/Acq.Eq −0.0319** −0.0386** −0.0209* −0.0276*

(continued on next page)

A. Ismail, et al. Journal of Corporate Finance 56 (2019) 343–363
360

Table B (continued)

Model 1 Model 2 Model 3 Model 4
ΔHF (−1,0) ΔHF (−1,1) ΔHF (−1,0) ΔHF (−1,1)

(0.0124) (0.0161) (0.0124) (0.0159)
Premium-to-Synergy 0.00397** 0.00505**

(0.0017) (0.0021)
Premium −0.00189 −0.00315

(0.0017) (0.0021)
Share Fraction in Payment 0.00482 0.00525

(0.0043) (0.0056)
Ln (Deal) 0.00794** 0.00755* 0.00761*

(0.0036) (0.0046) (0.0046)
Hostile 0.00359*** 0.00444*** 0.00327*** 0.00431***

(0.0011) (0.0014) (0.0011) (0.0014)
Industry-Related −0.00713 −0.0118 −0.00425 −0.00687

(0.0109) (0.0138) (0.0110) (0.0138)
Tobin’s q 0.00259 −0.00359 0.00354 −0.00356

(0.0028) (0.0036) (0.0028) (0.0036)
Debt-to-Assets MV −0.00202 −0.00136 −0.00203 −0.0013

(0.0015) (0.0019) (0.0015) (0.0019)
OCF-to-Assets MV −0.00351 −0.00896 −0.0014 −0.00867

(0.0086) (0.0110) (0.0088) (0.0112)
CAR (−1,+1) 0.0124 0.0389 0.00567 0.0375

(0.0298) (0.0392) (0.0305) (0.0395)
Stock Liquidity −0.00386 −0.0245 −0.0105 −0.0215

(0.0179) (0.0232) (0.0179) (0.0234)
Block-holding −4.86 4.203 −1.646 7.074

(5.7650) (8.6370) (5.8140) (8.6020)
Year Fixed Effect YES YES YES YES
N 283 278 283 278
adj. R-sq 0.098 0.063 0.06 0.05

***,**,* denote significance at the 1%, 5% and 10% level, respectively.

Table C
Does Synergy or Over/Underpayment explain the change in Total Institutional Holdings around mergers?

This table presents OLS regressions that explain Changes in Total Institutional Holdings during the quarter the merger is announced, BEFORE
Regulation Fair Disclosure of October 2000. The dependent variable is the Change in Total Institutional Holdings from quarter −1 to quarter 0
(or + 1) relative to the merger announcement quarter. The independent variables include total synergy scaled by the acquirer or Premium-to-
Synergy ratio, merger premium, dummies for the deal attitude, Hostile (that take the value 1 if the deal is hostile), industry relatedness (the deal is
between firms that share the same two-digit SIC code), share fraction in the method of payment. Other independent variables include the natural
logarithm of the deal value (Ln(Deal)), and the acquirer CAR (−1,+1) and market and accounting ratios of acquirers including the acquirer stock
liquidity, the Debt-to-Assets MV, OCF-to-Assets MV, the Tobin’s q ratio and Total Ownership by Institutional Block Holders. Standard errors are in
parentheses. Variables definitions are in Appendix A.

Model 1 Model 2 Model 3 Model 4
ΔIO (−1,0) ΔIO (−1,1) ΔIO (−1,0) ΔIO (−1,1)

Intercept 0.0266 0.188 0.0197 0.166
(0.0909) (0.1240) (0.0886) (0.1220)

Synergy/Acq.Eq −0.00497 0.018
(0.0116) (0.0158)

Premium-to-Synergy 0.0039 −0.0108
(0.0087) (0.0122)

Premium −0.00344 −0.0214
(0.0199) (0.0271)

Share Fraction in Payment 0.00464 0.0185 0.0161
(0.0193) (0.0267) (0.0266)

Ln (Deal) 0.00652 0.00264 0.0065 0.00456
(0.0062) (0.0084) (0.0059) (0.0082)

Hostile 0.0199 −0.00899 0.0226 −0.00802
(0.0406) (0.0556) (0.0394) (0.0556)

Industry-Related 0.000315 −0.00615 −0.00016 −0.00499
(0.0148) (0.0207) (0.0145) (0.0206)

Tobin’s q −0.00796 −0.0141 −0.00914 −0.0117
(0.0079) (0.0108) (0.0072) (0.0101)

(continued on next page)
A. Ismail, et al. Journal of Corporate Finance 56 (2019) 343–363
361

Table C (continued)

Model 1 Model 2 Model 3 Model 4
ΔIO (−1,0) ΔIO (−1,1) ΔIO (−1,0) ΔIO (−1,1)

Debt-to-Assets MV −0.0586 −0.0333 −0.0705 −0.0157
(0.0651) (0.0884) (0.0612) (0.0848)

OCF-to-Assets MV −0.039 −0.349 −0.0506 −0.27
(0.2200) (0.3080) (0.2090) (0.2980)

CAR (−1,+1) −0.0468 0.197 −0.0657 0.199
(0.0984) (0.1390) (0.0950) (0.1380)

Stock Liquidity 241.9 78.11 244.7 205.3
(209.50) (283.50) (184.00) (254.80)

Block-holding −0.0254 −0.089 −0.0321 −0.0712
(0.0696) (0.0949) (0.0660) (0.0924)

Year Fixed Effect YES YES YES YES
N 100 96 100 96
adj. R-sq −0.13 −0.034 −0.104 −0.037

***,**,* denote significance at the 1%, 5% and 10% level, respectively.

Table D
Do Synergies and Over/Underpayment explain Changes in Hedge Fund Holdings around merger announcements?

This table presents OLS regressions that explain Changes in Hedge Fund Holdings during the quarter the merger is announced Before Regulation
Fair Disclosure of October 2000. The dependent variable is the Change in hedge funds holdings from quarter −1 to quarter 0 (or + 1) relative to the
merger announcement quarter. The independent variables include total synergy scaled by the acquirer or Premium-to-Synergy ratio, merger pre-
mium, dummies for the deal attitude, Hostile (that take the value 1 if the deal is hostile), industry relatedness (the deal is between firms that share
the same two-digit SIC code), share fraction in the method of payment. Other independent variables include the natural logarithm of the deal value
(Ln(Deal)), and the acquirer CAR (−1,+1) and market and accounting ratios of acquirers including the acquirer stock liquidity, the Debt-to-Assets
MV, OCF-to-Assets MV, the Tobin’s q ratio and Total Ownership by Institutional Block Holders. Standard errors are in parentheses. Variables defi-
nitions are in Appendix A.

Model 1 Model 2 Model 3 Model 4
ΔHF (−1,0) ΔHF (−1,1) ΔHF (−1,0) ΔHF (−1,1)

Intercept −0.00379 −0.0127 −0.00295 −0.0137
(0.0236) (0.0284) (0.0230) (0.0278)

Synergy/Acq.Eq 0.00254 0.00191
(0.0031) (0.0037)

Premium-to-Synergy −0.00267 −0.00226
(0.0023) (0.0028)

Premium −0.00242 −0.00356
(0.0053) (0.0064)

Share Fraction in Payment 0.00151 0.00325 0.00282
(0.0051) (0.0062) (0.0061)

Ln (Deal) 0.00215 0.00262 0.00231 0.00275
(0.0016) (0.0019) (0.0015) (0.0019)

Hostile 0.0038 −0.0133 0.00419 −0.0132
(0.0104) (0.0126) (0.0101) (0.0125)

Industry-Related 0.000595 0.0000486 0.00101 0.000275
(0.0039) (0.0049) (0.0038) (0.0048)

Tobin’s q −0.00267 −0.00239 −0.00216 −0.00218
(0.0021) (0.0026) (0.0019) (0.0023)

Debt-to-Assets MV −0.0107 −0.00992 −0.00832 −0.00888
(0.0186) (0.0223) (0.0169) (0.0207)

OCF-to-Assets MV −0.0557 −0.00214 −0.0477 0.00486
(0.0601) (0.0750) (0.0576) (0.0723)

CAR (−1,+1) −0.00611 0.0199 −0.0034 0.0208
(0.0256) (0.0321) (0.0246) (0.0314)

Stock Liquidity 2.702 41.2 12.12 49.54
(58.61) (69.91) (52.32) (64.07)

Block-holding 0.0126 0.0207 0.0138 0.0219
(0.0185) (0.0223) (0.0177) (0.0217)

Year Fixed Effect YES YES YES YES
N 94 90 94 90
adj. R-sq −0.057 −0.094 −0.026 −0.078

***,**,* denote significance at the 1%, 5% and 10% level, respectively.
A. Ismail, et al. Journal of Corporate Finance 56 (2019) 343–363

362

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• Smart investments by smart money: Evidence from acquirers’ projected synergies

Introduction
Data description
Empirical results
Announcement returns and post-acquisition cash flows
An analysis of institutional holdings
A univariate analysis
Multivariate analysis
Synergy forecasts, institutional holdings and stock returns
Conclusion
Acknowledgments
Appendices
Variables Definitions
The calculation of merger synergy
Frequency of synergy disclosure by deal size
Analyses subject to regulation fair disclosure (Reg FD)
References

Journal of Corporate Finance

50 (2018) 538–555

Contents lists available at ScienceDirect

Journal of Corporate Finance

Review

journal homepage: www.elsevier.com/locate/jcorpfin

Entrepreneurial finance: Unifying themes and future directions

☆

Douglas Cumming a,⁎, Alexander Peter Groh b
a York University – Schulich School of Business, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada
b EMLYON Business School, EMLYON Research Centre for Entrepreneurial Finance, 23 Avenue Guy de Collongue, Ecully, 69132, France

a r t i c l e i n f o

ference on Entrepreneurial Finance, co-organized by th
owes thanks to the Social Sciences and Humanities Rese
⁎ Corresponding author.

E-mail addresses: dcumming@schulich.yorku.ca, http

https://doi.org/10.1016/j.jcorpfin.2018.01.011
0929-1199/

© 2018 Elsevier B.V. All rights reserved.

a b s t r a c t

Article history:
Received 15 January 2018
Accepted 15 January 2018

We overview the papers of this special issue of the Journal of Corporate Finance and explain
how they fit within the different segments of the entrepreneurial finance literature, including
equity crowdfunding, angel investors, debt, venture capital, and private equity. We point to
the growing importance of different sources of capital for entrepreneurs and emerging research
trends pertinent to academics, practitioners, and policymakers. We explain common questions
and suggest scope in future work for combining segments.

© 2018 Elsevier B.V. All rights reserved.

JEL codes:
G20
G23
G24

Keywords:
Entrepreneurial finance
Equity crowdfunding
Angel Investors
Venture capital
Private equity
IPOs

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 538
2. Google scholar trends in entrepreneurial finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539
3. Discussing recent research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540

3.1. Equity crowdfunding. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540
3.2. Angel finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551
3.3. Debt for entrepreneurs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552
3.4. Venture capital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552

3.4.1. Venture capital and the equity gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552
3.4.2. Venture capital and IPOs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553

3.5. Reporting quality of private equity backed IPOs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553
4. Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554
5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554

☆ We owe thanks to the Editors, Stuart Gillan and Jeff Netter, an anonymous referee, and the seminar participants at the 2016 MAELYSE Research Federation Con-

e University of Lyon and EMLYON Business School for helpful comments and suggestions. Douglas Cumming
arch Council of Canada (435-2012-1725) for financial support.

://ssrn.com/author=75390 (D. Cumming), GROH@em-lyon.com, http://ssrn.com/author=330804 (A.P. Groh).

http://crossmark.crossref.org/dialog/?doi=10.1016/j.jcorpfin.2018.01.011&domain=pdf

https://doi.org/10.1016/j.jcorpfin.2018.01.011

dcumming@schulich.yorku.ca

http://ssrn.com/author=75390

GROH@em-lyon.com

http://ssrn.com/author=330804

https://doi.org/10.1016/j.jcorpfin.2018.01.011

http://www.sciencedirect.com/science/journal/09291199

www.elsevier.com/locate/jcorpfin

539D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

1. Introduction

This special issue of the Journal of Corporate Finance comprises papers that each deal with different sources of capital, including
equity crowdfunding, angel investors, debt, venture capital, and private equity. While the entrepreneurial finance literature tends
to be segmented by different types of finance, the themes and questions addressed across different segments are similar:
(1) What factors affect investment rates and possible gaps in capital for entrepreneurs? (2) Is the source of capital ‘value
added’ in terms of facilitating advice, monitoring, and/or growth? (3) Are there governance problems, such as misreporting infor-
mation? (4) What factors affect investment success including successful exits from illiquid investments?

Although the sources of capital studied differ across the papers, each of these papers addresses at least one of these four re-
search questions. Hornuf and Schwienbacher (this issue) examine the determinants of investment in equity crowdfunding. Signori
and Vismara (this issue) examine exit success in equity crowdfunding. Capizzi, Bonini, Valletta, and Zocchi (this issue) examine
factors that affect business angel investment. Cole and Sokolyk (this issue) examine the role of debt in facilitating entrepreneurial
firm growth. Wilson, Wright, and Kaceer (this issue) provide large sample evidence on entrepreneurial capital gaps and the role
of venture capital. Jeppsson (this issue) studies the effect of VCs on IPO performance. Goktan and Muslu (this issue) analyze gov-
ernance and misreporting among private equity funds and compare listed to non-listed private equity funds. All of these papers
are at the forefront of their research areas and have significantly extended what is known about these topics. As well, the papers
in this special issue examine large datasets that are very hard to assemble. A notable hurdle in doing work on the financing of
private entrepreneurial firms is that data are often not publicly available; hence, there is a disproportionate focus on the analysis
of publicly traded firms. The efforts of the authors here break new ground and provide significant new insights into the under-
standing of how financial markets operate in the financing of entrepreneurs.

Most of the papers in this special issue of the Journal of Corporate Finance were originally presented at the Entrepreneurial Fi-
nance Conference, 8–9 July 2016, organized by Aurelie Sannajust, Peter Wirtz, and Alexander Groh and sponsored by MAELYSE;
the management, economics, and finance research federation of the University of Lyon (UdL) and EMLYON Business School,
France. The motivation for the conference was twofold. First, there is a growing interest in research on entrepreneurial finance
and in the massive differences in the landscape of entrepreneurial finance in different countries. However, there was a lack of
high quality academic conferences on entrepreneurial finance topics and, therefore the venue brought together academics from
around the world and from different disciplines to showcase, discuss, and obtain feedback on their latest work.

Second, there is a substantial private and public-sector interest in entrepreneurial finance, and many countries have a desire to
replicate the success of Silicon Valley (Armour and Cumming, 2006). For example, the European Commission established a
European Strategic Investments Fund in June 2016, which was expected to trigger € 315 billion investments in hopes of creating
over 1.3 million new jobs in young ventures and SMEs. The decision to implement this fund was based on the goal to mimic the
success of the North American capital market for start-ups and SMEs which has proven to help build the currently-most-
important multinational corporations. Policy makers acknowledge that investment shortfalls (in Europe) are caused by both sup-
ply and demand factors and that some European Union countries might not be sufficiently competitive. However, these countries
also cut public spending, and this requires action at the European level to increase the supply of risk capital in Europe. As such,
the conference brought together academics, practitioners, and policymakers to learn and gain insights from one another.

The first conference among a strongly growing entrepreneurial finance scholars network was initiated in 2016 in Lyon. Its suc-
cess and wide appreciation yielded a second entrepreneurial finance conference in 2017 in Ghent, Belgium. A third conference
will be hosted in Milan, Italy, in 2018, and there is hope additional initiatives and locations will follow.

This introduction proceeds as follows. We begin with Section 2, describing Google Scholar trends on research in different areas
of entrepreneurial finance. The papers in this special issue, as well as related work, are discussed in Section 3. Section 4 presents
suggestions for future research. Concluding remarks follow in Section 5.

2. Google scholar trends in entrepreneurial finance

Fig. 1 presents Google Scholar trends for different search terms in entrepreneurial finance for the number of documents that
also refer to specific journals. A notable feature of Fig. 1 is that entrepreneurial finance is an interdisciplinary field that covers
work in finance and entrepreneurship (including entrepreneurship and management journals). The data in Fig. 1 does not reveal
that entrepreneurial finance topics are more likely to appear in specific journals; but, instead, papers that refer to specific topics
also refer to specific journals, with the three most common being the Journal of Finance, the Journal of Financial Economics, and
Management Science. These references, in part, reflect the age of the journal, with more hits to older journals. Also, as documented
by Cumming and Johan (2017), these references also reflect a notable pattern in entrepreneurial finance research: management
and entrepreneurship journals tend to also reference work in finance, while work in finance journals tends not to reference work
in management or entrepreneurship journals. Cumming and Johan (2017) discuss some of the unfortunate consequences of this
type of research segmentation, which includes but is not limited to nontrivial mistakes in prior research that might have been
avoided with a broader reading and understanding of the related literature.1

Fig. 2 presents trends in the interest in different topics in entrepreneurial finance by year. The trends clearly show that IPOs
and venture capital have been the most popular research areas from 2000 to 2016, but interest in these topics has dropped

1 For a blog post on this point and a discussion of the problems in the literature with the relevant references, see https://corpgov.law.harvard.edu/2013/04/11/
measuring-the-effectiveness-of-public-policy-towards-venture-capital/.

Measuring the Effectiveness of Public Policy Towards Venture Capital

Measuring the Effectiveness of Public Policy Towards Venture Capital

Fig. 1. Google scholar hits by topic and journal This figure presents the number of Google Scholar hits for the years 2000–2016 for “Entrepreneurial Finance,” “Venture
Capital,” “Private Equity,” Entrepreneur Debt (not in quotes to capture papers about entrepreneurs and debt), “Trade Credit,” Angel Investor (not in quotes to capture
papers about angel investors), Crowdfunding, and IPOs. JF = Journal of Finance; JFE = Journal of Financial Economics; MS = Management Science; RFS = Review of
Financial Studies; JBV = Journal of Business Venturing; ResPol = Research Policy; SMJ = Strategic Management Journal; AMJ = Academy of Management Journal;
ASQ = Administrative Science Quarterly; JBF = Journal of Banking and Finance; JFQA = Journal of Financial and Quantitative Analysis; ETP = Entrepreneurship The-
ory and Practice; JMS = Journal of Management Studies; JCF = Journal of Corporate Finance; JIBS = Journal of International Business Studies; SEJ = Strategic Entre-
preneurship Journal (started in 2007). A paper in the data appears more than once for each journal that referenced the paper. Source: Cumming and Johan (2017).

540 D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

from 2013 to 2016. By contrast, interest in crowdfunding was essentially non-existent until 2010 and has grown at a remarkable
pace since that time. As such, in the next section, we begin our discussion of the papers in this special issue by introducing the
research issues in equity crowdfunding.

3. Discussing recent research

In this section, we introduce the papers in the special issue and place them in the context of the related literature in equity
crowdfunding (Subsection 3.1), angel finance (Subsection 3.2), debt for entrepreneurs (Subsection 3.3), venture capital
(Subsection 3.4), and private equity (Subsection 3.5). Papers in the special issue and closely related papers are also summarized
in Table 1 with the panels organized in the same order as the subsections herein.

3.1. Equity crowdfunding

Derived from crowdsourcing and microfinance, the term crowdfunding emerged with the development of internet-based
funding. The internet presence of crowdfunding has seen it spread to countries all over the world. Belleflamme et al. (2014) de-
scribe crowdfunding as an entrepreneur’s means of collecting capital from an external source represented by a large community.
Bradford (2012) identifies five subcategories of crowdfunding models, based on the return provided for the capital provider:
(1) donations-based, (2) reward-based, (3) pre-purchase, (4) lending-based, and (5) equity-based crowdfunding. Subcategories
2 and 3 are closely related, and the pre-purchase model is often replaced in terminology by the reward-based model. In its
debt-based form, crowdfunding is sometimes referred to as peer-to-peer lending. In terms of equity-based crowdfunding, multiple
supplementary names have emerged: investment-based or securities-based crowdfunding or crowdinvesting. Crowdfunding has
become an important source of funding for young ventures with return-based equity crowdfunding probably being the most in-
teresting, albeit challenging, category for academic research.

Fig. 2. Google scholar hits by topic and year This figure presents the number of Google Scholar hits for the years 2000–2016 for “Entrepreneurial Finance,” “Venture
Capital,” “Private Equity,” Entrepreneur Debt (not in quotes to capture papers about entrepreneurs and debt), “Trade Credit,” Angel Investor (not in quotes to cap-
ture papers about angel investors), Crowdfunding, and IPOs. Source: Cumming and Johan (2017).

