The project
Financial technology (FinTech) can be describes as “Computer programs and other technologies used to support or assist banking and financial services”, Hassnian, A (2017). Financial technology (FinTech) describes new tech that improve and automate the delivery and use of financial services. FinTech new technologies, machine learning/artificial intelligence are used to predict behavioural analytics for financial decisions and is transforming the business of finance; creating novel services and products.
The purpose of the research is to investigate the relationship between momentum, volatility effect and emerging markets by focusing in create a FinTech model (Artificial Intelligence, Machine Learning) and compare it with traditional statistical model (Hypothesis Test).
The project will address both theoretical and empirical point of view of the new FinTech strategy for momentum and volatility effect in emerging market stocks. From a methodological point of view, it will make use of the vanguard standard parametric and semi-parametric techniques and the programming implementation of Artificial Intelligence and Machine Learning algorithms, such Python, cloud-based Linux and R. Furthermore, an empirical analysis will be implemented based on techniques such random forest regression to prove the fit of the strategies. Although, researcher have attempted to analysed momentum phenomena and volatility using traditional statistical regression models for their studies, some of their conclusions are unconvincing due the lack of prediction ability. Hence, this project is going to satisfy that prediction ability using FinTech strategy models as a predictor to enhance the decision-making trading. The research is going to be organized as follows: 1) theoretical background of the study that is going to cover several aspects regarding momentum effect, volatility and FinTech strategies in the emerging markets. 2) The data is going to be presented for the study and the preparatory process is going to be carry out on the data as well as the descriptive statistics and artificial intelligence / machine learning strategy. 3) Methodology for this research is going to be adapted for the study. 4) Results are going to be presented. 5) Discussion of the study is going to be followed up with the final section; 6 ) Conclusion.
Research question
 The questions proposed for the research project:
–          How the momentum volatility and effect prediction in emerging markets stocks could challenge the Efficient Market Hypothesis?
–          How efficient are FinTech strategies for volatility and momentum effect as a constructed prediction model?
–          Which model is more efficient the statistical or the proposed FinTech strategy for the emerging market stock?
Objectives
Objective 1. To examine the indicators which drive momentum and volatility effect in the emerging markets stocks.
Objective 2. To determine the relationship between momentum and volatility effect.
Objective 3. To undertake a best Fintech strategy and identify the best performing model within the emerging markets stocks and compare it with the traditional statistical regression model.
Objective 4. To ensure the development of a practical and robust framework for adopting the best Fintech strategy model within the emerging markets stocks.
Literature review
Market anomaly refers to the difference in stock’s performance from its assumed price trajectory, as establish efficient market hypothesis (EMH). The efficient market hypothesis not always hold true and is have been proved by the appearance of financial market anomalies. The momentum anomaly effect is probably the most difficult to explain and represent. Volatility is associated with uncertainty and has implications for the market.

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Momentum and Volatility effects are an interesting phenomenon in the stock markets. The momentum effect states that stocks which have performed well in the past would continue to perform well. Furthermore, stocks which have performed poorly in the past would continue to perform badly. The evidence for momentum has been found across international equities in developed and numerous other classes (Asness, Moskowitz, & Pedersen, 2013). Volatility is a statistical measure of the degree of variation in their trading price observed over a period. The more dramatic the price swings are in that instrument, the higher the level of volatility, and vice versa. According with Zhixi Li and Vincent Tam (2018), momentum effect means that the stock that have perform well will probably continue to outperform those that have performed poorly in the past in the future. Relevant studies have been conducted in this topic, however, researchers stated that stock markets have varying degree of momentum, reversal effect and volatility. Santamaria, R., observed the momentum effect