541D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

Empirical papers on crowdfunding are still scarce, because of the lack of data and the relative newness of the financing rela-
tionship. Vulkan et al. (2016) describe the size, the growth, and the geographic distribution of the market. Hervé et al. (2017)
analyze gender effects and aspects of risk aversion of crowd investors. Ahlers et al. (2015) determine factors that affect
crowdfunding success. Vismara (2016) finds that the likelihood of completing a funding campaign is higher if founders have larg-
er social networks, and Vismara (2017) points to the importance of the funding momentum in the beginning of a new campaign:
Early allocations affect the likelihood of reaching the target funding amount. Johan et al. (2017) examine distance in equity
crowdfunding. However, none of the papers addresses the question of how the allocation of newly issued shares affects invest-
ments by the crowd. There are two currently used mechanisms: first come, first served and auctions. The founders’ choice of
the mechanism, which might also be related to the choice of a crowdfunding platform, is expected to influence the funding dy-
namics of a campaign. In a first come, first served environment, one would hypothesize that investors have no incentive to with-
hold their bids, because the bids would not have an effect on the price but would induce the risk of not being served. In an
auction, however, investors would prefer to wait until the expiration period not to disclose additional demand, which would
drive up the price. Both market mechanisms would, therefore, yield different funding dynamics during the campaign. Hornuf
and Schwienbacher (this issue) analyze the impact of the allocation mechanism on the funding dynamics of crowdfunding cam-
paigns. They confirm that when allocations happen on a first come, first served basis, equity crowdfunding dynamics are L shaped.
There is a relatively weak end-of-campaign effect. In auction processes, the funding momentum is U shaped. After strong investor
interest at the beginning of successful auctions, the authors document a downturn first, but then a sharp increase in investor sup-
port at their end. Under a second-price auction mechanism, it might be worthwhile for investors to place their bid only at the end
of a campaign. The reason is that bids could be considered to reveal private information about the target’s value and, thus, attract
additional capital. This would increase the price. In addition, an auction does not break up prematurely when investors have sub-
scribed for all available securities. If bids are sealed, the second-price auction has, therefore, the desired property of a more effi-
cient resource allocation (Vickrey, 1961). However, Hornuf and Schwienbacher (this issue) find that only a smaller proportion of
young ventures receive funding under the second-price auction mechanism. This could be due to the more complex rules of the
auction or due to investors’ selection abilities. They might, indeed, be able to pick only the ventures with better prospects. The
authors leave this question open for future research.

Is the crowd, indeed, able to separate the wheat from the chaff? This is the research question of Signori and Vismara (this
issue). They track 212 young UK ventures through April 2017 that successfully raised equity capital in a crowdfunding campaign
from 2011 to 2015 and determine if they received additional financing in seasoned equity offerings, if they were acquired by in-
cumbents, if they were still active without requiring supplementary financing, or if they ceased operations. Among the seasoned
offerings, the authors further distinguish between follow-on crowdfunding campaigns or private offerings from business angels or
venture capitalists. Signori and Vismara (this issue) find that only 38, or 17.0%, of their sample companies failed. This is a low
failure rate compared to the 56% reported by a UK business angels’ network. Among their sample, 74, or 34.9%, of the ventures
raised additional capital in seasoned offerings. Thereof, 54 went through another crowdfunding campaign, while 20 received cap-
ital privately. Only 3 ventures, or 1.4%, were acquired in an M&A transaction. All others were still active and operating. The paper
also reports that investors’ participation in the initial round is an important indicator or determinant of the ventures’ success. If

Table 1
Overview of studies on entrepreneurial finance.
This table summarizes select papers that focus on adjacent topics for the papers selected for this special issue. The authors, data sources, countries, time periods, var-
iables, and main findings are summarized. The main findings are largely paraphrased and/or copied from the abstracts of the papers to best and succinctly represent the
authors’ contributions but are not meant to exhaustively represent all of the findings from the papers.

Author(s) Data source(s) Country
samples

Time period Dependent variables Main explanatory
variables

Main findings

Panel A. Equity crowdfunding
Ahlers et al.
(2015).

Australian Small-Scale
Offerings Board
(AASOB)

Australia October 2006 to
October 2011

Number of Investors,
Funding Amount,
Speed of Funding

Human Capital (# Board
% Board MBA), Social
Capital (%
Non-Executive Board),
Intellectual Capital and
Contract Terms (Patent,
Equity Share, Equity
Offering, Financial
Projections, Disclaimers
Financial Forecasts),
Additional Controls (#
Staff, Award,
Government Grant,
Intended Number of
Rounds, Most Likely
Exit-Others, Most Likely
Exit-Trade Sale, Target
Funding, Years in
Business, Years to
Planned Exit)

This paper presents a
first-ever empirical
examination of the
effectiveness of signals
that entrepreneurs use to
induce (small) investors to
commit financial resources
in an equity crowdfunding
context. We examine the
impact of venture quality
(human capital, social
(alliance) capital, and
intellectual capital) and
uncertainty on fundraising
success. Our data highlight
that retaining equity and
providing more detailed
information about risks
can be interpreted as
effective signals and can,
therefore, strongly impact
the probability of funding
success. Social capital and
intellectual capital, by
contrast, have little or no
impact on funding success.
We discuss the
implications for successful
policy design.

Vismara
(2017)

Crowdcube UK 2014 Number of Investors,
Funding Amount,
Success (%)

Early Investors, Public
Profile of Investors,
Social Capital, Target
Capital, Equity Offered,
Voting Rights Threshold,
Tax Incentives, IPO Exit,
Dividends, Duration

Finance studies on
information cascades,
usually in an initial public
offering setting, typically
differentiate between
institutional and retail
investors, as this is the only
information available to
potential backers.
Information available
through equity
crowdfunding platforms
includes details on
individual investors, as
they may disclose
information about
themselves by linking their
profile to social networks
or websites. Using a
sample of 132 equity
offerings on Crowdcube in
2014, we show that
information cascades
among individual investors
play a crucial role in
crowdfunding campaigns.
Investors with a public
profile increase the appeal
of the offer among early
investors, who in turn
attract late investors.

Johan et al.
(2017)

Australian Small Scale
Offerings Board
(AASOB)

Australia 2006 to June
2012

Distance Variables for Human
Capital, Social Capital,
Intellectual Capital and
Contract Terms, and
Other controls used in

This paper presents the
first evidence of the
influence of geographic
distance among retail,
accredited, and overseas

542 D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

Table 1 (continued)

Author(s) Data source(s) Country
samples
Time period Dependent variables Main explanatory
variables
Main findings

Ahlers et al., (2015) investors and venture
location in an equity
crowdfunding context. By
analyzing investment
decisions, we show that
geographic distance is
negatively correlated with
investment probability for
all home country investors.
Our comparison of home
country and overseas
investors reveals that
overseas investors are not
sensitive to distance.
However, when comparing
only home country
investors (subdivided into
retail and accredited), we
document that both
investor types are similarly
sensitive to the distance of
possible ventures.

Signori and
Vismara
(this issue)

Crowdcube, Crunchbase,
and Companies House

UK 2011–2015 Exits (IPOs,
acquisitions, failure)

Dispersed ownership,
speed of target capital,
qualified investors,
firm-specific and time
controls

Based on data from the
UK’s largest crowdfunding
platform, Crowdcube, the
authors show that 18% of
these firms failed, while
35% pursued one or more
seasoned equity offerings
in the form of either
private equity injection
(9%) or follow-on
crowdfunding offerings
(25%), while three firms
were acquired. Among the
determinants of the
post-campaign scenarios,
they find that the degree of
investor participation in
the initial offering plays a
relevant role. In particular,
firms with more dispersed
ownership are less likely to
issue further equity, while
those that reach the target
capital more quickly are
more likely to launch a
follow-on offering. Further,
none of the companies
initially backed by
qualified investors
subsequently failed.

Hornuf and
Schwienbac-
her (this
issue)

Four equity
crowdfunding portals
(Companisto, United
Equity, Seedmatch, and
Innovestment)
comprising 89 funding
campaigns, which were
run by 81 startups

Germany November 6,
2011–August
28, 2014.

The number of
investments made
by crowd investors
on day t in a
particular campaign
i.

Information disclosure
variables, Peer effect
variables,
End-of-campaign
variables, Collective
attention variables,
Control variables

Equity crowdfunding is a
new form of
entrepreneurial finance, in
which investors do not
receive perks or engage in
pre-purchase of the
product but rather
participate in the future
cash flows of a firm. In this
paper, we analyze what
determines individual
investment decisions in
this new financial market.
One important factor that
may influence the behavior
of investors is the way the
portal allocates securities.

(continued on next page)

543D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

Table 1 (continued)
Author(s) Data source(s) Country
samples
Time period Dependent variables Main explanatory
variables
Main findings

We use unique data from
four German equity
crowdfunding portals to
examine how the
allocation mechanism
affects funding dynamics.
In contrast with the
crowdfunding campaigns
on Kickstarter, on which
the typical pattern of
project support is U
shaped, we find that equity
crowdfunding dynamics
are L-shaped under a first
come, first served
mechanism and U-shaped
under a second-price
auction. The evidence also
shows that investors base
their decisions on
information provided by
the entrepreneur in the
form of updates as well as
by the investment
behavior and comments of
other crowd investors.

Panel B. Angel finance
Cumming and
Zhang
(2014)

Pitchbook Over 5000
angel
investments
from 96
countries

1977 to 2012 Angel versus VC and
PE Investment, Exit
Outcomes

Legal Conditions,
Cultural Conditions,
Market Conditions, as
well as Investor,
Entrepreneur and
Deal-Specific
Characteristics

The theory and evidence
indicate that
disintermediated
individual angel
investments are more
affected by legal,
economic, and Hofstede’s
cultural conditions than
intermediated VC and PE
investments. The data
further indicate that
investee firms funded by
angels are less likely to
successfully exit. These
findings are robust to
propensity score matching
methods, as well as
clustering standard errors
and excluding U.S.
observations, among other
approaches.

Lerner et al.
(2015)

Self-Collected 295 angel
investments
from 13 angel
groups in 21
countries

February –
October 2014

Dummy variable:
venture received
funding from angel
group

Venture is above the
funding cut-off

When comparing
entrepreneurial applicants
in angel networks just
above and below the
funding cutoff, angel
investors have a positive
impact on the growth,
performance, and survival
of firms as well as their
follow-on fundraising.

Capizzi et al.,
this issue

Italian federation of
business angel
associations (IBAN,
www.iban.it)

810
angel or
angel-group
backed
investments in
619 companies
by 330 unique
business
angels in Italy

2008 to 2014 % Wealth Invested,
Investment
Participation

BAN Membership,
Co-Investors,
Monitoring, Age,
Education, Wealth,
Experience,
Entrepreneur, Manager,
Net Asset Value, Seed,
Foreign, Industry
Market/Book, Capital
Intensity, Industry and
Year Fixed Effects

Membership in a business
angel network (BAN)
affects the investment
decisions of the members.
Using a novel dataset
containing qualitative and
quantitative information
on 810 angel or
angel-group backed
investments in 619
companies by 330 unique

544 D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

home

Table 1 (continued)
Author(s) Data source(s) Country
samples
Time period Dependent variables Main explanatory
variables
Main findings

business angels from 2008
to 2014, we show that BAN
membership generates
valuable information,
networking, monitoring,
and risk reduction effects,
which ultimately affect the
amount of personal capital
committed by each angel
investor and their equity
stake in the targeted
company. These results
extend our knowledge of
the investment behavior
and characteristics of
business angels, a
relatively opaque funding
source that is rapidly
gaining prominence in
support of new ventures
and the development of
the global economy.

Panel C. Private debt finance for entrepreneurial firms
Cosh et al.
(2009)

Centre for Business
Research at the
University of Cambridge

UK 1996–1997 Application for
External Finance,
Rejection or
Acceptance of
Application, Type of
External Finance,
Percentage of
External Finance
Obtained

Firm financial
characteristics, age and
profile of board and
management,
competitors and
industry

This paper investigates
factors that affect rejection
rates in applications for
outside finance among
different types of investors
(banks, venture capital
funds, leasing firms,
factoring firms, trade
customers and suppliers,
partners and working
shareholders, private
individuals, and other
sources), taking into
account the
non-randomness in a
firm’s decision to seek
outside finance. The data
support the traditional
pecking order theory.
Further, the data indicate
that firms seeking capital
are typically able to secure
their requisite financing
from at least one of the
different available sources.
However, external finance
is often not available in the
form that a firm would
like.

Robb and
Robinson
(2014)

Kauffman Firm Surveys US 2004–2007 Capital Structure Firm specific
characteristics

Contrary to many accounts
of startup activity
[although very consistent
with Cosh et al., 2009 for
the UK], the firms in our
data rely heavily on
external debt sources, such
as bank financing and, less
extensively, on friends-
and family-based funding
sources.

Cole et al.
(2016)

Report of Condition and
Income (informally
known as the “Call
Report”), PWC
MoneyTree Report,

US 1995–2011 Growth in firms,
establishments,
payroll, employment

VC, bank finance,
market conditions, state
level institutional
conditions,
demographic variables

We directly compare, for
the first time, banks versus
VCs for stimulating
entrepreneurship and
growth. We examine state

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545D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

Table 1 (continued)
Author(s) Data source(s) Country
samples
Time period Dependent variables Main explanatory
variables
Main findings

Statistics of U.S.
Businesses (SUSB)
dataset constructed by
the U.S. Census Bureau,
U.S. Bureau of Economic
Analysis (BEA), Patent
Technology Monitoring
Team (PTMT) reports

level data to account for
externalities across firms
and control for
endogeneity. We find the
effect of VC to be both
economically and
statistically significant in
stimulating small firm
growth. We do find a
significant effect of banks
in stimulating small firm
growth.

Tykvova
(2017)

Dow Jones Venture
Source, Standard &
Poor’s (S&P) Capital IQ,
Bureau van Dijk Orbis,
National Venture Capital
Association (NVCA),
Thomson ONE, and
various Web sites and
reports.

US 1995–2008 Choice of VC or
Venture Lending,
Exit Performance

Firm specific
characteristics,
demographic and
market Conditions

Early-stage VC investors
that own high-quality
value companies tend to
signal their quality, and
they frequently turn to
uninformed venture
lending (VL) investors.
Early-stage VC investors
prefer VC, if the proportion
of high-quality companies
in the population is high, if
their companies have a
high upside potential, if
they can benefit from the
value that late-stage VC
investors add, or if
uncertainty is high.
Empirical evidence is
consistent with these
predictions.

Cole and
Sokolyk (this
issue)

Kauffman Firm Surveys.
This annual survey
follows 4928 privately
held firms that were
established in 2004.

US 2004–2012 Use of Debt, Survival,
Revenues

Debt Finance,, Owner
and Firm Characteristics

Start-up firms with better
performance prospects are
more likely to use debt
and, in particular, business
debt. Compared to
all-equity firms, firms
using debt at the initial
year of operations are
significantly more likely to
survive and achieve higher
levels of revenue three
years after the firm’s
startup. However, results
hold for business debt
only. Debt obtained in the
name of the firm is
associated with longer
survival times and higher
revenues, while debt
obtained in the name of
the firm’s owner has no
effect on survival time and
is associated with lower
revenues.

Panel D. Venture capital and capital gaps
Leleux and
Surlemont
(2003)

European Venture Capi-
tal Association

Europe 1990–1996 VC Funds Government VC
Programs, legal system
controls

Large public [government]
participation is correlated
with smaller VC industries,
but analyses do not
support the view that
public venture capitalists
are acting to seed the
industry or that are they
crowd out private funds.
On the contrary, public
involvement seems to
cause greater amounts of

546 D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

Table 1 (continued)
Author(s) Data source(s) Country
samples
Time period Dependent variables Main explanatory
variables
Main findings

money to be invested in
the industry as a whole.
We argue that the effects
of public intervention,
whatever the motives, are
real and probably result
from
demonstrating/sanctioning
the social merit of venture
capital and from signaling
an enduring commitment
to it.

Cumming and
MacIntosh
(2006)

Macdonald & Associates,
Ltd., for the Canadian
Venture Capital
Association
[subsequently acquired
by Thomson SDC]

Canada 1977–2001 Number of VC deals,
Dollar value of VC
deals

Presence of government
legislation for tax
subsidized funds,
market conditions,
provincial and federal
incorporation data

The data indicate
government funds have
crowded out private VC
investment, even so much
so as to lead to a reduction
in the aggregate pool of
venture capital in Canada,
frustrating one of the key
governmental goals under-
lying the government pro-
grams; namely, the
expansion of the aggregate
pool of capital.

Howell (2017) US Department of
Energy’s (DOE) Small
Business Innovation
Research (SBIR)
program.

US 1998–2013 Cite-Weighted
Patents, Venture
Capital Investment

SBIR Awards, various
control variables

This paper conducts the
first large-sample,
quasi-experimental
evaluation of R&D
subsidies using data on
ranked applicants from the
US Department of Energy’s
SBIR grant program. An
early-stage award
approximately doubles the
probability that a firm
receives subsequent
venture capital and has
large, positive impacts on
patenting and revenue.
These effects are stronger
for more financially
constrained firms.
Certification, where the
award contains
information about firm
quality, likely does not
explain the grant effect.
Instead, the grants are
useful because they fund
technology prototyping.

Wilson et al.
(this issue)

Net Lending Growth and
Real Interest rate – Bank
of England; GDP growth
and GDP deflator –
Federal Reserve Bank of
St. Louise; HTKI
company, HTKI
industry – indicators
generated using the
Eurostat categorization
of companies on the
basis of two-digit NACE
codes; Output Area
Classification – Office of
National Statistics,
matched by postcode;
credit reference
agencies ICC Credit and

UK 2005–2014,
over 12 million
company-years;
2852 VC backed
companies and
4048 deals.

VC financing, VC
amounts/total assets

Firm-specific and
industry-specific
characteristics; the size
of investment and
investments rounds. We
construct variables from
‘event’ filing and
director and
shareholder records that
capture expertise and
resource-combinations
to differentiate target
VC investees from other
companies.

We provide estimates for
the potential size of the
equity gap for all ventures
and for the subpopulation
of the corporate sector
facing later-stage financing
issues, the second equity
gap. This ‘second’ equity
gap relates to a second
so-called ‘valley of death’
in financing the growth
phase, particularly
pertinent for
knowledge-intensive (KI)
firms. Enterprises that are
successful in acquiring
equity investors are able to
overcome informational

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547D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

Table 1 (continued)
Author(s) Data source(s) Country
samples
Time period Dependent variables Main explanatory
variables
Main findings

Creditsafe, asymmetries by
demonstrating,
communicating, and
signaling desirable
attributes to outside
investors. Using propensity
scoring methods and
multivariate models
determining investment
demand, we screen the
corporate population for
potential VC investments
and estimate the size of the
equity gap in total and by
subpopulation (i.e., we
identify the high technolo-
gy and knowledge inten-
sive companies that
potentially face the second
equity gap as a subset of
our total equity gap esti-
mates).

Panel E. Venture capital and IPOs
Lee and Wahal
(2004)

Jay Ritter US 1980–2000 IPO Underpricing VC-backing, market
conditions, firm-specific
controls

Controlling for
endogeneity in the receipt
of venture funding, we find
that venture capital backed
IPOs experience larger
first-day returns than
comparable non-venture
backed IPOs. Between
1980 and 2000, the
average return difference
ranged from 6.20 to 9.51%.
This return difference is
particularly pronounced in
the “bubble” period of
1999–2000. As a potential
explanation for these
results, we explore a
variant of the
grandstanding hypothesis,
in which the publicity
associated with high
first-day returns brings
future commitments of
capital to venture
capitalists. Capital flow
regressions show that
commitments of capital
are positively related to
first-day returns.

Nahata et al.
(2014)

Securities Data
Corporation’s
VentureXpert database
provided by Thomson
Financial

US 1991–2001
(Investments)
and to 2005
(Exits)

Exit outcome (IPO,
M&A), Productivity
(sales/book value of
assets)

VC reputation, market
conditions, firm-specific
variables

Companies backed by
more reputable VCs by
initial public offering (IPO)
capitalization share (based
on cumulative market
capitalization of IPOs
backed by the VC), are
more likely to exit
successfully, access public
markets faster, and have
higher asset productivity
at IPOs. Further tests
suggest VCs’ IPO
capitalization share
effectively captures both
VC screening and
monitoring expertise.

548 D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

Table 1 (continued)
Author(s) Data source(s) Country
samples
Time period Dependent variables Main explanatory
variables
Main findings

Cumming
(2008)

Self-Collected Europe 1995–2002
(Investments)
and to 2005
(Exits)

Exit outcome (IPO,
M&A, Liquidation)

VC contracts, market
conditions, legal
conditions, firm-specific
variables

Consistent with
control-based theories of
financial contracting, the
data indicate that ex ante,
stronger VC control rights
increase the likelihood that
an entrepreneurial firm
will exit by an acquisition,
rather than through a
write-off or an IPO. The
findings are robust to
controls for a variety of
factors, including
endogeneity and cases in
which the VC preplans the
exit at the time of contract
choice.

Jeppsson (this
issue)

Securities Data
Company’s (SDC) New
Issues
Database

US 2003–2016, 311
VC Backed IPOs

VC Investment in
IPO, IPO offer price
revision,
underpricing,
completion of an IPO,
long-run
performance

Issuer and issue
characteristics, VC
characteristics,
investment bank
characteristics, market
conditions

This study finds support
for the certification role by
venture capitalists. Insider
participation in the S-1
filing is associated with
smaller offer price
revisions, shorter duration
to the IPO, positively
associated with the
offering being completed,
and better long-run
aftermarket performance.
Furthermore, the results
indicate that insider
participation is mediated
through offer price
revisions, which, in turn,
are associated with IPO
underpricing. Overall, the
results are robust in both
two-stage least squares
(2SLS) models and
simultaneous eq. (SE)
models. This paper
provides a first step in
understanding the
complex role of venture
capitalists in the initial
public offering process and
contrasts past research
considering IPOs as exit
events.

Panel F. Private equity and reporting
Cumming and
Walz (2010)

Center for Private Equity
Research (CEPRES,
Germany)

5038 Venture
Capital and
Private Equity
Backed
Companies in
39 Countries

1971–2003 Severity of
Misreporting of
Unexited Venture
Capital and Private
Equity Returns

Country Level Legal and
Accounting Standards,
Various Venture Capital
and Private Equity
Governance Proxies

Unexited venture capital
and private equity returns
are severely over-reported.
The severity of
over-reporting is more
pronounced in countries
with worse legal and
accounting standards,
worse contractual
governance, and among
first-time fund managers
that have pronounced
incentives to over-report
for fundraising reasons.

Johan and
Zhang
(2014)

Pitchbook 5068 PE funds
from 44
countries

2000–2012 The difference
between reported
returns to
institutional

Type of institutional
investor, legal
conditions, market
conditions, firm-specific

We show that
endowments are
systematically associated
with less pronounced

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549D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

Table 1 (continued)
Author(s) Data source(s) Country
samples
Time period Dependent variables Main explanatory
variables
Main findings

investors and
subsequently
realized returns

variables differences between
unrealized returns and
subsequently realized
returns. Moreover,
endowments receive more
frequent reports from their
PE funds, implying more
stringent governance. We
find that higher reporting
frequencies from PE funds
are correlated with a lower
tendency for the limited
partners to receive
overstated performance
reports. These findings
persist after controlling for
stock market conditions,
legal environments and
origins, fund and GP
characteristics, PE fund
types, as well as cultural
dimensions.

Jenkinson
et al. (2016)

Burgiss US, 645 funds 1988–2014 Future discounted
cash flows (DCFs)

Private equity
valuations, control
variables

Reported NAVs converge
on the future DCF early in
the life of the fund. This
result is particularly
interesting to investors for
whom unbiased asset
valuations are important in
keeping portfolios
optimally allocated. In
addition, findings indicate
that although NAVs
generally are more
conservative in the first
half of the sample period,
NAVs for venture capital
funds tend to overstate
economic value after 1999.
Findings from additional
tests suggest that the
overstatement is
attributable to the effects
of the financial crisis, and
that VC fund managers fail
to update NAV estimates in
post-crisis years to reflect
the effects of the crisis on
future cash flows.

Goktan and
Muslu (this
issue)

Securities Data
Company (SDC) Venture
Xpert database,
Compustat North
America and Compustat
Global databases.

Worldwide 1990–2009 Abnormal Accruals,
Timeliness of Loss
Recognition, 1-year
buy-and-hold
abnormal returns

Firm specific variables,
transaction specific
variables, market
conditions, industry
conditions, legal
conditions

Private equity firms that
are listed on stock
exchanges commit to
extensive public
disclosures. By contrast,
unlisted private equity
firms communicate
privately with partner
investors. We examine the
reporting quality of
portfolio companies that
are backed by listed and
unlisted private equity
firms worldwide. We find
that portfolio companies
that are backed by listed
private equity firms report
lower abnormal accruals,
recognize losses faster, and
experience higher post-IPO
stock returns. These

550 D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

Table 1 (continued)
Author(s) Data source(s) Country
samples
Time period Dependent variables Main explanatory
variables
Main findings

findings are stronger for
smaller and European
portfolio companies and
those that receive direct
private equity invest-
ments. Overall, our find-
ings suggest that the public
reporting model of listed
private equity firms leads
to greater capital market
benefits than the private
reporting model of unlist-
ed private equity firms.

551D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

the ownership of the young venture is widely dispersed, then a successful outcome (i.e., a seasoned equity offering or an M&A) is
less likely. This could be related to inferior monitoring abilities of a larger crowd (Brennan and Franks, 1997) but also to weaker
selection skills. The authors also demonstrate that “qualified” or “sophisticated” investors eventually have better selection and/or
monitoring skills. These investors also tend to invest in securities that deliver voting rights compared to the “retail” investors
(Cumming et al., 2017). Finally, a stronger initial funding dynamic e.g., as discussed in Hornuf and Schwienbacher (this issue)
yields a higher propensity of launching a subsequent crowdfunding round. Signori and Vismara (this issue) are the first to mon-
itor crowdfunded ventures after a successful campaign, which allows deriving important results on their long-term success and
the sustainability of their business models. However, the future development of a large fraction of their sample is yet unknown.
We realize that 97, or 46%, of their sample ventures were still active and operating at the end of the sampling period. Therefore,
we don’t know yet if these are successful businesses or eventual failures. Some of them will need to be re-classified in the close
future, and this will affect the long run crowdfunding success rates.

3.2. Angel finance

Business angels are high net worth individuals who usually invest their own private wealth, mostly between USD 10,000 and
USD 250,000, in ventures that are, typically, local, unlisted, and without a family connection to the business angels. They play
major roles for young ventures, besides supplying capital as they also provide strategic input, monitoring, and control (however,
less formal than institutional investors), as well as adjoining their professional network. They often take positions on the board of
directors and become consultants to the ventures. Business angels may be former entrepreneurs or may at least have had a career
in management contributing their contacts and know-how related to entrepreneurship and management (Aernoudt, 1999; Berger
and Udell, 1998; Bonnet and Wirtz, 2011, 2012; Capizzi, 2015; Ibrahim, 2008; Leavitt, 2005; Politis, 2008; Prowse, 1998;
Wallmeroth et al., 2018; Wetzel, 1983, 1987). Angel investors, however, are found to be a highly heterogeneous community
and also pursue varied processes when investing in start-ups (Freear et al., 1994; Lerner, 1998). Often, they co-invest with ven-
ture capitalists (Bonnet et al., 2013).

Wetzel (1983, 1987) already pointed to the fact that the informal venture capital market is very opaque with numerous puz-
zles. He also highlights that, based on the data at the time, the informal capital market was twice the size of the formal venture
capital market. Prowse (1998) notes that business angels’ activities are significant, adding that, though this market is not trans-
parent, it is found to be heterogeneous and localized.

Business angels often associate in networks, and these networks have recently attracted strong academic interest. Kerr et al.
(2014) assess informal venture capital financing and identify five benefits of angels being network members: (1) Since they com-
bine individual investments, deals generally accumulate larger investment amounts. (2) As a result of this accumulation, angels
are able to diversify and spread their investment risks over more investments. (3) The resulting economies of scale produce
lower due diligence and legal costs. (4) As it is easier for entrepreneurs to find business angels’ networks than individual business
angels, more attractive deal-flow can be produced. Lastly, (5) these networks are more likely to include more experienced angel
investors.

Capizzi, Bonini, Valletta, and Zocchi (this issue) compare the investment choices of Italian business angels who are members of
angel networks to those who are unaffiliated, acting as single and independent investors. In particular, they investigate whether
and how being a member of a semi-formal organization affects the share of the angel’s personal wealth invested or the amount of
the equity stake taken. They gather a unique data set of 810 investments in 619 ventures by 330 business angels from 2008 to
2014 and show that belonging to an angel network has a significant effect on the investment characteristics of business angels.
Network affiliated business angels allocate larger fractions of their wealth to young ventures, and they diversify more at the
same time. The authors also find that being a member of an angel network provides valuable information and superior network-
ing opportunities.

The paper is among the first to analyze the role of networks and associations for the activity and investment characteristics of
business angels. It points to the advantages of such networks for the transaction costs of angels, for their deal flow generation, and

552 D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

for their portfolio diversification. However, it does not answer the question of why other business angels still act without a net-
work affiliation.

There are only a few papers that have considered international differences in angel investing in multiple countries. For exam-
ple, Lerner et al. (2015) provide important regression discontinuity evidence that angel investment enhances differences in
growth, consistent with their earlier work from the U.S. (Kerr et al., 2014). Notably, however, there appears to be issues of smaller
samples that are not completely comparable with those in specific countries, such as Italy, in Capizzi, Bonini, Valletta, and Zocchi
(this issue), which makes use of more extensive, industry-wide data from a single country. The results presented in Capizzi et al.
indicate that evidence from angel groups will not be representative from the broader population of angel investors. Cumming and
Zhang (2014) provide a larger sample from 96 countries and note an apparent pronounced impact of Hofstede cultural traits on
angel investment. It is difficult to assess whether or not these Pitchbook data are perfectly representative; they do offer the ad-
vantage of being one of the largest (if not the largest, at least at this stage) angel investment databases in the world. Other
work that expands the data sources across countries on angel investment has the potential to add significant value to our under-
standing of early-stage finance around the world.

3.3. Debt for entrepreneurs

An overwhelming number of academic papers focuses on entrepreneurial finance as equity finance. It seems obvious that
many disruptive, capital-intensive technologies and business models are not bankable at the given uncertainties and lack of col-
lateral. However, not all start-ups build on disruptive innovations, and important contributions point to the importance of debt
financing for entrepreneurial firms (Holtz-Eakin et al., 1994; Berger and Udell, 1998; Beck and Demirguc-Kunt, 2006). Entrepre-
neurs have a strong incentive to retain a high-equity ownership stake and to borrow to meet a venture’s required capital. Robb
and Robinson (2014) find that US start-ups rely heavily on external debt and that higher levels of debt are associated with faster
revenue and employment growth, consistent with prior UK evidence (Cosh et al., 2009). Cosh et al. (2009) note that entrepre-
neurs often obtain the capital that they need, but not in the form that they like. Cole et al. (2016) find that venture capital appears
to help more than debt finance, at least based on aggregate state-level data in the US. Tykvova (2017) shows that venture lending
tends to go to different types of entrepreneurs than venture capital.

Cole and Sokolyk (this issue) extend prior papers and focus on the two kinds of debt which exist for start-ups: debt originated
by the venture (business debt) and debt originated by the entrepreneur (personal debt). They hypothesize that high-quality start-
ups are more likely to use business debt and less likely to use personal debt than other start-ups. This hypothesis is based on the
fact that business debt is fundamentally different from personal debt, with respect to screening and monitoring. Furthermore,
business debt usually originates from an informed lender, while personal debt is obtained from an arm’s-length lender (Rajan,
1992). Loan officers base their approval of a business loan-application on the performance prospects of the firm and on its cred-
itworthiness. If the loan is approved, the lender typically monitors the firm during the term of the loan. In contrast, when eval-
uating a personal loan, a lender assesses the creditworthiness of the entrepreneur and not the venture. Consequently, the cost of
underwriting a business loan is greater than the cost of underwriting a personal loan. A borrower might also find a business loan
application more costly than a personal loan application, in terms of document preparation and time spent. Therefore, entrepre-
neurs might favor personal loans, even if they put their personal wealth and assets at risk. The authors find that the distinction
between personal debt and business debt is important. Better quality start-ups are more likely to obtain business debt, and such
debt is associated with higher survival and revenue growth rates. Unfortunately, their data do not allow a control for the banks’
selection or monitoring capabilities. At the same time, only the best entrepreneurs might solicit bank financing. Hence, self-
selection might also explain part of the results. Nevertheless, the paper provides additional evidence on the importance of tradi-
tional bank lending in the entrepreneurial finance landscape.

3.4. Venture capital

3.4.1. Venture capital and the equity gap
The equity gap is the difference between the amount of equity that would be invested in a complete market and the actual

amount invested. There is substantial interest in practice, as to whether or not an equity gap exists for entrepreneurs, and
there is considerable theoretical debate about the equity gap or, more generally, a funding gap (Stiglitz and Weiss, 1981; De
Meza and Webb, 1987; Le Grand, 1991; Holmström and Tirole, 1998). However, contributions empirically assessing the funding
gap’s existence and magnitude are lacking. This is surprising, since the question of whether or not firms, especially small and
young ventures, are able to secure sufficient funding for viable projects remains of particular importance for every country to se-
cure its future innovation capacity, prosperity, and employment. Therefore, policymakers and academic researchers have increas-
ingly begun to discuss whether or not an equity gap indeed exists and whether or not the government can or should step in to
bridge it. There are several types of direct public support for young and small ventures, including debt and equity guarantee
schemes, grants, direct investments, tax reliefs, incubators, investments via venture capital funds, or funds of funds. These
schemes are not limited to certain countries. They are widespread across the world to such an extent that the public activity
may even crowd out private investment where programs are designed improperly, such as in Canada (Cumming and
MacIntosh, 2006). Other countries have had a better experience with public support programs, such as Europe (Leleux and
Surlemont, 2003) and the US (Howell, 2017).

553D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

European policymakers are currently concerned that an equity gap limits Europe’s competitiveness and innovation capacity.
The notion is based on the observation that, given comparable economic development levels and cultural similarity, access to cap-
ital for young ventures seems to be much easier in the US than in Europe. Various measures of the liquidity of the entrepreneurial
finance market and investment activity (e.g. estimated venture capital or business angels’ investments over GDP) reveal that
Europe is at a strong disadvantage compared to the US. At the same time, this capital market segment in the US proves to incu-
bate current and future world technology leaders. It is, therefore, a viable issue to address the existence of an equity or funding
gap in Europe. McCahery et al. (2015) estimate a funding gap for France, Germany, The Netherlands, Poland, and Romania and
conclude that in 2013 the gap ranged between 4.8% (in Germany) and 20.3% (in The Netherlands) of the countries’ GDPs. They
estimate the supply in the entrepreneurial finance market via SME loan and equity investment aggregates. The more challenging
task remains the estimate of demand for early stage capital. The authors refer to the number of SMEs in the respective countries
and the results of a survey on the access to finance of enterprises carried out by the European Central Bank in 2013.

Wilson, Wright, and Kaceer (this issue) estimate the equity gap from the start-up to the growth phase of young ventures in
the UK from 2004 to 2014. They comprise a data set of 12.2 million company-year observations and match this with all
known UK VC transactions, thus detecting 1847 VC backed “knowledge-intensive” manufacturing or service ventures. These com-
panies can be considered a distinct subsample of the population of UK corporations. Using this sample and a propensity score
matching procedure, Wilson, Wright, and Kaceer (this issue) are then able to identify the young ventures that did not receive ven-
ture capital but resemble the VC backed companies in the sample. This provides an estimate for the potential demand for equity
funding and can be extrapolated to the population of investable companies, by sector. Their annual equity gap estimates range
between £12 billion to £32 billion and present an unmet demand among the relevant company population at the given time.

Wilson et al. acknowledge that these estimates do not provide direct evidence that the non-VC-backed companies are indeed
in search of external financing nor that they are particularly attractive for VC investors. Nevertheless, the paper provides an intu-
itive and rigorous quantitative assessment of an equity gap for the UK and, therefore, makes an important contribution.

Further research could examine the sources of capital gaps in other countries. For example, Bao et al. (2016) provide Chinese
evidence that political connections are critical for accessing equity markets. International evidence on differences in access to
capital and equity gaps has the potential to highlight the role of policy in facilitating entrepreneurship and economic
development.

3.4.2. Venture capital and IPOs
The IPO is an important exit event of venture capital investments (Cumming, 2008; Cumming and Johan, 2013; Ozmel et al.,

2013). IPOs have strong reputation effects for venture capitalists (Gompers, 1996; Lee and Wahal, 2004; Nahata et al., 2014;
Johan, 2010), and there is evidence that experienced funds are able to time them (Lerner, 1994), subject to their contractual rights
to control exit (Cumming, 2008). However, venture capitalists rarely sell all of their shares at the time of the IPO (Barry et al.,
1990; Gompers and Lerner, 1998). IPO subscribers would assume that the venture capitalists sell because they believe that the
issue is overpriced. Therefore, the issuing price needs to be low enough to allow a positive return to the subscribers. If this is
not the case, the IPO market could be considered a market for lemons (Akerlof, 1970) and collapse. Venture capitalists have a
strong incentive to maintain their reputation and to retain access to the IPO market.

So why should venture capitalists sell their shares in an IPO if the issue is not overpriced? This question is the motivation of
the paper by Jeppsson (this issue). He follows the suggestion of Barry et al. (1990), assuming that retention of ownership in an
IPO signals value and an ongoing commitment to monitoring. Megginson and Weiss (1991) even argue that maintaining owner-
ship in the company certifies the issue price. Therefore, Jeppsson (this issue) analyzes a rationale which contrasts the extant ven-
ture capital literature: Preexisting venture capital investors’ can subscribe for shares in the public offer. In particular, he examines
the certification role of venture capitalists as insiders buying in the IPO and the association with price revision, underpricing, the
probability of completing the offer, and the aftermarket performance. He finds that venture capitalists are more likely to make
subscription pre-commitments when they hold higher pre-IPO ownership stakes, when the fund is older, when the dilution effect
is larger, and when more capital has been invested prior to the IPO. The insiders’ participation is negatively associated with
underpricing, although this effect is mediated in pricing revisions, and their participation increases the likelihood of completing
the offering.

However, despite the positive consequences of retaining ownership in public firms, it remains to be discussed that it is neither
the economic nor the directed task of venture capital funds to maintain exposure in public stock markets and to monitor quoted
companies. This activity could distract resources, which are required for selecting, backing, and monitoring start-ups. Furthermore,
longer holding periods could negatively impact the internal rates of return of particular transactions, even if additional capital
gains were possible after the IPO. This would penalize the venture capital industry as a whole from the capital supplying institu-
tions’ perspective.

3.5. Reporting quality of private equity backed IPOs

Unquoted equity markets are in-transparent market segments. Not only academic researchers criticize the (un-) availability of
data; politicians and the general public also have an interest in better understanding and being informed about the activities of
venture capital and private equity investors and the consequences of their investments. In the first publicly distributed paper
on the topic (dating back to early 2004), Cumming and Walz (2010) show that private equity fund managers tend to overvalue
their unexited investments, and one reason they do so is to attract future funds from institutional investors, as they know funds

554 D. Cumming, A.P. Groh / Journal of Corporate Finance 50 (2018) 538–555

that overvalue more have raised more money. Cumming and Walz show that reporting is better in countries with better legal
standards. Similar evidence is found in Johan and Zhang (2014) with more detailed information (arguably the most detailed in-
formation possible, which is from Pitchbook) about specific types of institutional investors. Johan and Zhang (2014) note that
even different institutional investors in the same PE fund receive different valuation reports at the same time. Endowments
tend to receive better information than other types of institutional investors. Jenkinson et al. (2016) provide evidence that private
equity valuations are inaccurate predictors of future cash flows. Ironically, Cumming and Johan (2017) document that research on
reporting in private equity valuations tends not to completely report other competing work on the topic and offers some expla-
nation for why this is the case.

In the more regulated public equity market, investors and investees are obliged to follow certain reporting standards. These
standards increase transparency for investors. Crain and Law (2017) reveal that implementing fair value accounting principles
also improves the quality and transparency of reports of venture capital and private equity funds to their investors. Morsfield
and Tan (2006) show that venture capital funds raise the quality of financial statements of the companies they bring public.
Goktan and Muslu (this issue) add to their finding by taking advantage of the circumstance that, increasingly, venture capital
and private equity funds become listed entities. Once listed, they must disclose information about their activity and portfolio com-
panies according to the prevailing listing standards. They could demand the same standards from their investees right after taking
a stake. As a result, listed venture capital and private equity firms could establish higher-quality reporting infrastructures for their
portfolio companies long before they take them public. Goktan and Muslu (this issue) collect a sample of listed and non-listed
venture capital and private equity firms and the portfolio companies that they brought public. They find that the investees that
went public indeed provide higher-quality financial information, if they were formerly backed by a listed fund. These investees
also experience less stock return reversals after their IPO. In addition, if a venture capital or private equity fund becomes listed
itself, abnormal accruals of its portfolio companies drop significantly. Overall, the authors document a positive effect of the public
reporting model of listed venture capital and private equity firms. They suggest that the implementation of tight reporting stan-
dards for alternative asset managers is warranted, given the fact that they can do better if they want, or if they have to.

4. Future directions

As mentioned above, research in entrepreneurial finance in this special issue of the Journal of Corporate Finance has followed
important themes, including the determinants of investment patterns and capital gaps, the value-added by investors, governance
and reporting problems, and factors of investment success. These themes can be applied to different types of sources of capital.

Future work in entrepreneurial finance could focus more closely on the intersection of different sources of capital. Cumming
and Vismara (2017) document an unfortunate degree of segmentation of research in entrepreneurial finance, as data on the
topic are typically derived from the source of capital. Some exceptions include Cosh et al. (2009), Robb and Robinson (2014),
Cole et al. (2016), Tykvova (2017), and Cumming et al. (2018). More work along these lines is certainly warranted to better un-
derstand what happens to entrepreneurs who do not secure the capital that they desire, which sources of finance are optimal and
in what combinations, and how policymakers should consider strategies that optimize support programs for entrepreneurial fi-
nance that account for an array of sources. These research outcomes will likely be better realized by reflection on entrepreneurial
finance as an interdisciplinary subject with insights from a wide range of journals and disciplines.

5. Conclusions

This introduction highlights select papers in this special issue of the Journal of Corporate Finance and how these papers contrib-
ute to related literature on the topic. While our literature review is not exhaustive in this short introductory article, we identify
some gaps and offer some suggestions for future research.

The excellent papers collected for this special issue break significant new ground by introducing and examining extensive data
that improves our understanding of important questions in entrepreneurial finance research, practice, and policy. Most of the pa-
pers were presented at the MAELYSE Research Federation entrepreneurial finance conference in Lyon, France, in July 2016. We
believe the presentations and collegial discussions at the conference and the referee process at the Journal of Corporate Finance
helped strengthen the papers and certainly enable us to reflect more completely on the interesting developments in entrepre-
neurial finance research. We hope you enjoy reading the cutting-edge papers in this special issue.

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Entrepreneurial finance: Unifying themes and future directions
1. Introduction
2. Google scholar trends in entrepreneurial finance
3. Discussing recent research
3.1. Equity crowdfunding
3.2. Angel finance
3.3. Debt for entrepreneurs
3.4. Venture capital
3.4.1. Venture capital and the equity gap
3.4.2. Venture capital and IPOs
3.5. Reporting quality of private equity backed IPOs
4. Future directions
5. Conclusions
References

Contents lists available at ScienceDirect

Journal of Corporate Finance

journal homepage: www.elsevier.com/locate/jcorpfin

Do institutional investors play hide-and-sell in the IPO
aftermarket?☆

T

amara Nefedovaa,⁎, Giuseppe Pratobeverab
a Université Paris Dauphine – PSL, CNRS, UMR[

7

0

8

8], DRM, 7

5

01

6

Paris, France
b Vienna University of Economics and Business, Welthandelsplatz 1,

10

2

0 Vienna, Austria

A R T I C L E I N F O

Keywords:
IPO allocations
IPO aftermarket trading
Laddering
Flipping
Institutional investors

JEL classification:
G2

3

G2

4

G3

9

A B S T R A C T

We document a robust buy/sell asymmetry in the choice of the broker in the IPO aftermarket:
institutions that sell IPO shares through non‑lead brokers tend to have bought them through the
lead underwriters in the IPO aftermarket. This trading behavior is consistent with institutional
investors hiding their sell trades and presumably breaking their laddering agreements with the
lead underwriters. The asymmetry is the strongest in cold IPOs and is limited exclusively to the
first month after the issue, when the incentives not to be detected are the strongest. We show that
the intention to flip IPO allocations is not an important motive for hiding sell trades from the lead
underwriters. We find that hiding sell trades is an effective strategy to circumvent underwriters’
monitoring mechanisms: the more institutions hide their sell trades, the less they are penalized in
subsequent IPO allocations.

1. Introduction

Despite considerable research on the conflicts of interest in initial public offerings, there is little evidence describing moral hazard
problems faced by IPO investors. This topic deserves attention because investors’ behavior may ultimately affect the benefits and the
costs of the book-building method. In particular, we are interested if the IPO mechanism in place motivates the choice of the broker(s)
to which investors direct their trades in the IPO aftermarket. We hypothesize that the IPO bookbuilding method provides incentives
to investors to avoid lead underwriters for their sell trades in the IPO stocks in the early aftermarket.

Institutional investors may have an incentive to hide their sell trades from the lead underwriters in the IPO aftermarket (we call it
“hide-and-sell” hypothesis) for two main reasons. First, investors might try to hide their allocations sales in order to preserve their
business and relationship with the lead underwriters in the IPO allocations market. A key feature of book-built IPOs is that the
investment banks that underwrite the issue have considerable discretion over who receives allocations. As explained by Jenkinson
and Jones (

20

04), one of the popular justifications for such discretion, often emphasized by investment bankers, is that underwriters
can allocate shares to long-term holders of the stock in the interests of the issuer. Investors that readily sell their allocations in the IPO
aftermarket, commonly referred to as “flippers”, tend to put a downward pressure on the trading price. While this might not be a

https://doi.org/10.10

16

/j.jcorpfin.2020.101627
Received

19

February 2019; Received in revised form 5 March 2020; Accepted 9 April 2020

☆ We thank an anonymous referee and the Editors (Thomas Chemmanur, Douglas Cumming, Gang Hu, and Jonh Wei), Shiu-Yik Au, Romain
Boulland, François Degeorge, Michel Dubois, Laurent Frésard, Peter Gruber, Chuan-Yang Hwang, Tim Jenkinson, Andy Puckett and the participants
of 20

18

JCF Special Issue Conference at Hong Kong PolyU, NFA 2018 in Charlevoix, 2018 SFI Research Days, and the 1st Dauphine Ph.D. workshop
for their insightful comments and suggestions. We thank Jay Ritter for making IPO data available on his website. All errors and omissions are our
own.

⁎ Corresponding author.
E-mail addresses: tamara.nefedova@dauphine.psl.eu (T. Nefedova), giuseppe.pratobevera@usi.ch (G. Pratobevera).

Journal of Corporate Finance 64 (2020) 101627

Available online 18 April 2020
0929-

11

99/ © 2020 Published by Elsevier B.V.

T

http://www.sciencedirect.com/science/journal/09291199

https://www.elsevier.com/locate/jcorpfin

https://doi.org/10.1016/j.jcorpfin.2020.101627

https://doi.org/10.1016/j.jcorpfin.2020.101627

mailto:tamara.nefedova@dauphine.psl.eu

mailto:giuseppe.pratobevera@usi.ch

https://doi.org/10.1016/j.jcorpfin.2020.101627

http://crossmark.crossref.org/dialog/?doi=10.1016/j.jcorpfin.2020.101627&domain=pdf

relevant concern in hot IPOs, where flipping may serve to increase market liquidity, the selling pressure generated by flippers could
lower the price below the offer price in cold offerings (see Aggarwal (2003)). Underwriters may find it convenient to reward in-
stitutions that play a supportive role and do not flip their allocations, as they play a role as market makers in the secondary market
(Ellis et al. (2000)), and they may face reputational losses in case of poor aftermarket performance and too much flipping activity
(Aggarwal (2003)). Consistent with this view, Chemmanur et al. (2010) find that investors receive larger allocations when they hold
their allocations for longer periods. This gives investors an incentive to hide their allocation sales from the lead underwriters. We
label this incentive as the “allocation sales explanation”. Some existing studies suggest that investors may try to hide their allocation
sales in post-IPO trading (Griffin et al. (2007), Chemmanur et al. (2010)).

The second reason for hiding sell trades from the lead underwriters is related to the practice known as “laddering”, which involves
a quid-pro-quo arrangement between underwriters and their clients: investors receive IPO allocations in exchange for a commitment
to purchase additional shares in the aftermarket. The clients that enter such an agreement are called “ladderers”. As explained by Hao
(2007) and Griffin et al. (2007), laddering could be beneficial for the lead underwriters as the buying pressure from ladderers could
reduce the underwriters’ price support costs in the IPO aftermarket, especially in cold IPOs. Moreover, the pre-arranged client
demand in the aftermarket may increase underwriters’ brokerage commission revenues. The Securities and Exchange Commission
(SEC) considers laddering as a manipulative practice prohibited by Rule 101 of Regulation M under the Securities Exchange Act of
1934. However, the legal definition of laddering requires the aftermarket purchase to be a condition imposed by the underwriter

,

thus leaving some space for implicit quid-pro-quo arrangements in which investors volunteers to buy additional shares (Hao (2007)).
Consistent with lead underwriters engaging in laddering agreements with their clients, Griffin et al. (2007) find that investors are net
buyers through the lead underwriters in a sample of Nasdaq IPOs. We posit that ladderers may have an incentive to break their quid-
pro-quo arrangements if the shares that they committed to buy in the secondary market are in excess of their optimal holdings in the
IPO firm. The potential costs for the investors that break the agreement, in terms of future business with the underwriters, may
incentivize them to hide their sell trades. We label the incentive to hide sell trades that break investors’ laddering agreements with the
lead underwriters as “laddering explanation”. To the best of our knowledge, we are the first to document that laddering mechanism
may provide an incentive for the investor to avoid the underwriting brokers when selling the IPO stock in the aftermarket.

The hiding strategy that we consider in this paper is to sell IPO shares through brokers other than the lead underwriters (hen-
ceforth, “non‑lead brokers”). We motivate our focus on this hiding strategy because of its simplicity of execution, as institutional
investors usually trade through more than one brokerage house (Goldstein et al. (2009)). If the hide-and-sell hypothesis holds, and
investors use this simple hiding strategy, then we should observe them to be less likely to trade through the lead underwriters when
they sell, than when they purchase shares in the IPO aftermarket. We directly test this prediction using detailed institutional trading
data, which allow us to control for important variables that may affect both the selling decision and choice of the broker, such as the
relationship between the institution and the lead underwriters or any other institution-IPO specific characteristic. To the best of our
knowledge, we are the first to directly test this prediction. Our analyses document a robust buy/sell asymmetry in the choice of the
broker in the IPO aftermarket: institutional investors are significantly less likely to sell than buy through the lead underwriters during
the first month of trading after the IPO.

We consider two factors that may affect the hiding incentives of financial institutions. First, if the buy/sell asymmetry is driven by
hiding incentives, then it should be the strongest in cold IPOs: both the “allocation sales explanation” and the “laddering explanation”
predict the lead underwriters to be concerned the most about investors’ selling activity in weak offerings. Second, if the buy/sell
asymmetry is driven by hiding incentives, then we should not be able to detect it when there are no incentives to hide stock sales from
the lead underwriters. We perform placebo tests to show that the buy/sell asymmetry disappears after few months from the issue date
and in a matched sample of non-IPO stocks. Overall, our evidence is consistent with the predictions of the hide-and-sell hypothesis.

The buy/sell asymmetry may be driven by the “allocation sales explanation” and the novel “laddering explanation”. Our data and
methodology allow us to disentangle allocation sales from investors’ buying and selling activity in the secondary market. Hence, we
can investigate the reasons behind institutions’ behavior, in order to understand whether it is driven by flipping or laddering motives.

We argue that the “allocation sales explanation” might be overall weak in the United States because underwriters receive reports
documenting the allocation sales of their customers. Flipping of shares is tracked via the Depository Trust Company’s (DTC) IPO
Tracking System and the lead underwriters receive two types of reports (Aggarwal (2003)). The first report provides them with client-
level information about flipping activity of the investors to whom they allocated IPO shares. The second report provides them with
information about the aggregate flipping activity for each syndicate member, but this does not include client-level details. Therefore,
lead underwriters can detect their clients who sold their allocations, but do not have direct access to the identity of flippers that
received their allocations from other syndicate members. Consequently, investors that received IPO shares from other syndicate
members have some chances to hide their flipping activity from the lead underwriters by avoiding selling through them. Morevover,
flipping reports are not flawless and there is anecdotal evidence of institutional investors circumventing the DTC IPO Tracking
System.1 Though imperfect, the DTC IPO Tracking System dampens the scope for hiding flipping trades. The risk of being caught by
the lead underwriters might not be zero even for other syndicate members’ clients, as lead underwriters could exploit their re-
lationship with the other syndicate members or use allocations and aggregate flipping data to infer flippers’ identities. Since a great
portion of the IPO shares are underwritten by the lead managers (Corwin and Schultz (2005)), the incentive to hide allocations sales
might be overall weak.

1 Griffin et al. (2007) report that “in March 2005, the NASD fined Spear, Leeds and Kellogg with $1 million for concealing IPO shares from the DTC
system from August 1997 to January 2001”.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627

2

On the contrary, the hiding behavior that we investigate in this paper, that is, selling IPO shares through non‑lead brokers, might
allow investors to break their laddering agreements without being caught by the lead underwriters. Ladderers may purchase the
shares that they committed to buy through the lead underwriters and then sell the shares in excess of their optimal holdings through
any other broker. Since these stock sales (henceforth, “other sales” or “other sell trades” or “secondary sales”) do not involve
allocation sales, they are not detected by the DTC IPO Tracking System and leave scope for hiding them.

We disentangle allocation sales from secondary sales and, consistent with the above arguments and contrary to the conventional
view, we find that flipping is not a relevant motive for the institutions to hide their sell trades: the buy/sell asymmetry is mainly
driven by sell trades other than allocation sales.

The buy/sell asymmetry may be driven by investors buying through the lead underwriters and not necessarily by investors selling
through other brokers. If institutions showcase their buy trades to the lead underwriters when entering a laddering agreement, then
the buy/sell asymmetry may not be indicative of hiding behavior. In order to address this concern, we test whether institutions’
trading behavior differ during the first month relative to the third month after an IPO. Consistent with investors showcasing their buy
trades, we find that they tend to execute a higher percentage of their buy trades through the lead underwriters during the first month
after the issue. However, we also find that institutions tend to execute a lower percentage of their secondary sales through the lead
underwriters during the first month after the IPO. This evidence is consistent with the buy/sell asymmetry being driven, at least in
part, by secondary sales.

We investigate other predictions of the novel laddering explanation of hiding. First, if investors break their laddering agreements,
then it has to be the case that they sell the shares that they committed to buy through the lead underwriters. Second, if investors hide
the breaking of the agreement and use the simple hiding technology considered in this paper, then they should tend to execute a
higher proportion of their sell trades through non‑lead brokers when they buy shares through the lead underwriters and when they
sell secondary shares. These two hypotheses predict a positive correlation between the proportion of sell trades executed through
non‑lead brokers, the volume of shares bought through the lead underwriters, and the volume of secondary sales. Overall, we find
evidence consistent with these predictions and with the “laddering explanation”.

Finally, we find that hiding sell trades is an effective strategy to circumvent underwriters’ monitoring mechanisms: the more
institutions hide their sell trades, the less they are penalized in subsequent IPO allocations.

The idea that investors may hide their sell trades is not new. However, the literature has exclusively framed it within the
allocation sales explanation. Some existing studies suggest that investors might try to hide their allocation sales from the lead
underwriters in the IPO aftermarket. For example, Griffin et al. (2007) find that investors are overall net sellers through brokers that
do not belong to the syndicate group and net buyers through the lead underwriters during the first month after the issue. Using
institutional trading data, Chemmanur et al. (2010) finds that institutional investors abnormally split their orders in the IPO after-
market and suggest that it might be an attempt to hide flipping trades. In both papers, the idea is that flippers would like to hide their
allocations sales in order to preserve their business with the lead underwriters in subsequent IPOs.

Though suggestive and relevant, the existing evidence is far from being conclusive. Investors could split their orders or sell
through non‑lead brokers for reasons other than hiding. For example, they could split their trades in order to generate a stream of
abnormal commissions to the lead underwriters as a reward for receiving IPO allocations (Reuter (2006), Nimalendran et al. (2007),
Goldstein et al. (2011), and Jenkinson et al., 2018). The difference in net buy between lead underwriters’ clients and non‑lead
brokers’ clients might be driven by the characteristics of the trading institutions, such as their relationship with the lead underwriters.
Since institutional investors tend to keep stable relationships with their brokers (Goldstein et al. (2009)), some of them being
connected to underwriting brokers through common educational background (Hwang et al. (2018)), institutions that are usual
underwriters’ clients are more likely to trade with them in the IPO aftermarket. In order to preserve this relationship, they may also be
more likely to support IPO prices by buying or avoiding to sell in the secondary market. On the contrary, institutions that are not
usual underwriters’ clients are more likely to trade with their own usual brokers in the IPO aftermarket and may also be more likely to
sell IPO stocks. Moreover, the existence of flipping reports dampens the scope for hiding allocations sales through any trading
strategy in the aftermarket. The questions whether, to what extent, and why institutions may hide their trades remained open. The
aim of this paper is to shed light on them.

Our findings contribute two streams of research. First, our paper is related to an extensive literature that investigates the benefits
and costs of the bookbuilding method of bringing companies public. While underwriters’ discretion may have the benefits of in-
centivizing investors’ information production (Benveniste and Spindt (1989), Benveniste and Wilhelm (1990), Sherman (2000),
Cornelli and Goldreich (2001), and Sherman and Titman (2002)) and of placing allocations in the hands of long-term investors
(Aggarwal (2003), Jenkinson and Jones (2004), Jenkinson and Jones (2009), and Chemmanur et al. (2010)), an increasing body of
research unravels the conflicts of interest inherent to the bookbuilding method (Loughran and Ritter (2004), Reuter (2006), Griffin
et al. (2007), Hao (2007), Nimalendran et al. (2007), Ritter and Zhang (2007), Jenkinson and Jones (2009), Liu and Ritter (2010),
Goldstein et al. (2011), Ritter (2011), Jenkinson et al., 2018, and Hwang et al. (2018)).2 Underwriters seek to stimulate investor
demand and raise the offer price. Vismara et al. (20

15

) show that underwriters bias upward the selection of comparable companies
for the IPO company to look relatively more attractive, which result in higher IPO underpricing. Laddering arrangement, extensively
discussed in this paper, is another practice by underwriters that is aimed to increase the aftermarket price of the issuer’s shares. As the
existing literature mainly focuses on the conflicts of interest between underwriters and issuers, we enrich it by investigating a so far
overlooked moral hazard problem faced by investors. Our findings suggest that investors’ hiding behavior may affect the potential

2 See Lowry et al. (20

17

) for a recent comprehensive survey of the IPO literature.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
3

benefits and costs of underwriters’ discretion and stimulate further research to study the incentives of IPO investors.
Second, we shed light on the determinants of the choice of the broker by financial institutions. Our findings are consistent with

models in which investors face a trade-off between preserving long-term relationships with brokers that give them access to premium
services and the need to hide their trading strategies (Goldstein et al. (2009)). We find a clear persistence in the choice of the broker,
which cannot be explained trading costs and depends strongly on the long-term relationship between institutions and their brokers.
We show how hiding incentives affect the choice of the broker in the context of IPOs.

The rest of the paper is organized as follows. Section 2 describes our sample selection criteria, defines the main variables used in
our analyses, and provides summary statistics. Section 3 documents that institutions are less likely to trade through the lead un-
derwriters when they sell than when they buy shares in the IPO aftermarket, especially in cold IPOs. This section includes placebo
analyses to check that this behavior is not present when there are no hiding incentives. Section 4 examines what drives the buy/sell
asymmetry: buy trades, secondary sales, or allocation sales. Section 5 tests the predictions of the “laddering explanation” of hiding.
Section 6 rules out potential alternative explanations and addresses endogeneity problems. Section 7 investigates the effectiveness of
the hiding behavior in an attempt of the institutions to preserve their relationship with the underwriter. Finally, Section 8 concludes.

2. Data and summary statistics

2.1. IPO data

We use the Thomson Financial Security Data Company (SDC) database to identify IPOs made in the United States from 1999 to
2010.3 We exclude all American Depository Receipts (ADRs), Real Estate Investment Trusts (REITs), unit and rights offerings, closed-
end funds, IPOs with SIC codes between 6000 and 6199 and IPOs with offer price smaller than $5. We further require IPOs to have a
match with the Center for Research in Security Prices database (CRSP) within seven calendar days from the issue. These filters leave
us with

14

39 IPOs. In addition, we require IPOs to have a CUSIP match with the ANcerno/Abel Noser Solutions database, which
provides us with detailed institutional trading data. We describe ANcerno trading data in the next subsection. This criterion leads us
to drop 51 IPOs. We drop three IPO firms that show inconsistent data: these firms show trading activity in the ANcerno database
before the IPO date. Finally, we require at least one lead underwriter of each IPO to be matched with a broker of the Abel Noser
Solutions database. This filter leaves out

24

firms. Our final sample consists of

13

61 IPOs involving 89 distinct lead underwriters. The
number of IPOs varies considerably by year, ranging from 14 in 2008 to 373 in 1999.

By matching SDC and CRSP, we get the percentage return from the IPO offer price to the first day closing price (Underpricing) and
we winsorize it at the 95% level. The average underpricing in our sample is 37.6% and the median is 14.8%. Since our hide-and-sell
hypothesis depends on underpricing, we split our sample into terciles based on this variable. We define an IPO as “hot” if it is in the
highest tercile (Underpricing > 29.4%), “weak” if it is in the middle tercile (5.1 % > Underpricing ≤ 29.4%), and “cold” if it is in
the lowest tercile (Underpricing ≤ 5.1%).

2.2. Institutional trading data in the IPO aftermarket

We obtain institutional trading data for our sample of 1361 IPOs from the ANcerno/Abel Noser Solutions database. The IPO
trading data covers the period from January 1999 to March 2011. For each trade placed by an institution, we get the following
information: the name and the identity code (“managercode”) of the institution, the name and the identity code (“brokercode”) of the
broker executing the trade, the trading date, the CUSIP of the stock traded, the number of shares traded, a variable identifying the
side of the trade (buy or sell), the execution price, and the commissions paid. The reader may refer to the Appendix A for the detailed
description of the database.

We require trades to have non-missing managercodes and brokercodes, and to be sent to ANcerno by pension plan sponsors or
money managers.4 We match the Abel Noser Solutions database to the Thomson Reuters Institutional 13F Holdings database by
institution names. We require institutions to have a match with 13F. A description of the matching procedure across several databases
is provided in Fig. A1 of the Appendix A.

Summary statistics for more than 1.2 million institutional trades during the first year after the issue date are presented in Table 1.5

The trades in the sample are placed by

22

7 distinct institutions of Abel Noser Solutions and are executed by 700 different brokers. The
average trade involves 6565 shares. 8.2 billion IPO shares are traded during the first year from the issue, for a total value of

25

1.9
billion US dollars. Lead underwriters have a large weight in the brokerage market of IPO stocks: during the first month after the IPO
date, 40.4% of the IPO shares are traded through the lead underwriters. The percentage decreases in subsequent months to about
15%. The market share of brokers that did not participate in the underwriting syndicate (henceforth, “other brokers”) shows the
opposite pattern: it is 52.4% during the first month after the IPO date and it increases in subsequent months to about 70%.

Our hide-and-sell hypothesis predicts that institutional decision to trade with the lead underwriters depends on the side of their

3 We apply corrections to our sample of IPOs using the information provided by Jay R. Ritter at the University of Florida as of April 2014: https://
site.warrington.ufl.edu/ritter/ipo-data/.
4 This means that we require trades to have client-type code equal to 1 or 2. We exclude the relatively small amount of trades sent to ANcerno by

brokers.
5 Results are similar if we exclude IPOs issued after March 2010, thus ensuring we have full

12

months of trading data for all IPOs in our sample.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
4

IPO Data

IPO Data

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T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
5

trade. Fig. 1 breaks down the market share of the lead underwriters for buy trades (black lines) and sell trades (light grey lines). For
each IPO, we compute the percentage volume of institutional buy and sell trades executed by the lead underwriters and other brokers
in each month from the IPO date. Then we average these percentages across IPOs and compute 95% confidence intervals around the
means. Panel (A) shows that the weight of the lead underwriters during the first month after the IPO date differs significantly
depending on the trade side: it is almost 40% for buy trades and it is below 30% for sell trades, consistent with hiding behavior.6 The
market share of buy and sell trades becomes insignificant after the first month, consistent with hiding incentives being at place only
during the first month of trading. Panels (B)-(D) break down the brokerage market share by underpricing terciles. We notice that the
difference between buy and sell trades is mainly driven by cold IPOs, consistent with the intuition that hiding incentives are stronger
in cold IPOs.

In the rest of the paper, we aggregate Abel Noser Solutions’ trade volumes at the daily level. Thus, our trading dataset comprises
observations at the IPO-institution-broker-day level. Henceforth, with the word “trade” we mean “daily trade”. The daily level of
aggregation allows us to neglet intra-day trading decisions, which might involve several factors unrelated to our subject of study, such
as institutions’ churning shares to generate commissions to the lead underwriters (Goldstein et al. (2011)). Morevover, it allows us to
avoid complications related to the intra-day trading time reported by the Abel Noser Solutions database. Fig. 2 focuses on the first 21
trading days after the IPO. For each IPO, we compute the total amount bought and sold in each day by institutions that trade through
the lead underwriters, through other syndicate members, and through brokers that did not participate in the IPO syndicate (bars). We

Fig. 1. Average lead UW market share for different levels of underpricing. This figure shows the average brokerage market share for buy trades
(dark grey lines) and sell trades (light grey lines) of the lead underwriters by month from the IPO date. For each IPO, we compute the percentage of
institutional buy and sell trades executed by the lead underwriters in each month from the IPO date; then we average these percentages across IPO
and we compute 95% confidence intervals of the means (dashed lines). Panels (A) reports the brokerage market share for all IPOs. Panel (B) reports
the brokerage market share for hot IPOs (highest tercile of Underpricing); Panels (C) reports the brokerage market shares for weak IPOs (middle
tercile of Underpricing); and Panels (D) reports the brokerage market share for cold IPOs (lowest tercile of Underpricing).

6 These numbers are slightly different from those in Table 1 because Figure 1 computes the average broker market shares in IPOs, while Table 1
computes brokerage market shares in the IPO aftermarket for IPOs as a whole.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
6

also compute the cumulative netbuy of lead managers’ clients, syndicate members’ clients, and other brokers’ clients (lines). The
volume traded is scaled by the number of shares issued and it is averaged across IPOs. Panel (A) plots buy, sell, and cumulative
netbuy volumes for all sample IPOs. Broadly consistent with the existing literature (see Griffin et al. (2007)), we observe that
institutions are net buyers through lead managers and syndicate members and net sellers through other brokers in the first few
trading days after the IPO. Moreover, the daily volume sold tends to be larger through other brokers than through the lead un-
derwriters; on the contrary, the daily volume bought tends to be larger through the lead underwriters than through other brokers.
Finally, the difference in net buy between lead underwriters’ clients and other brokers’ clients is greater in cold IPOs. This is broadly
consistent with hiding behavior.

The graphical evidence presented in this section suggests that some hiding behavior might be at place, but it is far from being
conclusive. For example, the difference between buy and sell trades might be driven by institutional characteristics affecting both the
decision to sell and the decision to trade with the lead underwriters, without any them having an intention to hide their trades.
Institutions that decide to buy IPO shares and support the price of cold IPOs might be usual lead underwriters’ clients; therefore, they
might also tend to trade more through lead underwriters in the IPO aftermarket. Institutions that decide to sell IPO shares migth not
be usual lead underwriters’ clients; therefore, they might also tend to trade more through their usual brokers in the IPO aftermarket.
Our institution-level analysis in Section 3 sheds light on these issues and directly tests the predictions of the hide-and-sell hypothesis.

2.3. Identifying institutional IPO allocations sales

We identify institutional IPO allocations sales following the algorithm proposed by Chemmanur et al. (2010), which is consistent

Fig. 2. Average cumulative netbuy and buy/sell volume in the first 21 trading days after IPO issue date. For each IPO, we compute the total amount
bought and sold in each day by institutions that trade through the lead underwriters, through other syndicate members, and through brokers that
did not participate in the IPO syndicate. We also compute the cumulative netbuy of lead managers’ clients, syndicate members’ clients, and other
brokers’ clients in the first 21 trading days after the IPO. We scale the volume traded by the number of shares issued and we average it across IPOs.
Bars show institutions’ daily volume bought and sold; lines plot institutions’ cumulative netbuy. Panel (A) averages buy and sell volumes and
cumulative netbuy for all IPOs. Panels (B)-(D) break the averages down for hot IPOs (highest tercile of Underpricing), weak IPOs (middle tercile of
Underpricing), and cold IPOs (lowest tercile of Underpricing).

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
7

with the Depository Trust Company’s (DTC) IPO Tracking System. The objective is to disentangle an institutional allocations sales
from its buying and selling activity in the IPO aftermarket. In order to do so, we classify as IPO allocation sales only shares sold in
excess of the shares bought until a specific point in time by an institution. For example, consider an institution that buys 500 shares in
the secondary market during the first day after the issue date and then sells 300 shares on the second day and 300 shares on the third
day. Then the IPO allocation sales of that institution are equal to zero on day 1 and 2 and are equal to 100 on day 3.

Our sample institutions flip 3.2% of the shares offered within the first 21 trading days post-IPO and continue to sell their
allocations in subsequent months. By the end of the first year, our sample institutions flip 8.5% of the shares issued on average. The
amount of flipping is the highest for hot IPOs (almost 12% at the end of the first year) and the lowest for cold IPOs (less than 5% at the
end of the first year). For more details on flipping activity of our sample institutions refer to Fig. B2 of the Appendix.

2.4. Identifying institutional IPO allocations

We identify IPO allocations by combining institutional trading data with quarterly holdings data reported in 13F. The basic idea is
to compute IPO allocations as the difference between the institutional holdings in the IPO firm at the first 13F filing date following
the IPO and the net buying by the institution in the IPO firm between the IPO date and the 13F filing date. However, as pointed out by
Chemmanur et al. (2010), it is unlikely to compute allocations precisely by matching 13F and the Abel Noser’s Solution Database
because of data differences in the two databases. For example, 13F might not contain all stock holdings, as institutions are required to
disclose common stock positions greater than 10,000 shares or $200,000. This kind of matching problems might generate some
inconsistencies when computing allocations as holdings minus net buying. For example, we might compute negative allocations and/
or allocations smaller than the amount of shares flipped.

In order to rule out these inconsistencies, we complement our allocation proxy with allocation sale data. The idea is that an IPO
allocation has to be at least equal to the amount of shares flipped by the institution. Formally, we proxy IPO allocations as follows. We
denote Hi, j as the number of shares of IPO i held by institution j at the first filing date after the IPO; Δi, j as the total netbuy of IPO i
shares by institution j between the IPO date and the first filing date after the IPO; and Let Fi, j as the number of shares of IPO i flipped
by institution j – as computed in Section 2.3 – in the first three months after the IPO. We then compute the percentage of shares of IPO
i allocated to institution j, AllocPerci, j, as:

=AllocPerc
H F

SharesIssued
max ( , ) 100i j

i j i j i j

i
,

, , ,

and we winsorize it at the 95% level. Table 2 reports IPO allocations summary statistics at the institution and issuer level. Conditional
on receiving an allocation, an average institution gets 1.89% of the issue. In an average IPO, about 23 sample institutions receive an
allocation and get 42.7% of the offer.

Allocations vary with underpricing. Institutions that receive cold IPO shares get a larger percentage of the issue than institutions
that receive hot IPO shares (2.53% versus 1.53%). However, the number of institutions that receive allocations is much smaller in
cold IPOs than in hot IPOs (13.6 versus 30.6). Thus, the total allocation to institutional investors is lower in cold IPOs than in hot IPOs
(34.3% versus 47%).

Table 2
Summary statistics of IPO allocations by institution and by issuer.

Mean p50 sd

AllocPerc (all IPOs) 1.89 0.54 3.05
AllocPerc (hot IPOs) 1.53 0.40 2.71
AllocPerc (weak IPOs) 1.98 0.67 3.02
AllocPerc (cold IPOs) 2.53 0.78 3.67
Number of allocations (all IPOs) 22.7 21 14.4
Number of allocations (hot IPOs) 30.6 30 13.9
Number of allocations (weak IPOs) 23.7 22 13.6
Number of allocations (cold IPOs) 13.6 12 10.1
Total % institutional allocation (all IPOs) 42.7 42.5 21.7
Total % institutional allocation (hot IPOs) 47.0 45.6 23.1
Total % institutional allocation (weak IPOs) 46.9 47.3 20.6
Total % institutional allocation (cold IPOs) 34.3 33.9 18.7

This table provides IPO allocations summary statistics at the institution level (AllocPerc) and issuer level (Number of Allocations; Total %
Institutional Allocation). AllocPerc is the percentage of IPO shares allocated to an institution (winsorized at the 95% level). The table
reports summary statistics for all IPOs and for subsamples of IPOs based on Underpricing terciles: hot IPOs (highest tercile), weak IPOs
(middle tercile), and cold IPOs (lowest tercile). For each variable, the table reports its average (mean), its median (p50), and its standard
deviation (sd).

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
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3. Buy/sell asymmetry

3.1. Baseline results

If investors systematically hide some of their sell trades from the lead underwriters (hide-and-sell hypothesis) by trading with
other brokers, then we should observe the probability of trading through the lead underwriters to be lower for sell trades than for buy
trades in the IPO aftermarket. In order to test this prediction, we run several specifications of the following linear probability model
(LPM) described in Eq. (1)7:

= + + + + + +LeadDummy Sell X ui j b t i j b t i j b t j i i j i j b t, , , , , , , , , , , , , (1)

where Selli, j, b, t is a dummy variable equal to one if the institution j is selling the IPO i through broker b on day t and zero if it is
buying. The dependent variable, LeadDummyi, j, b, t, is a dummy variable equal to one if the broker b executing the trade is any of the
lead underwriters of IPO i and zero otherwise. Xi, j, b, t is a vector of control variables, which are described below; δj, θi, and λi, j are
institution, IPO, and institution-IPO fixed effects; ui, j, b, t is the error term, which we allow to be correlated within institution. The
hide-and-sell hypothesis predicts β < 0.

The vector of control variables includes the trading volume RelVol, which is the number of shares traded by the institution scaled
by the number of shares issued and multiplied by 100. We control for the relationship between institutional investors and lead
underwriters. Lead underwriters’ usual clients are more likely to choose a lead underwriter as a broker at any point in time, including
the IPO aftermarket (see Goldstein et al. (2009)). They might also be more likely to support the IPO price to preserve their re-
lationship with the underwriters, thus being less likely to sell IPO shares than other investors. Conversely, institutions that are not
usual underwriters’ clients are less likely to trade with them and might be more likely to be IPO sellers. Therefore, a negative
correlation between the decision to sell IPO shares and the decision to trade with a lead underwriter might be driven by the re-
lationship between investors and underwriters. We control for it by means of the variable NormalTradeLead. For each institution-IPO
pair, we compute the percentage volume traded in non-IPO stocks by the institution through the lead underwriters in a 6-month
period prior to the issue.8 We compute this variable separately for buy and sell trades, to capture any potential heterogeneity in the
investor‑lead underwriters relationship by trade side. We include in the specification the variable Day, which is the day in which the
trade is executed relative to the issue date, in order to control for the likely decreasing trend in the probability of trading with a lead
underwriter. One important determinant of the choice to trade with the lead underwriters might be their trading expenses. Ex-
cLeadComm is the average percentage commission to the lead underwriters minus the average percentage commission to any other
broker paid by sample institutions in the first 21 trading days after the issue date. With this variable we capture how expensive it is to
trade with the lead underwriters relative to other brokers in the IPO aftermarket. We compute this variable separately for buy and sell
trades to capture any potential heterogeneity in brokerage commissions by trade side. Finally, we control for the percentage IPO
allocation received by an institution, AllocPerc. Institutions that receive IPO allocations might be more likely to trade with the
underwriters for several reasons, including quid-pro-quo agreements to generate a stream brokerage commissions to the lead un-
derwriters (Goldstein et al. (2011), Reuter (2006), and Nimalendran et al. (2007)) and “laddering” agreements to buy shares in the
IPO aftermarket (Griffin et al. (2007)).

Table 3 reports the OLS estimation results. We use standard errors clustered at the institution level for inference.9 Panel (A)
includes trades executed during the first 21 trading days after the issue date. We focus on this period because lead underwriters’
practices suggest that investors’ incentives to hide their sell trades should exist mainly during the first month of trading. Lead
underwriters track IPO flipping through the Depository Trust Company’s (DTC) IPO Tracking System and engage in market stabi-
lization activities usually during the first 30 calendar days after the issue date (see Aggarwal (2000)). In Column (1) we regress
LeadDummy on Sell; column (2) introduces control variables in the specification; columns (3), (4), and (5) control for institution,
institution and firm, and institution-firm fixed effects.

The coefficient of the variable Sell is negative and statistically significant in all specifications. Considering the estimate in column
(1), institutional investors are 6 percentage points less likely to trade through a lead underwriter when they sell IPO shares than when
they buy, consistent with the hide-and-sell hypothesis. The coefficient is statistically significant at least at the 5% level. It is also
economically significant: the probability of selling with a lead underwriter is almost 20% less than the probability of buying (0.06/
0.32). The correlation survives when we control for institution, firm, and institution-firm fixed effects. Column (3) controls for
institution fixed effects, such as their usual trading strategies in IPOs. Column (4) introduces IPO fixed effects, which capture any IPO-
specific characteristics, including the identity of the lead underwriters. It might be argued that NormalTradeLead controls only for the
past relationship between institutions and lead underwriters in brokarage services, but not for their future expected relationship nor
for their relationship in other services; in column (5) we control for any institution-IPO specific factor, exploiting within institution-

7 We choose the linear probability model because it allows us to control for fixed effects without incurring in the incidental parameter problem and
to estimate marginal effects. The potential bias and inconsistency of OLS with binary outcome are unlikely to be a concern in our setting, as the
average value of the dependent variable is not at the boundaries of the unit interval (it is 0.292). For monitoring purposes, we keep track of the
proportion of predicted probabilities outside the [0, 1] interval in our regression tables.
8 The 6-month period includes trades in non-IPO stocks executed from the trading day −147 to the trading day −22 before the issue date.
9 In unreported analyses, we allow the error term to be correlated within IPO, clustering standard errors at the firm level. The results become

stronger.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
9

IPO variation: an institution that is both buying and selling a given IPO is more likely to trade with the lead underwriters when it buys
than when it sells.

The coefficient of RelVol is positive and significant in all specifications: institutions that make larger trades are more likely to trade
with the lead underwriters. A one percentage point increase in the trading volume is associated with about 13 percentage points
increase in the probability of trading with a lead underwriter. As expected, there is a positive and statistically significant correlation
between LeadDummy and NormalTradeLead. A one percentage point increase in the proportion of trades that the institution normally
execute through the lead underwriters is associated with about 0.9 percentage points increase in the probability of trading with a lead
underwriter in the IPO aftermarket. The coefficient becomes much smaller and statistically insignificant when we control for in-
stitution-firm fixed effects, suggesting that the relationship between investors and underwriters is homogeneuos across trade side and,
thus, captured by these fixed effects. As expected, the coefficient of Day is negative and statistically significant. A one day increase in
the trading time relative to the issue date is associated with about one percentage point decrease in the probability of trading with a
lead underwriter. The coefficient of ExcLeadComm is negative in all specifications. However, it is statistically significant only when
we control for institution-firm fixed effect. Even though commissions does not seeem to be a main driver of the choice of the broker,
differences in trading commissions across trade side help explain the within institution-IPO variation of LeadDummy. Finally,
AllocPerc is only weakly significant in one specification (at the 10% level). Moreover, its sign flips across specifications. We cannot
make definitive conclusions about its correlation with the choice of the broker in the IPO aftermarket.

In unreported results we replicate our analysis considering trades executed during the first 7 trading days after the issue date. The
coefficient on Sell gets much stronger in all specifications, suggesting that most of the documented effect is concentrated in the first
few trading days after the IPO.

Table 3
Buy-Sell asymmetry of IPO stock trades and broker choice.

Dependent variable: LeadDummy

(1) (2) (3) (4) (5) (6) (7) (8)

Month 3 non-IPOs

Sell −0.060∗∗ −0.078∗∗∗ −0.053∗∗∗ −0.057∗∗∗ −0.052∗∗ −0.041∗ −0.0013 −0.0010
(−2.08) (−3.62) (−3.15) (−2.97) (−2.44) (−1.88) (−0.13) (−0.27)

Sell*ColdIPO −0.064∗∗∗

(−3.23)
RelVol 0.070∗∗ 0.14∗∗∗ 0.13∗∗∗ 0.14∗∗∗ 0.14∗∗∗ 0.098∗∗∗ −0.44

(2.44) (6.72) (8.06) (7.12) (7.19) (7.19) (−0.32)
NormalTradeLead 0.0086∗∗∗ 0.0092∗∗∗ 0.0094∗∗∗ 0.0025 0.0028 0.0002 0.0099∗∗∗

(5.20) (9.55) (7.40) (0.64) (0.72) (0.08) (7.59)
Day −0.011∗∗∗ −0.0092∗∗∗ −0.0089∗∗∗ −0.0081∗∗∗ −0.0081∗∗∗ −0.0001 0.0002

(−5.78) (−6.02) (−5.76) (−4.77) (−4.77) (−0.17) (0.54)
ExcLeadComm −0.17 −0.16 −0.057 −0.25∗∗ −0.28∗∗ 0.089 −0.028

(−1.13) (−1.53) (−0.49) (−2.29) (−2.56) (0.79) (−0.31)
AllocPerc −0.0003 0.0021 0.0024∗

(−0.06) (1.41) (1.82)
Constant 0.32∗∗∗ 0.35∗∗∗ 0.30∗∗∗ 0.18∗∗∗ 0.33∗∗∗ 0.33∗∗∗ 0.15∗∗∗ 0.0030

(9.91) (10.12) (21.54) (9.49) (17.34) (17.35) (6.27) (0.35)
Institution fixed effects No No Yes Yes No No No No
Firm fixed effects No No No Yes No No No No
Institution-firm fixed effects No No No No Yes Yes Yes Yes
Adjusted R2 0.0043 0.049 0.15 0.

26

0.45 0.45 0.48 0.44
Observations 44,576 44,576 44,576 44,576 44,576 44,576 24,643 28,990
% Outside [0,1] 0 0 0 0.080 0 0 0 0.0013

This table reports the estimation results of several specifications of a linear probability model in a sample of institutional trades in 1361 IPO stocks
issued between 1999 and 2010. The dependent variable LeadDummy is equal to one if the broker executing the trade is any of the lead underwriters
of the IPO. Columns (1)–(6) include 44,576 trades executed in the first 21 trading days after the issue date; Column (1) reports the results of an OLS
regression of LeadDummy on a dummy variable equal to one if the institution is selling and zero otherwise (Sell). Column (2) introduces several
control variables: RelVol is the number of shares traded by the institution scaled by the number of shares issued; NormalTradeLead is the percentage
volume of sell or buy trades in non-IPO stocks made by the institution through the lead underwriters in a 6-month period prior to the issue; Day is
the day in which the trade is executed, relative to the issue date; ExcLeadComm is the average percentage commission to the lead underwriters minus
the average percentage commission to any other broker paid by sample institutions for their buy or sell trades in the first 21 trading days after the
issue date; AllocPerc is the percentage IPO allocation received by the institution. Columns (3), (4), and (5) introduce institution, firm, and institution-
firm fixed effects. Column (6) reports the results of the regression including Sell ∗ ColdIPO – the interaction of Sell and ColdIPO, a dummy equal to one
if the IPO is in the lowest tercile of Underpricing. Column (7) includes the results of the placebo test on 24,643 trades executed in the 3rd trading
month after the issue date; Column (8) includes the results of the placebo test on 28,990 trades in 1109 matched non-IPO stocks during the 21 days
after the issue date of the matched IPO stocks. All non-dummy variables are winsorized at the 95% level. Standard errors are clustered at the
institution level (t-statistics are in parentheses). Significance levels are denoted as: * 0.1, ** 0.05, *** 0.01.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
10

3.2. Incentives in cold IPOs

Hiding incentives are stronger in cold IPOs. Underwriters are more likely concerned by sell trades when the aftermarket demand
for the IPO stock is weak, because sell trades put additional downward pressure on the price (Chemmanur et al. (2010)). Hence, we
hypothesize the buy/sell asymmetry in the choice of the broker to be stronger in cold IPOs. We define the variable ColdIPOi to be
equal to one if the firm i is in the lowest tercile of the variable Underpricingi and zero otherwise. We introduce an interaction variable
between ColdIPOi and Selli, j, b, t in our regression specifications. Under the hide-and-sell hypothesis, we expect the coefficient on the
interaction term to be negative. We report the estimation results in Column (6) of Table 3.

Consistent with the hide-and-sell hypothesis, the negative correlation between LeadDummy and Sell is stronger when hiding
incentives are more pronounced. The coefficient of the interaction term is negative and statistically significant at least at the 5% level.
The economic magnitude is also significant: investors are about 10.5 percentage points less likely to trade with a lead underwriter
when they sell shares of cold IPOs than when they buy shares in cold IPOs.

3.3. Placebo tests

If institutional investors are less likely to sell through the lead underwriters because they try to hide their sell trades, then we
should not observe this behavior when there is no incentive to hide.

Lead underwriters’ practices suggest that investors’ incentives to hide their sell trades should exist mainly during the first month of
trading. Hence, we should not detect systematic hiding behavior after the first month. Column (7) of Table 3 implements our
regression analysis for institutional investors’ trading activity during the third month after the IPO date. The coefficient of Sell is not
statistically different from zero and its magnitude is very small.

The hiding incentive is peculiar to IPOs: it should not exist for non-IPO stocks. Hence, we test for the buy/sell asymmetry in a
matched sample of trades in non-IPO stocks. We match trades as follows. First, we require candidate non-IPO stocks to be similar to
the matched IPO. For each IPO, we select candidate non-IPO stocks that: (i) are in the same one-digit industry; (ii) are in the same
quintile of market capitalization; (iii) are in the same tercile of Tobin’s Q.10 Then, we match each buy (sell) trade in IPO stocks with a
buy (sell) trade made by the same institution in a candidate non-IPO stock within a 21 trading days window from the IPO date. The
matched trade is the one with the closest dollar volume. We lose 1909 trades in 55 IPOs because of missing data about market
capitalization, industry, or Tobin’s Q. Moreover, we lose 13,677 trades because of no match found. Our final sample consists of
28,990 trades in non-IPO stocks matched to 1109 IPOs.11 Column 8 of Table 3 implements our regression analysis for institutions’
trading activity in non-IPO stocks. The coefficient of Sell is not statistically different from zero and economically small.

Overall, our placebo tests confirm that the buy/sell asymmetry in the choice of the broker is peculiar to the IPO aftermarket,
consistent with hiding incentives.

4. What drives the buy/sell asymmetry?

4.1. Allocation sales versus secondary sales

In this section, we investigate the potential drivers of the buy/sell asymmetry. The existing literature suggests that investors might
try to hide their allocations sales in order to preserve their business with the lead underwriters in the IPO allocations market (Griffin
et al. (2007), Chemmanur et al. (2010)). Though relevant and sound, the incentive to hide allocation sales might be overall weak
because of the lead underwriters’ ability to infer flippers’ identities: though imperfect, the flipping reports produced via the DTC IPO
Tracking System dampen unambiguously the investors’ chances to hide their allocation sales. We find evidence consistent with this
view.

We keep only sell trades in our sample and introduce the explanatory variable AllocationSaleDummy that takes the value of one
when the trade contains an allocation sale and zero otherwise.12 Similar to Section 3, we run the following regression:

= + + + + + +LeadDummy AllocationSaleDummy X ui j b t i j b t i j b t j i i j i j b t, , , , , , , , , , , , , (2)

Table 4 reports the results.
Table 4 shows that the buy/sell asymmetry is mainly driven by the trades other than allocation sales (we refer to these sales as

secondary sales throughout the paper). The coefficient of AllocationSaleDummy is positive and significant, meaning these trades are
significantly more likely to be executed through the lead underwriters when they contain allocation sales, than when they do not
contain allocation sales. Column (6) of Table 4 shows that this asymmetry in the choice of the broker is no more detectable in month

10 We get this data from CRSP and COMPUSTAT.
11 The median volume difference between matched non-IPO trades and original IPO trades is 50 dollars. The correlation between dollar volumes of

original and matched trades is 0.7.
12 Since we observe allocation sales at the institution-IPO-day level (see Section 2.3), this definition of AllocationSaleDummy may be inaccurate if

investor j executes both secondary sales and allocation sales of IPO i during the same trading day t through several distinct brokers b. In our sample,
this problem can affect at most 645 observations out of 20,653. In Table 4, we assume that all of these 645 sell trades contain an allocation sale. In
unreported analyses, we exclude these 645 observations from the sample and find similar results.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
11

3, the coefficient on the variable AllocationSaleDummy is low and insignificant.

4.2. Showcasing buy trades versus hiding sell trades

The results of Table 3 may be driven by investors buying through the lead underwriters and not necessarily by investors selling
through other brokers. If institutions showcase their buy trades to the lead underwriters when entering a laddering agreement, then
the coefficient of the regression of LeadDummy on Sell may be downward biased in favor of our hide-and-sell hypothesis. To address
this concern, we perform an alternative empirical strategy and look at how institutions’ trading behavior differ during the first month
relative to the third month after an IPO. We run the regressions 3, 4, and 5 conditional on institutions that receive IPO allocations:

= + + + +BuyLead TotBuy Month X/ 1i j t t i j t i j i j t, , 0 1 , , 1 , , , (3)

= + + + +SecondarySalesLead TotSell Month X/ 1i j t t i j t i j i j t, , 0 1 , , 2 , , , (4)

= + + + +AllocationSalesLead TotSell Month X f u/ 1i j t t i j t i j i j t, , 0 1 , , 3 , , , (5)

where BuyLead/TotBuyi, j, t is the percentage of IPO i shares bought by institution j in month t through the lead underwriters of the IPO
i in the total amount of shares bought by the same institution j over month t; AllocationSales/TotSelli, j, t is the percentage of allocated
shares sold by institution j in month t through the lead underwriters of IPO i from the total amount of allocated shares sold, and
SecondarySales/TotSelli, j, t is the percentage of secondary sales of IPO i shares by institution j in month t through the lead underwriters
from total secondary sales of IPO i.13 Month1t is a dummy variable equal to one in month 1 and zero in month 3; Xi, j, t is a vector of
control variables, which include RelVoli, j, t and ExcLeadCommi, j, t; δi, j, ϕi, j, and fi, j capture institution-firm fixed effects; and εi, j, t, εi, j,
t, and ui, j, t are the error terms, which we allow to be correlated within institution j. All ratios are expressed in percentage terms.
Table 5 reports the results.

Columns (1) and (2) suggest that institutions tend to showcase their buy trades to the lead underwriters in the IPO aftermarket.
The percentage of buy trades executed through the lead underwriters is about 17 percentage points greater in month 1 than in month
3. Columns (3) and (4) suggest that institutions tend to hide their secondary sales from the lead underwriters in the IPO aftermarket.
The percentage of secondary sales executed through the lead underwriters is about 5 percentage points smaller in month 1 than in
month 3. Columns (5) and (6) suggest that institutions do not hide their flipping activity and, actually, tend to showcase them to the
lead underwriters in the IPO aftermarket. The percentage of allocation sales executed through the lead underwriters is about 11

Table 4
What drives the Buy/Sell asymmetry: allocation sales vs secondary sales?

Dependent variable: LeadDummy

(1) (2) (3) (4) (5)

(6)

Month 3

Allocation sale dummy 0.12∗∗∗ 0.081∗∗∗ 0.053∗∗∗ 0.046∗∗∗ 0.043∗∗∗ −0.0087
(5.68) (5.60) (4.99) (4.01) (3.39) (−0.40)

Controls No Yes Yes Yes Yes Yes
Institution fixed effects No No Yes Yes No No
Firm fixed effects No No No Yes No No
Institution-firm fixed effects No No No No Yes Yes
Adjusted R2 0.012 0.052 0.14 0.24 0.46 0.50
Observations 20,653 20,653 20,653 20,653 20,653 9853
% Outside [0,1] 0 0.0091 0 0.091 0 0

This table reports the estimation results of several specifications of a linear probability model in a sample of institutional trades in 1361 IPO stocks
issued between 1999 and 2010. The sample includes 20,653 sell trades executed in the first 21 trading days after the issue date (columns (1)–(5))
and 9853 sell trades in the 3rd month after the IPO. The dependent variable is a dummy equal to one if the broker executing the trade is any of the
lead underwriters of the IPO (LeadDummy). Column (1) reports the results of an OLS regression of LeadDummy a dummy variable equal to one if the
sell trade contains an allocation sale and zero otherwise (AllocationSaleDummy). Column (2) introduces several control variables: RelVol is the
number of shares traded by the institution scaled by the number of shares issued; NormalTradeLead is the percentage volume of sell or buy trades in
non-IPO stocks made by the institution through the lead underwriters in a 6-month period prior to the issue; Day is the day in which the trade is
executed, relative to the issue date; ExcLeadComm is the average percentage commission to the lead underwriters minus the average percentage
commission to any other broker paid by sample institutions for their buy or sell trades in the first 21 trading days after the issue date; AllocPerc is the
percentage IPO allocation received by the institution. Columns (3), (4), and (5) introduce institution, firm, and institution-firm fixed effects. All non-
dummy variables are winsorized at the 95% level. Standard errors are clustered at the institution level (t-statistics are in parentheses). Significance
levels are denoted as: * 0.1, ** 0.05, *** 0.01.

13 Since we can compute allocation sales at the daily level, there is some noise when we split an institution’s flipping volume by broker type and
such institution sells shares through both the lead underwriters and other brokers on a given day. In such cases, we split flipping volume pro-
portionally to the total amount sold by broker type on that day.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
12

percentage points greater in month 1 than in month 3. Overall, these results suggest that the buy/sell asymmetry is driven by both
institutions showcasing their buy trades to the lead underwriters and hiding their secondary sales from the lead underwriters.

5. Hiding the breaking of laddering agreements

We suggest a novel reason for why investors might have an incentive to hide their sell trades. An investor that enters in a
laddering agreement a là Hao (2007) receives an IPO allocation and agrees with the lead underwriters to generate additional demand
in the IPO aftermarket by buying shares. As argued by Griffin et al. (2007), this form of laddering helps explaining why investors are
overall net buyers through lead underwriters in the IPO aftermarket. However, investors might have an incentive to break the
laddering agreement if the shares bought in the secondary market are in excess of their optimal holding in the IPO firm. A way to do it
without being caught by the lead underwriters is to sell the shares in excess through any other broker. If investors systematically
break their laddering agreements, then we should observe them simultaneously buying through the lead underwriters and selling
through non‑lead brokers.

The evidence so far points into a “laddering explanation” of investors’ behavior. Column (5) of Table 3 shows that institutional
investors that simultaneously buy and sell a given IPO are more likely to buy than sell through the lead underwriters. Moreover,
Table 5 shows that institutional investors tend to both showcase their buy trades to the lead underwriters and hide their secondary
sales from the lead underwriters. In this Section, we provide more direct evidence of the “laddering explanation” and directly test two
of its predictions. First, if investors hide the breaking of the agreement and use the simple hiding technology considered in this paper,
then they should tend to execute a higher proportion of their sell trades through non‑lead brokers when they buy shares through the
lead underwriters and when they sell secondary shares. Second, if investors break their laddering agreements, then it has to be the
case that they sell the shares that they committed to buy through the lead underwriters.

5.1. Trading volume decomposition

To test these predictions, we decompose trading volume in four parts. Let Vi, j
T be the total number of shares traded by institution j

in IPO i during the first 21 trading days after the issue and let Ni be the number of shares issued in IPO i. The total volume traded can
be written as in Eq. (6):

= + + +
V
N

B
N

F
N

S F
N

B
N

i j
T

i

i j

L

i
i j
T
i
i j
T
i j
T
i

i j
NL

i

, , , , , ,

(6)

where Fi, j
T is the total number of shares of IPO i flipped by institution j during the first 21 trading days, Bi, j

L (Si, j
L) is the number of

shares of IPO i bought (sold) by institution j through the lead underwriters during the first 21 trading days, Bi, j
NL (Si, j

NL) is the
number of shares of IPO i bought (sold) by institution j through brokers other than the lead underwriters during the first 21 trading
days, and BT = BL + BNL (ST = SL + SNL). The third component on the right hand side of the identity, (Si, j

T − Fi, j
T)/Ni, is the

institution’s total volume of “secondary” shares sold, meaning total sales excluding allocations sales, scaled by the number of shares

Table 5
What drives the buy/sell asymmetry: showcasing buy trades versus hiding sell trades.

Dependent variable: BuyLead/TotBuy SecondarySalesLead/TotSell AllocationSalesLead/TotSell

(1) (2) (3) (4) (5) (6)

Month1 18.2∗∗∗ 17.3∗∗∗ −5.41∗∗ −5.30∗∗ 12.4∗∗∗ 11.0∗∗∗

(7.21) (7.16) (−2.38) (−2.12) (7.14) (5.44)
RelVol 0.70 2.38∗∗∗ 2.87∗∗

(1.33) (3.92) (2.56)
ExcLeadComm 33.6 40.5 76.3∗∗

(1.55) (1.28) (2.43)
Constant 20.3∗∗∗ 19.6∗∗∗ 20.4∗∗∗ 18.4∗∗∗ 22.7∗∗∗ 21.7∗∗∗

(12.74) (10.70) (19.40) (14.81) (15.33) (13.76)
Inst-Firm fixed effects Yes Yes Yes Yes Yes Yes
Adjusted R2 0.48 0.48 0.28 0.29 0.50 0.51
Observations 6710 6710 2561 2561 9180 9180

This table reports the estimates of OLS regressions of three dependent variables: (1)BuyLead/TotBuyi, j, t – the percentage of IPO i shares bought by
institution j in month t through the lead underwriters of the IPO i in the total amount of shares bought by the same institution j over month t; (2)
SecondarySalesLead/TotSelli, j, t – the percentage of secondary sales of IPO i shares by institution j in month t through the lead underwriters of IPO i;
and (3)AllocationSalesLead/TotSelli, j, t – the percentage of allocated shares sold by institution j in month t through the lead underwriters of IPO i from
the total amount of allocated shares sold, on the variable Month1t, which is a dummy variable equal to one in month 1 and zero in month 3. We
include the following control variables: RelVol is the number of shares traded by the institution scaled by the number of shares issued; ExcLeadComm
is the average percentage commission to the lead underwriter minus the average percentage commission to any other broker paid by institutions for
their buy trades (Column (1)–(2)) or sell trades (Column (3)–(4)) over a month t. We include institution-firm fixed effects in all specifications. All
ratios are expressed in percentage terms. Standard errors are clustered at the institution level (t-statistics are in parentheses). Significance levels are
denoted as: * 0.1, ** 0.05, *** 0.01.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
13

issued. In order to capture the propensity to sell through brokers other than the lead underwriters, we compute the percentage of
shares of IPO i sold by institution j through non‑lead brokers, Si, j

NL/Si, j
T. Since we are interested in analyzing institutional selling, we

constrain our dataset to institutions that have positive sales (i.e., Si, j
T > 0). We count 9018 institution-firm observations.

Under the laddering explanation, institutions tend to sell shares through non‑lead brokers, while having bought them in the IPO
aftermarket through the lead underwriters. Hence, controlling for how the institution normally trades with the lead underwriters
(NormalTradeLead), we should observe the percentage of shares sold through non‑lead brokers, Si, j

NL/Si, j
T, to be positively correlated

with the relative volume of shares bought through the lead underwriters, Bi, j
L/Ni, and the relative volume of “secondary” shares sold,

(Si, j
T − Fi, j

T)/Ni. These predictions are conditional on the institution j having received some allocation in the IPO i, as institutions
involved in laddering received some allocation in the IPO. Hence, under the laddering motive, these predictions should not hold for
institutions with no allocations. Moreover, they should not hold after the first month of trading, when the incentives for institutions to
hide their sales become weaker.

To test these predictions, we perform a linear projection of the propensity to sell through non‑lead brokers on the trading volume
components, running several specifications of the following regression 7:

= + + + + + + + +
S
S

B
N
S F
N
F
N
B
N

X vi j
NL

i j
T

i j
L

i
i j
T
i j
T
i
i j
T
i
i j
NL

i
i j i j i j

,

,
0 1

,
2

,

,
3

,
4

,
, ,

(7)

where Xi, j is a vector of control variables (which includes NormalTradeLeadi, j and AllocPerci, j), ϕi and φj are firm and institution fixed
effects, and vi, j is the error term, which we allow to be correlated within institution. The laddering motive for hiding predicts γ1 > 0
and γ2 > 0 for institutions that received allocations and trade during the first month after the issue. Table 6, Panel (A), reports the
OLS results. All ratios are multiplied by 100, thus being expressed as percentages. We use institution-clustered standard errors for
inference.14

In Columns (1)–(4) of Table 6, Panel (A), we perform the regression on first-month trading data, including in the sample in-
stitutions that received some allocations (i.e., institutions with AllocPerci, j > 0). Overall results are consistent with the laddering
explanation. The coefficients γ1 and γ2 are positive in all specifications: institutions tend to execute a higher proportion of their sell
trades through non‑lead brokers when they buy more shares through the lead underwriters and when they sell more “secondary”
shares. According to Column (4), a one unit increase in the volume of shares bought through the lead underwriters (volume of
“secondary” shares sold) as a percentage of the amount of shares issued is associated with a 1.24 (2.02) percentage points increase in
the proportion of sell trades executed through non‑lead brokers. Results are also statistically significant at the 1% level in most
specifications. The specification in Column (2), which does not control for fixed effects, shows insignificant or weakly significant
results. Firm fixed effects keep IPO characteristics constant, including the identity of the lead underwriters, which might be relevant
factors affecting both the propensity to sell and the amount of shares bought in the aftermarket through lead underwriters. For
example, some underwriters might have simultaneously a higher proportion of sell trades executed through them and a larger buying
activity from investors than other underwriters, thus making it difficult to detect the laddering hiding motive in specifications (1) and
(2).15 Controlling for IPO fixed effects allows us to keep these factors constant, exploiting within IPO variation. Hence, specifications
(3) and (4) are more suitable tests of the laddering motive for hiding.

Consistent with flipping not being a relevant explanation for the Buy/Sell asymmetry we document, we find that γ3 is negative in
most specifications and statistically significant at the 1% level when controlling for IPO and institution fixed effects: the proportion of
shares sold through non‑lead brokers is lower when institutions flip more of their IPO allocations.

In Column (5) of Table 6, Panel (A), we perform a placebo analysis, including in our sample only the institutions with no IPO
allocations (i.e., institutions with AllocPerci, j = 0). Consistent with the laddering motive for hiding, γ1 and γ2 are not statistically
different from zero for institutions with no allocations; in addition, γ1 enters the regression with a negative sign. In Column (6) of
Table 6, Panel (A), we perform another placebo analysis, running the regression on volumes traded during the third month after the
issue. We include in the sample only institutions that received a positive allocation. Consistent with the laddering explanation for
hiding, γ1 and γ2 are not significantly positive after the first month of trading; both coefficients enter the regression with a negative
sign. In addition, γ1 is statistically significant, consistent with hiding incentives not being at place after the first month.

The remaining volume component, that is the relative amount of shares bought through brokers other than the lead underwriters
(BuyNonLead or Bi, j

NL/Ni), is in general positively correlated with the proportion of sell trades executed through the lead under-
writers, especially in placebo samples. Intuitively, it makes sense: institutions that buy more through non‑lead brokers also tend to
sell more through non‑lead brokers. Noticeably, this positive correlation disappears in specifications (3) and (4), where the laddering
hiding motive becomes an important driver of institutional behavior. Unsurprisingly, the coefficient of NormalTradeLead is negative
and significant in all specifications, including the placebo analyses: the higher the proportion of trades that the institution usually
executes through the lead underwriters, the lower the proportion of sell trades executed through non‑lead brokers in IPOs. AllocPerc
enters the regression with a positive sign, but only during the first month of trading.

For robustness, Table 6, Panel (B), replaces the dependent variable with a dummy equal to one if the number of shares sold

14 In unreported analyses, we allow the error term to be correlated within IPO, clustering standard errors at the firm level. The results are
consistent.
15 In an unreported analysis, we aggregate data at the lead underwriter level and, indeed, we observe a negative correlation between the pro-

portion of sell trades through non‑lead brokers and the volume components of interest, confirming the importance of controlling for IPO fixed effects
in our regressions.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
14

through non‑lead brokers (Si, j
NL) is greater than the number of shares sold through lead brokers (Si, j

L) and zero otherwise. The results
are overall consistent with those in Panel (A).

The laddering motive for hiding produces another testable prediction. If institutions that enter in a laddering agreement break it,
it has to be the case that they sell at least part of the shares that they bought through the lead underwriters. Hence, there should be a
positive correlation between the volume bought through the lead underwriters and the volume of “secondary” shares sold. In order to
test this prediction, we regress secondary sales on the other trading volume components as shown in the Eq. (8):

Table 6
The determinants of selling through brokers other than lead underwriters.

Panel (A): SellNonLead

(1) (2) (3) (4) (5) (6)

No-allocations Month 3

BuyLead 1.12∗ 0.41 1.64∗∗∗ 1.24∗∗∗ −0.37 −2.24∗∗

(1.75) (0.58) (4.60) (3.50) (−0.23) (−2.08)
SecondarySales 1.74∗∗∗ 1.22∗ 3.11∗∗∗ 2.02∗∗∗ 0.92 −0.96

(2.77) (1.78) (3.29) (2.83) (0.42) (−1.01)
AllocationSales 0.23 −1.48∗ −0.14 −1.93∗∗∗ 0.41

(0.28) (−1.87) (−0.14) (−3.15) (0.45)
BuyNonLead 1.55∗∗∗ 1.18∗∗ 0.40 −0.045 1.56∗ 3.30∗∗∗

(2.71) (2.52) (0.80) (−0.11) (1.93) (4.96)
NormalTradeLead −3.81∗∗∗ −3.86∗∗∗ −4.43∗∗∗ −3.96∗∗∗ −2.17∗ −3.12∗∗∗

(−14.61) (−15.06) (−19.52) (−18.52) (−1.95) (−9.04)
AllocPerc 1.99∗∗∗ 1.41∗∗∗ 0.82∗∗∗ 0.24

(6.90) (4.37) (3.21) (1.15)
Constant 75.2∗∗∗ 73.9∗∗∗ 75.4∗∗∗ 62.2∗∗∗ 84.0∗∗ 78.7∗∗∗

(34.32) (33.25) (33.91) (16.55) (2.60) (11.63)
Institution fixed effects No No Yes Yes Yes Yes
Firm fixed effects No No No Yes Yes Yes
Adjusted R2 0.20 0.20 0.32 0.41 0.33 0.34
Observations 8539 8539 8539 8539 479 2421

Panel (B) =1 if Si, j
NL > Si, j

L
(1) (2) (3) (4) (5) (6)
No-allocations Month 3

BuyLead 0.0100 0.0028 0.014∗∗∗ 0.010∗ −0.0070 −0.016
(1.19) (0.30) (2.67) (1.83) (−0.41) (−1.52)

SecondarySales 0.028∗∗∗ 0.023∗∗∗ 0.042∗∗∗ 0.032∗∗∗ 0.016 −0.013
(4.27) (3.10) (4.89) (5.14) (0.61) (−1.20)

AllocationSales −0.0012 −0.019∗∗ −0.0053 −0.023∗∗∗ 0.0027
(−0.14) (−2.23) (−0.51) (−3.32) (0.26)

BuyNonLead 0.013∗∗ 0.0094∗ 0.0010 −0.0034 0.015∗ 0.035∗∗∗

(2.10) (1.80) (0.19) (−0.74) (1.77) (4.76)
NormalTradeLead −0.038∗∗∗ −0.039∗∗∗ −0.045∗∗∗ −0.040∗∗∗ −0.019 −0.030∗∗∗

(−14.46) (−14.96) (−19.92) (−18.86) (−1.30) (−7.62)
AllocPerc 0.020∗∗∗ 0.014∗∗∗ 0.0088∗∗∗ 0.0036

(6.58) (3.99) (3.31) (1.51)
Constant 0.75∗∗∗ 0.74∗∗∗ 0.76∗∗∗ 0.62∗∗∗ 1.05∗∗ 0.76∗∗∗

(34.04) (32.93) (33.51) (15.63) (2.59) (10.34)
Institution fixed effects No No Yes Yes Yes Yes
Firm fixed effects No No No Yes Yes Yes
Adjusted R2 0.18 0.19 0.29 0.38 0.23 0.29
Observations 8539 8539 8539 8539 479 2421

Panel (A) reports the estimates of an OLS regression of SellNonLead – the volume of sales executed through brokers other than the lead underwriter as
a percentage of total sales [Si, j

NL/Si, j
T] – on the trading volume components: BuyLead is the relative number of shares bought through the lead

underwriters [Bi, j
L/Ni]; SecondarySales is the relative volume of sales other than allocation sales [(Si, j

T − Fi, j
T)/Ni]; AllocationSales is the relative

number of shares flipped [Fi, j
T/Ni]; and BuyNonLead is the relative number of shares bought through non‑lead brokers [Bi, j

NL/Ni]. Control variables
are described in Table 1. All ratios are multiplied by 100. Columns (1)–(4) include trades executed during the first month after the issue by
institutions that received an IPO allocation. Column (5) includes trades executed during the first month after the issue by institutions with no IPO
allocations. Column (6) includes trades executed during the third month after the issue by institutions that received an IPO allocation. Panel (B)
reports the estimates of the same regression model, replacing the dependent variable with a dummy equal to one if the number of shares sold
through non‑lead brokers (Si, j

NL) is greater than the number of shares sold through lead brokers (Si, j
L) and zero otherwise. Standard errors are

clustered at the institution level (t-statistics are in parentheses). Significance levels are denoted as: * 0.1, ** 0.05, *** 0.01.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
15

= + + + + + + +
S F

N
B
N

F
N
B
N

Xi j
T

i j
T
i
i j
L
i
i j
T
i
i j
NL

i
i j j i i j

, ,
0 1

,
2
,
3

,
, , (8)

where Xi, j is a vector of control variables (which includes NormalTradeLeadi, j and AllocPerci, j), κj and ηi are institution and firm fixed
effects, and εi, j is the error term, which we allow to be correlated within institution. The laddering motive for hiding predicts θ1 > 0.
Table 7 reports the OLS results. We use institution-clustered standard errors for inference.16

In all columns of Table 7, we perform the regression on first-month trading data, including in the sample institutions that received
some allocations (i.e., institutions with AllocPerci, j > 0). Overall, results are consistent with investors breaking their laddering
agreements. The coefficient θ1 is positive and statistically significant at least at the 5% level in all specifications. Column (4) reports
that institutional investors sell 16% of the shares that they buy through the lead underwriters.

In Column (5) of Table 7, we test whether investors tend to break their laddering agreements more in hot markets than in cold
markets.17 We define the IPO market to be hot if the average underpricing and the number of IPOs are above their median values in a
given year. Accordingly, we build the dummy variable HotMarket and we interact it with BuyLead. Column (5) shows that institu-
tional investors tend to break their laddering agreements significantly more in hot markets.18

Overall, our evidence suggests that, contrary to the conventional view, allocation sales do not seem to be an important motive for
hiding sell trades from the lead underwriters. Instead, we find evidence consistent with the laddering explanation being a relevant
driver of institutional hiding behavior.

6. Alternative explanations and endogeneity issues

6.1. Do lead underwriters charge higher commissions for sell trades?

An alternative explanation to our findings in Table 3 is the following. Underwriters might try to disincentivize selling of IPO
stocks by increasing brokerage commissions selectively on sell trades. If this is the case, some investors might choose to sell through
brokers other than lead underwriters in order to save on commissions without any intention to hide their trade. This would generate
the buy/sell asymmetry in the choice of the broker observed in our regressions even when the null hypothesis of no hiding behavior

Table 7
The determinants of secondary sales.

Dependent variable: SecondarySales

(1) (2) (3) (4) (5)

BuyLead 0.089∗∗ 0.089∗∗ 0.099∗∗ 0.16∗∗∗ 0.071∗∗

(2.43) (2.53) (2.47) (4.26) (2.42)
BuyLead × HotMarket 0.15∗∗∗

(2.93)
AllocationSales 0.025∗∗∗ 0.026∗∗ 0.027∗∗ −0.041 −0.039

(3.16) (2.58) (2.55) (−1.57) (−1.61)
BuyNonLead 0.27∗∗∗ 0.27∗∗∗ 0.27∗∗∗ 0.27∗∗∗ 0.27∗∗∗

(3.50) (3.54) (3.96) (5.56) (5.55)
NormalTradeLead 0.0025∗∗∗ 0.0025∗∗∗ 0.0016∗∗∗ 0.0012∗ 0.0015∗∗

(3.51) (4.14) (2.95) (1.74) (2.27)
AllocPerc −0.00095 0.0022 0.010 0.011

(−0.21) (0.38) (1.01) (1.01)
Constant −0.028∗∗ −0.026 −0.030∗ −0.0054 −0.0049

(−2.45) (−1.60) (−1.96) (−0.12) (−0.11)
Institution fixed effects No No Yes Yes Yes
Firm fixed effects No No No Yes Yes
Adjusted R2 0.46 0.46 0.49 0.52 0.52
Observations 10,704 10,704 10,704 8539 8539

This table reports the estimates of an OLS regression of SecondarySales – the volume of sales other than allocation sales scaled by the number of
shares issued [(Si, j

T − Fi, j
T)/Ni] – on other trading volume components: BuyLead is the relative number of shares bought through the lead

underwriters [Bi, j
L/Ni]; AllocationSales is the relative number of shares flipped [Fi, j

T/Ni]; and BuyNonLead is the relative number of shares bought
through non‑lead brokers [Bi, j

NL/Ni]. Control variables are described in Table 3. All ratios are multiplied by 100. All columns include trades
executed during the first month after the issue by institutions that received an IPO allocation. Column (5) introduces in the regression the interaction
term BuyLead × HotMarket, where HotMarket is a dummy variable equal to one if the average underpricing and the number of IPOs are above their
median values in a given year and zero otherwise. Standard errors are clustered at the institution level (t-statistics are in parentheses). Significance
levels are denoted as: * 0.1, ** 0.05, *** 0.01.

16 In unreported analyses, we allow the error term to be correlated within IPO, clustering standard errors at the firm level. Results are consistent.
17 We thank an anonymous referee for suggesting this analysis.
18 The existing literature shows that allocation sales tend to be more pronounced in hot markets. In unreported analyses, we confirm that this is the

case also in our sample.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
16

holds, thus invalidating our conclusions. Broadly consistent with this argument, Ellis (2006) finds evidence of bookrunners offering
better terms on buy trades in a sample of Nasdaq IPOs.

We show that this explanation is unlikely to drive our results. First, in Table 3 we control for the average commission required by
lead underwriters in excess of the commission required by other brokers (ExcLeadComm) in our regressions. The variable Ex-
cLeadComm is computed for buy trades and sell trades separately. Hence, it controls for the effect of the potential differential
treatment that lead underwriters give to different trades on the investors’ probability of choosing a lead underwriter as a broker.
Second, we investigate the commissions story more deeply. If the commission story is a concern, then we should observe lead
underwriters to require higher brokerage commissions for sell trades relative to at least one of these benchmarks: i) lead underwriters’
commissions for buy trades in the IPO aftermarket; ii) lead underwriters’ commissions for sell trades few months after the IPO; iii)
commissions of brokers other than the lead underwriters for sell trades in the IPO aftermarket. Fig. 3 plots the average trading
commission paid to the lead underwriters for buying trades (dark grey line) and sell trades (light grey line) by month from the issue
date. Commissions are scaled by the dollar volume traded and 95% confidence intervals are reported with dotted lines.

Table 8 shows commissions for lead underwriters and other brokers by side of the trade.
If anything, average brokerage commissions of lead underwriters are higher for buy trades than for sell trades during the IPO

aftermarket. Moreover, average brokerage commissions for sell trades tend to be somewhat higher several months after the IPO than
during the first month after the issue date. Table 8 reports difference of means tests for the percentage trading commissions paid to
lead underwriters and to any other broker during the first month after the IPO. The table shows that sell trade commissions do not
significantly differ among broker types. They do differ, however, for buy trades: lead underwriters require higher commissions for
buy trades than other brokers.

Hence, empirical evidence does not support the commissions story: lead underwriters do not increase commissions on sell trades
to disincentivize selling of IPO stocks. In fact, there is some evidence that they might be doing the opposite: commissions on buy

0
.0

2
.0

4
.0

6
.0

8
.1

.1
2

.1
4

.1
6

.1
8

.2
.2

2

A
ve

ra
g
e
%

c
o
m

m
is

si
o
n

1 2 3 4 5 6 7 8 9 10 11 12

Months after IPO

lleSyuB

95% conf. interval 95% conf. interval

Fig. 3. Average trading commissions. This figure plots the average trading commission paid to the lead underwriters for buying trades (dark grey
line) and sell trades (light grey line) by month from the issue date. Commissions are scaled by the dollar volume traded. 95% confidence intervals
are reported with dotted lines.

Table 8
Commissions by broker type and side of the trade.

All others Lead UWs Diff. of means

% sell commissions 0.0886 0.0895 −0.000869
(−0.472)

% buy commissions 0.109 0.122 −0.0124∗∗∗

(−6.814)

This table reports difference of means tests for the percentage trading commission paid to lead underwriters and to any other broker
by financial institutions in IPOs issued between 1999 and 2010. The sample includes 20,107 sell trades and 24,469 buy trades
executed during the first month after the issue date. The percentage trading commission paid by an institution to the broker is
winsorized at the 95% level. Standard errors are corrected for unequal variances (t-statistics are in parentheses). Significance levels
are denoted as: * 0.1, ** 0.05, *** 0.01.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
17

trades seem to be particularly high in the IPO aftermarket.19 If anything, this could actually work against finding results in favor of
the hide-and-sell hypothesis.

6.2. Adressing endogeneity concerns

The decision to sell is endogenous. Institutions that decide to sell an IPO stock might differ from institutions that buy the IPO
under several dimensions that might be correlated with their choice of the broker. In an ideal experiment, we would like to observe
how institution j would have traded IPO i if, for a given trade, it would have switched trade side. Since in one of our specifications we
exploit within institution-IPO variation, we rule out sources of endogeneity that are constant within institution-IPO pairs (e.g., the
relationship between an investor and the lead underwriters of an IPO): we observe the same institution buying and selling the same
IPO stock through different brokers, often over the same trading day.20 Even though this might seem reasonably close to the ideal
experiment mentioned above, we cannot exclude that some trade-varying unobserved factors jointly drive investors’ selling and
broker choices within institution-IPO pairs. However, it is hard to find a trade-level factor that would make the buy/sell asymmetry in
the choice of the broker vanish, given that we control for commissions, volume, and day in Table 3. Another source of potential
criticism is related to the fact that our estimation in column (5) of Table 3 exploits variation in the trading side within institution-IPO
pairs. In our sample, more than 50% of the observations do not exhibit variation within institution-IPO; i.e., the investor is either
buying or selling the IPO stock. Hence, in column (5) we use information of a specific subsample of observations. This is unlikely to be
a relevant issue for our purposes, as the specification of column (5) still serves the goal of detecting hiding behavior. Moreover, the
coefficient of Sell in the regressions of Table 3 is fairly stable across different specifications, including column (5). Overall, even
though we do not claim that we estimate a causal effect, endogeneity concerns are unlikely to qualitatively change our conclusions
about the buy/sell asymmetry in the choice of the broker.

For robustness, we also seek for a source of exogenous variation in the selling decision of financial institutions. Funds in distress,
which experience large outflows, tend to decrease their existing positions (Coval and Stafford (2007)), including their IPO holdings.
Hence, institutions that manage funds in distress are more likely to sell IPO shares. This suggests a candidate instrument for financial
institutions’ selling decisions: the number of funds in distress managed by the institution. This instrument is plausibly exogenous in
this setting, as funds’ distress is likely unrelated with the probability of the institution trading through the lead underwriters of a
given IPO.21 Moreover, underwriters usually allocate shares to fund families, which then decide how to distribute them within the
family (Ritter and Zhang (2007)). This lowers the scope for direct links between distressed funds and the institution’s choice to trade
through the underwriters in the IPO aftermarket.

We use clientcode-clientmgrcode pairs in the Abel Noser Solutions’ database to identify distinct funds managed by our sample
institutions.22 We define a fund to be in distress in a given month if two conditions are met: 1) more than 99% of its trading volume in
non-IPO stocks is due to sell trades; 2) the monthly dollar volume traded by the fund in non-IPO stoks is above the 90th percentile.
The idea is that funds with large selling volumes are likely experiencing a fire-sale event. Our institution-level distress variable,
LnDistressFundsi, j, is the natural logarithm of the number of funds in distress managed by institution j during the month in which the
IPO i is made. We use it as instrumental variable for Sell. Table 9 reports the 2SLS results, which are qualitatively consistent with our
baseline regressions.

The results of Table 9 have to be taken cautiously. We acknowledge that they are sensitive to the choice of the dollar volume
threshold: the instrument becomes weak when we set lower thresholds, such as the 50th or the 75th percentiles of the monthly
volume traded. Even though it make sense that only large transaction volumes are related to fire-sales events that could be relevant in
the first stage regression, we cannot justify the choice of a specific volume threshold to build our variable. Table 9 suggests that
endogeneity concerns do not seem to qualitatively change our conclusions, but the potential weakness of the instrument does not
allow us to make strong causal statements.

19 Understanding why lead underwriters’ commissions on buy trades are high in the IPO aftermarket goes beyond the scope of this paper. Though
difficult to reconcile with Ellis (2006)’s result, we notice that our evidence is broadly consistent with the literature on quid-pro-quo agreements in
IPOs, which suggest that investors might get preferential treatment in the allocation of IPOs in exchange of paying excessive brokerage commissions
to the lead underwriters (e.g., Reuter (2006)). Our finding is also broadly consistent with Griffin et al. (2007), who finds that there is more net
buying through the bookrunners in IPOs in which the bookrunner charges higher trading costs.
20 We observe an institution j trading the same stock i through several distinct brokers b during the same trading day t for 23% of the observations.
21 A theoretically possible channel that could invalidate the exogeneity assumption is that institutions with several funds in distress might be

institutions with little or no connections with important brokers, which also underwrite IPOs. Under this “connection” argument, institutions with
distressed funds would tend to trade more with non‑lead brokers regardless of the side of the trade. We find no evidence in this direction: the
number of distressed funds of an institution is not significantly correlated with its normal number of trades executed through the lead underwriters
in non-IPO stocks (NormalTradeLead).
22 From our talks with ANcerno it became clear that clientmgrcode identifies individual funds, fund managers, or separately managed accounts

(see also Hu et al. (2018)). Clientmgrcode is provided by the client and may change over time, ANcerno however reassured us that clientmgrcode
remains unchaged within each a batch of data provided by the client (identified by the lognumber). For this reason, we follow Eisele et al. (2020)
and use a couple clientcode-clientmgrcode to separate among individual funds.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
18

7. Is the hiding strategy effective?

In this section we test whether selling through non‑lead brokers allow institutional clients to be less penalized in future IPO
allocations from the lead underwriter. According to Chemmanur et al. (2010), institutions that flip their shares receive fewer allo-
cations in subsequent IPOs. We develop our predictions following their findings, and investigate whether institutions that employ the
hiding strategy and sell their shares through non‑lead brokers, manage to circumvent underwriters’ penalty in terms of share allo-
cations. It is important to assess if the hiding strategy is indeed: 1) beneficial for the institution; 2) costly for the IPO process, as
allocations might be suboptimal. To test our predictions, we estimate Arellano-Bond regressions with difference-GMM of IPO allo-
cations on the selling transactions executed by institutions lead underwriters and non‑lead brokers using model 9:

= +
+ + … + + + +

AvgPercAlloc AvgSecondarySalesLead
SecondarySalesNonLead X[ ]

j t j t

j t j t j t j t

, 0 1 , 1

2 , 1 , 1 , (9)

where AvgAllocPerc is the average percentage IPO allocation received by the institution in a 6-months period as a portion of total
shares offered in an IPO. Our variables of interest are L. AvgSecondarySalesLead (L. AvgSecondarySalesNonLead) that is lagged 6-
months average relative share volume of secondary sales executed through lead brokers (non‑lead brokers). Other variables included
in the model are the average lagged 6-month trading volume components scaled by the number of shares issued: L. AvgBuyLead (L.
AvgBuyNonLead) is the average relative number of shares bought through the lead underwriters (non‑lead brokers); L. AvgAlloca-
tionSalesLead (L. AvgAllocationSalesNonLead) is the relative number of allocated shares sold through lead brokers (non‑lead brokers).
Xj, t−1 is a vector of control variables: NormalTradeLeadj, t−1 and the lagged AvgAllocPerc. δj are institution fixed effects, τt are semi-
annual fixed effects, and ϵi, t is the error term, which we allow to be correlated within institution. We use Arellano-Bond instead of
OLS because lagged variables are arguably correlated with the error term in a dynamic panel regression with fixed effects. The lagged
dependent variable, L. AvgPercAlloc, is correlated with the error term by construction in a fixed-effects regression. L. Secondar-
ySalesLead and the other lagged trading variables are also likely correlated with the error term as they are arguably influenced by L.
AvgPercAlloc. Hence, we use difference-GMM to estimate the model, which instruments the first differences in the lagged variables

Table 9
IV regression with distressed funds.

(a) First stage.

(1) (2) (3) (4)

LnDistressFunds 0.11∗∗∗ 0.13∗∗∗ 0.054∗∗∗ 0.030∗∗∗

(3.17) (3.98) (8.02) (4.22)
Controls No Yes Yes Yes
Institution fixed effects No No Yes Yes
Firm fixed effects No No No Yes
F-stat 10.0 70.3 96.4 .
Adjusted R2 0.0058 0.067 0.18 0.31
Observations 44,576 44,576 44,576 44,576

(b) Second stage.

(1) (2) (3) (4)

Sell −1.32∗∗∗ −1.12∗∗∗ −0.56∗∗∗ −1.35∗

(−3.92) (−5.26) (−3.02) (−1.65)
Controls No Yes Yes Yes
Institution fixed effects No No Yes Yes
Firm fixed effects No No No Yes
Observations 44,576 44,576 44,576 44,576

This table reports the estimation results of several specification of a 2SLS regression in a sample of institutional trades in 1361 IPO stocks issued
between 1999 and 2010. The dependent variable is a dummy equal to one if the broker executing the trade is any of the lead underwriters of the IPO
(LeadDummy). The sample includes 44,576 trades executed in the first 21 trading days after the issue date. Panel (A) reports the first stage results;
Panel (B) reports the second stage results. Column (1) reports the results of a 2SLS regression of LeadDummy on a dummy variable equal to one if the
institution is selling and zero otherwise (Sell), instrumented by LnDistressFunds. LnDistressFunds is the natural logarithm of the number of funds
managed by the institution that are in distress. A fund is defined to be in distress if: 1) its total volume traded in all stocks in the IPO month is more
than 25 million dollars and 2) its total dollar netbuy in all stocks divided by the total volume traded is less than −0.99. Column (2) introduces
several control variables: RelVol is the number of shares traded by the institution scaled by the number of shares issued; NormalTradeLead is the
percentage volume of sell or buy trades in non-IPO stocks made by the institution through the lead underwriters in a 6-month period prior to the
issue; Day is the day in which the trade is executed, relative to the issue date; ExcLeadComm is the average percentage commission to the lead
underwriters minus the average percentage commission to any other broker paid by sample institutions for their buy or sell trades in the first 21
trading days after the issue date; AllocPerc is the percentage IPO allocation received by the institution. Columns (3) and (4) introduce institution and
firm fixed effects. All non-dummy variables are winsorized at the 95% level. Standard errors are clustered at the institution level (t-statistics are in
parentheses). Significance levels are denoted as: * 0.1, ** 0.05, *** 0.01.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
19

with their levels at past times.
Table 10 reports the results on the regression analysis of IPO allocations received by institutions in our sample. All ratios are

multiplied by 100. The results confirm that institutions selling IPO shares through lead underwriters receive fewer allocations (as a
fraction of the number of shares issued). According to Column (4), the coefficient on L. AvgSecondarySalesLead is negative and
significant. A one unit increase in the average volume of secondary shares sold through lead underwriters is associated with a 6.42
percentage points decrease in the proportion of allocated shares to the institution in the following 6-month period. The coefficient on
L. AvgSecondarySalesNonLead is insignificant, confirming the hypothesis that institutions hiding their sells with non‑lead brokers are
unlikely to be penalized for their selling.

In Column (2) we control for NormalTradeLead – the % volume of sell or buy trades in non-IPO stocks made by the institution
through the lead underwriters in a 6-month period prior to the issues. Columns (3) and (4) include semi-annual fixed effects. Columns
(1)–(3) use one-lag instruments, Column (4) includes one-lag and two-lag instruments. The results are qualitatively similar in all
specifications.

Overall, our findings support the idea that the choice of the broker for selling IPO shares can be an effective way to bypass the
underwriters’ attention and avoid the penalty in terms of future allocations. Selling IPO shares in the amount that does not exceed the
amount of shares bought by the institution in the aftermarket allows institutions to avoid a penalty in terms of share allocation that a
lead underwriter may impose on institutions otherwise. The incentives to punish and hide selling trades seem to be present only for
secondary sales, they do not seem to be pronounced for allocations sales.

8. Conclusion

We document that institutional investors are less likely to sell than buy through lead underwriters in the aftermarket of IPOs
issued between 1999 and 2010 in the United States. The probability of trading through a lead underwriter during the first month after
the issue is about 6 percentage points less for sell trades than for buy trades. This result holds when controlling for important
determinants of the choice to trade with a lead underwriter, such as the relationship between the institution and the lead under-
writers, and is robust to institution, IPO, and institution-IPO fixed effects. We find that the documented buy/sell asymmetry varies
consistently with hiding incentives: it is stronger when the aftermarket demand for IPO stocks is weaker (i.e., in cold IPOs), it does not

Table 10
Is the hiding strategy effective?

Dependent variable: AvgAllocPerc

(1) (2) (3) (4)

L.AvgSecondarySalesLead −6.69∗∗∗ −6.48∗∗∗ −6.42∗∗∗ −4.53∗∗∗

(−3.12) (−2.90) (−2.82) (−3.25)
L.AvgSecondarySalesNonLead −0.38 −0.44 −0.44 −0.24

(−1.03) (−1.14) (−1.12) (−0.59)
L.AvgAllocationSalesLead 0.76 0.68 0.70 0.84

(1.31) (1.11) (1.12) (1.60)
L.AvgAllocationsSalesNonLead 0.12 0.12 0.11 −0.11

(0.79) (0.78) (0.71) (−0.58)
L.AvgBuyLead 0.23 0.21 0.21 0.13

(1.43) (1.31) (1.32) (0.90)
L.AvgBuyNonLead 0.35∗∗ 0.34∗∗ 0.34∗∗ 0.14

(2.56) (2.47) (2.44) (1.11)
L.AvgPercAlloc 0.020 0.017 0.019 0.089

(0.19) (0.16) (0.17) (0.78)
NormalTradeLead 0.00075 0.00079 0.00099∗∗

(1.46) (1.55) (2.31)
Institution fixed effects Yes Yes Yes Yes
Time fixed effects No No Yes Yes
N. instrument lags 1 1 1 2
AR(2) (p-value) 0.55 0.62 0.58 0.19
Hansen overid. test (p-value) . . . 0.079
Observations 3696 3696 3696 3696

This table reports the estimates of Arellano-Bond regressions estimated with difference-GMM. The dependent variable, AvgAllocPerc, is the average
percentage IPO allocation received by the institution in a 6-months period. The regressors are the average lagged 6-month trading volume com-
ponents scaled by the number of shares issued: L. AvgBuyLead (L. AvgBuyNonLead) is the average relative number of shares bought through the lead
underwriters (brokers other than lead underwriters); L. AvgSecondarySalesLead (L. AvgSecondarySalesNonLead) is the average relative share volume
of secondary sales through lead brokers (brokers other than lead underwriters); L. AvgAllocationSalesLead (L. AvgAllocationSalesNonLead) is the
relative number of allocated shares sold through lead brokers (brokers other than lead underwriters). In Column (2) we control for NormalTradeLead
– the % volume of sell or buy trades in non-IPO stocks made by the institution through the lead underwriters in a 6-month period prior to the issues.
All ratios are multiplied by 100. Columns (3) and (4) include semi-annual fixed effects. Columns (1)–(3) use one-lag instruments, Column (4)
includes one-lag and two-lag instruments. Standard errors are clustered at the institution level (t-statistics are in parentheses). Significance levels are
denoted as: * 0.1, ** 0.05, *** 0.01.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
20

hold after the first month of trading, and it does not hold for a matched sample of non-IPO stocks. Moreover, we find that the
asymmetry is not only driven by institutions’ showcasing their buy trades to the lead underwriters, but also by institutions’ avoiding
sell trades through the lead underwriters.

We rule out potential alternative explanations for the buy/sell asymmetry. Our findings are not driven by underwriters’ strate-
gically setting differential brokerage commissions to disincetivize sell trades. Moreover, our evidence suggests that the buy/sell
asymmetry is not only driven by investors buying more through the lead underwriters, but also by investors selling less through the
lead underwriters. Finally, potential endogeneity concerns are unlikely to make the buy/sell asymmetry vanish and we find evidence
consistent with this view in an IV setting, using a proxy for institutional fire-sales as exogenous shock for the decision to sell an IPO.

We investigate what drives the buy/sell asymmetry. Contrary to the conventional view, we find that flipping IPO allocations is not
an important motive for hiding sell trades from the lead underwriters. This is reasonable, as underwriters have access to reports that
document investors’ flipping activity. We propose and test a novel explanation of the buy/sell asymmetry in the choice of the broker
in IPO aftermarket. We find evidence in favor of this explanation. Institutional investors that agree with the underwriters to buy
additional shares in the IPO aftermarket in exchange of receiving allocations (a practice known as “laddering”), might break this
agreement by hiding-and-selling the shares bought in the aftermarket through other brokers. Consistent with the laddering ex-
planation, we find that: i) the percentage of sell volume executed through non‑lead brokers is higher when institutional investors buy
more shares through the lead underwriters in the IPO aftermarket and when institutional investors execute more “secondary” sales
(i.e., sales other than allocation sales); and ii) the volume of “secondary” shares sold in the aftermarket by an institution is positively
correlated with its buy volume through the lead underwriters.

Finally, we show that hiding sell trades is an effective strategy to circumvent underwriters’ monitoring mechanisms: the more
institutions hide their sell trades, the less they are penalized in subsequent IPO allocations.

Our evidence sheds light on how hiding incentives affect institutions’ choice of their broker in the IPO aftermarket and stimulates
further research to investigate how the incentives of IPO investors may influence the IPO allocation process.

Appendix A. Ancerno data description

This data appendix provides a detailed description of ANcerno data inspired by years of exchanges with the data provider, as well
as the explanation of the mapping procedure we use to produce the dataset. Our sample consists of institutional transaction-level
trading data from ANcerno/Abel Noser Solutions. ANcerno clients (money managers, pension plan sponsors, and brokers) provide
their trading data to ANcerno to monitor their transaction costs. Each client has a unique numerical identifier in the dataset (cli-
entcode) that allows distinguishing among the three types of clients. Nevertheless, the identity of the client is anonymized. We use
clientcode mainly as a technical variable in several matching exercises we perform. One of the main variables of interest to us is
managercode by ANcerno attributed to the trading institutions. After receiving data from their clients, ANcerno assigns a code to each
manager within the client’s portfolio. Because several clients may use the same manager, in order to associate a manager with a
particular client, ANcerno codes the manager in relation to a client. Another reason they do this is because different clients may
report the same managers differently (e.g., different spelling). By coding the manager in relation to a customer, ANcerno can trace
back the manager to a particular client. Managers can be grouped across clients by using the managercode. ANcerno uses the same
logic in mapping executing brokers in the data. The main ANcerno trading dataset includes clientcode, clientmgrcode and clientbkrcode
we use in our matching process.

ANcerno data is subscription specific. For a limited period of time in 2010, ANcerno provided its academic subscribers with the
identification table “MasterManagerXref” that includes managercodes with the associated names of trading institutions. The file we
got includes 1088 unique institutions. Additional identification files “ManagerXref” and “BrokerXref” include clientcode, cli-
entmgrcode, and clientbkrcode variables allowing to link fund families and brokers to the trading data in the main ANcerno dataset.
The mapping procedure we use is shown in detail in Fig. A1. Fig. A1 shows the two-step matching we use to get the managing
company name on the main ANcerno trading dataset. In the first step, we merge “ManagerXref” file on the main ANcerno table using
clientcode-clientmgrcode as a key identifier. We further link the resulting table with the managing company name (variable manager)
from the “MasterManagerXref” file on provided (managercodes).

We use the S12type5 Table provided by Wharton Research Data Services (WRDS) to map management companies from SEC 13F
filings to mutual funds reporting their holdings in the Thomson Reuters S12 Mutual fund holdings database. S12 data contains funds
associated to fund families in 13F. Finally, we match ANcerno institutions with the institutions from S12/13F Thomson Reuters
database. We manually match managing company names from both datasets: variable manager in ANcerno and mgrco in S12 data-
base.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
21

Main ANcerno trades database

clientcode (ANcerno’s unique client identifier)
clientmgrcode (as reported by the client)
clientbkrcode (as reported by the client)

Master

ManagerXref file from ANcerno

managercode (unique asset manager
identifier by ANcerno)
manager (unique asset manager name)

ManagerXref file from ANcerno

clientcode
clientmgrcode
managercode
reportedmanager (asset manager name as
reported by the client)

on managercode

BrokerXref file from ANcerno

clientcode
clientbkrcode
broker (unique broker identifier in ANcerno)
brokername

Thomson Reuters 13F filings

mgrcocd (asset manager numerical identifier)
mgrco (asset manager name)

manager and
mgrco hand-
matched

Thomson Financial Security Data Company
(SDC)

IPO underwriters’ (brokers’) names

broker names
hand-matched

Fig. A1. Mapping money managers and brokers across databases (key identifier(s) for the match are provided in bold).

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
22

Appendix B. Allocation Sales

0
2

4
6

8
1

0
1

2

C
u
m

u
la

tiv
e
A

llo
ca

tio
n
S

a
le

s
(%

o
f
S

h
a
re

s
Is

su
e
d
)

1 2 3 4 5 6 7 8 9 10 11 12
Months after IPO

sOPI toHsOPI llA

sOPI dloCsOPI kaeW

95% conf. intervals

Fig. B2. Allocation sales by IPO type. This figure plots the average cumulative percentage of allocated IPO shares sold, scaled by the number of
shares offered, by month from the issue date. 95% confidence intervals are reported with dotted lines. The black line report the average for the
whole sample of IPOs. The grey lines break the averages down for hot IPOs (highest tercile of Underpricing), weak IPOs (middle tercile of
Underpricing), and cold IPOs (lowest tercile of Underpricing).

Appendix C. Further robustness tests

We use a linear probability model (LPM) in our regressions in Table 3 and we estimate its coefficients via OLS. We justify the use
of OLS because the unconditional probability of trading with the lead underwriters is not at the boundaries of the unit interval (it is
0.292). Moreover, a very small proportion of the predicted probabilities of trading with the lead underwriters fall outside the [0, 1]
interval and only one specification out of five suffers of this problem (see Table 3). Horrace and Oaxaca (2006) show that OLS is
unbiased and consistent if all the observations have true predicted probabilities within the unit interval. We cannot know the true
predicted probabilities, but our predicted probabilites do not raise suspect that potential OLS biasedness and inconsistency are
relevant concerns in our setting. Finally, a LPM is desirable in our situation because it allows us to control for fixed effects without
incurring in the incidental parameter problem and it estimates marginal effects. For robustness, we also run logit regressions and get
rid of the fixed effects by means of a conditional logit model. Table C1 reports the estimation results, which are overall consistent
with Table 3.23

Almost 50% of the IPOs in our sample are issued during the internet bubble period. We replicate our regression analysis excluding
IPOs issued in 1999 and 2000 and report our findings in Table C2. The results are similar to those in Table 3.

We use LeadDummy as dependent variable in Table 3. This implies that we pool in the same group of brokers the other syndicate
members and brokers that do not belong to the underwriting syndicate. For robustness, we replicate our regression analysis using
UWDummy as dependent variable. UWDummy takes the value of 1 if the trade is executed through any of the underwriters of the IPO
and zero otherwise. Table C3 shows that results are overall consistent with Table 3. If anything, they are slightly weaker, consistent
with hiding incentives being mainly related to lead underwriters.

We test the robustness of our results in Table 10 to an alternative specification. We regress AvgPercAllocj, t on AvgSecondar-
ySalesDiffj, t−1, which is given by the difference between AvgSecondarySalesNonLeadj, t−1 and AvgSecondarySalesLeadj, t−1, AvgAllo-
cationSalesDiffj, t−1, which is given by the difference between AvgAllocationSalesNonLeadj, t−1 and AvgAllocationSalesLeadj, t−1, and
AvgBuyDiffj, t−1, which is given by the difference between AvgBuyNonLeadj, t−1 and AvgBuyLeadj, t−1. Table C4 reports the results,
which are overall consistent with Table 10.

23 We cannot estimate all the specifications because of computational problems with the conditional logit model. In unreported analyses, we also
run the LPM while trimming observations with predicted probabilities outside the unit interval, as suggested by Horrace and Oaxaca (2006). If
anything, our results get stronger.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
23

Table C1
Buy-Sell asymmetry: logit and conditional logit specifications.

Dependent variable LeadDummy

(1) (2) (3)

Sell −0.29∗∗ −0.40∗∗∗ −0.37∗∗

(−2.07) (−3.64) (−2.44)
RelVol 0.31∗∗ 1.04∗∗∗

(2.57) (7.60)
NormalTradeLead 0.041∗∗∗ 0.019

(4.92) (0.78)
Day −0.059∗∗∗ −0.069∗∗∗

(−6.09) (−5.07)
ExcLeadComm −0.87 −1.56∗

(−1.15) (−1.79)
AllocPerc −0.0011

(−0.05)
Constant −0.76∗∗∗ −0.62∗∗∗

(−5.12) (−4.08)
Institution-Firm fixed effects No No Yes
Pseudo R2 0.0036 0.041 0.078
Observations 44,576 44,576 21,693

This table reports the coefficient estimates of logit and conditional logit models in a sample of institutional trades in 1361 IPO stocks issued
between 1999 and 2010. The dependent variable is a dummy equal to one if the broker executing the trade is any of the lead underwriters of
the IPO (LeadDummy). The original sample includes 44,576 trades executed in the first 21 trading days after the issue date. Column (1) reports
the results of a logit regression of LeadDummy on a dummy variable equal to one if the institution is selling and zero otherwise (Sell). Column
(2) introduces several control variables: RelVol is the number of shares traded by the institution scaled by the number of shares issued;
NormalTradeLead is the percentage volume of sell or buy trades in non-IPO stocks made by the institution through the lead underwriters in a
6-month period prior to the issue; Day is the day in which the trade is executed, relative to the issue date; ExcLeadComm is the average
percentage commission to the lead underwriters minus the average percentage commission to other brokers paid by sample institutions for
their buy or sell trades in the first 21 trading days after the issue date; AllocPerc is the percentage IPO allocation received by the institution.
Column (3) controls for institution-firm fixed effects by means of a conditional logit model. All non-dummy variables are winsorized at the
95% level. Standard errors are clustered at the institution level (t-statistics are in parentheses). Significance levels are denoted as: * 0.1, **
0.05, *** 0.01.

Table C2
Dropping 1999–2000 period.

Dependent variable LeadDummy
(1) (2) (3) (4) (5)

Sell −0.052∗∗ −0.067∗∗∗ −0.060∗∗∗ −0.055∗∗∗ −0.050∗∗

(−2.27) (−3.48) (−3.78) (−3.43) (−2.34)
Controls No Yes Yes Yes Yes
Institution fixed effects No No Yes Yes No
Firm fixed effects No No No Yes No
Institution-Firm fixed effects No No No No Yes
Adjusted R2 0.0032 0.063 0.15 0.24 0.40
Observations 24,109 24,109 24,109 24,109 24,109
% Outside [0,1] 0 0.0016 0.00040 0.072 0

This table reports the estimation results of several specification of a linear probability model in a sample of institutional trades in 698 IPO stocks
issued between 2001 and 2010. The dependent variable is a dummy equal to one if the broker executing the trade is any of the lead underwriters of
the IPO (LeadDummy). The sample includes 24,109 trades executed in the first 21 trading days after the issue date. Column (1) reports the results of
an OLS regression of LeadDummy on a dummy variable equal to one if the institution is selling and zero otherwise (Sell). Column (2) introduces
several control variables: RelVol is the number of shares traded by the institution scaled by the number of shares issued; NormalTradeLead is the
percentage volume of sell or buy trades in non-IPO stocks made by the institution through the lead underwriters in a 6-month period prior to the
issue; Day is the day in which the trade is executed, relative to the issue date; ExcLeadComm is the average percentage commission to the lead
underwriters minus the average percentage commission to other brokers paid by sample institutions for their buy or sell trades in the first 21 trading
days after the issue date; AllocPerc is the percentage IPO allocation received by the institution. Columns (3), (4), and (5) introduce institution, firm,
and institution-firm fixed effects. All non-dummy variables are winsorized at the 95% level. Standard errors are clustered at the institution level (t-
statistics are in parentheses). Significance levels are denoted as: * 0.1, ** 0.05, *** 0.01.

T. Nefedova and G. Pratobevera Journal of Corporate Finance 64 (2020) 101627
24

Table C3
Including all underwriter syndicate members.

Dependent variable UWDummy

(1) (2) (3) (4) (5)

Sell −0.066∗∗ −0.080∗∗∗ −0.051∗∗∗ −0.049∗∗ −0.044∗

(−2.07) (−3.33) (−2.90) (−2.59) (−1.85)
Controls No Yes Yes Yes Yes
Institution fixed effects No No Yes Yes No
Firm fixed effects No No No Yes No
Institution-Firm fixed effects No No No No Yes
Adjusted R2 0.0047 0.053 0.14 0.25 0.42
Observations 44,576 44,576 44,576 44,576 44,576
% Outside [0,1] 0 0 0 0.042 0

This table reports the estimation results of several specification of a linear probability model in a sample of institutional trades in 1361
IPO stocks issued between 1999 and 2010. The dependent variable is a dummy equal to one if the broker executing the trade is any of the
underwriters of the IPO (UWDummy). The sample includes 44,576 trades executed in the first 21 trading days after the issue date. Column
(1) reports the results of an OLS regression of UWDummy on a dummy variable equal to one if the institution is selling and zero otherwise
(Sell). Column (2) introduces several control variables: RelVol is the number of shares traded by the institution scaled by the number of
shares issued; NormalTradeUW is the percentage volume of sell or buy trades in non-IPO stocks made by the institution through the
underwriters in a 6-month period prior to the issue; Day is the day in which the trade is executed, relative to the issue date; ExcUWComm
is the average percentage commission to the underwriters minus the average percentage commission to other brokers paid by sample
institutions for their buy or sell trades in the first 21 trading days after the issue date; AllocPerc is the percentage IPO allocation received
by the institution. Columns (3), (4), and (5) introduce institution, firm, and institution-firm fixed effects. All non-dummy variables are
winsorized at the 95% level. Standard errors are clustered at the institution level (t-statistics are in parentheses). Significance levels are
denoted as: * 0.1, ** 0.05, *** 0.01.

Table C4
Robustness check: is the hiding strategy effective?

(1) (2) (3) (4)

L.AvgSecondarySalesDiff 0.91∗∗ 0.93∗∗ 0.94∗∗ 0.55∗∗

(2.34) (2.31) (2.27) (2.13)
L.AvgAllocationSalesDiff −0.091 −0.063 −0.077 −0.20

(−0.44) (−0.30) (−0.36) (−0.98)
L.AvgBuyDiff −0.062 −0.071 −0.072 0.035

(−0.52) (−0.59) (−0.60) (0.35)
L.AvgPercAlloc 0.040 0.034 0.036 0.12

(0.35) (0.30) (0.31) (0.96)
NormalTradeLead 0.0010∗ 0.0011∗∗ 0.0011∗∗

(1.93) (2.03) (2.38)
Institution fixed effects No No Yes Yes
N. instrument lags 1 1 1 2
AR(2) (p-value) 0.22 0.24 0.23 0.11
Hansen overid. test (p-value) . . . 0.20
Observations 3696 3696 3696 3696

This table reports the estimates of Arellano-Bond regressions estimated with difference-GMM. The dependent variable, AvgAllocPerc, is the average
percentage IPO allocation received by the institution in a 6-months period. The regressors are L. AvgSecondarySalesDiff, which is given by the
difference between L. AvgSecondarySalesNonLead and L. AvgSecondarySalesLead, L. AvgAllocationSalesDiff, which is given by the difference between
L. AvgAllocationSalesNonLead and L. AvgAllocationSalesLead, and L. AvgBuyDiff, which is given by the difference between L. AvgBuyNonLead and L.
AvgBuyLead. L. AvgBuyLead (L. AvgBuyNonLead) is the lagged average relative number of shares bought through the lead underwriters (brokers other
than lead underwriters); L. AvgSecondarySalesLead (L. AvgSecondarySalesNonLead) is the lagged average relative share volume of secondary sales
through lead brokers (brokers other than lead underwriters); L. AvgAllocationSalesLead (L. AvgAllocationSalesNonLead) is the lagged average relative
number of allocated shares sold through lead brokers (brokers other than lead underwriters). In Column (2) we control for NormalTradeLead – the %
volume of sell or buy trades in non-IPO stocks made by the institution through the lead underwriters in a 6-month period prior to the issues. All
ratios are multiplied by 100. Columns (3) and (4) include semi-annual fixed effects. Columns (1)–(3) use one-lag instruments, Column (4) includes
one-lag and two-lag instruments. Standard errors are clustered at the institution level (t-statistics are in parentheses). Significance levels are denoted
as: * 0.1, ** 0.05, *** 0.01.

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• Do institutional investors play hide-and-sell in the IPO aftermarket?

Introduction
Data and summary statistics
IPO data
Institutional trading data in the IPO aftermarket
Identifying institutional IPO allocations sales
Identifying institutional IPO allocations
Buy/sell asymmetry
Baseline results
Incentives in cold IPOs
Placebo tests
What drives the buy/sell asymmetry?
Allocation sales versus secondary sales
Showcasing buy trades versus hiding sell trades
Hiding the breaking of laddering agreements
Trading volume decomposition
Alternative explanations and endogeneity issues
Do lead underwriters charge higher commissions for sell trades?
Adressing endogeneity concerns
Is the hiding strategy effective?
Conclusion
Ancerno data description
Allocation Sales
Further robustness tests
References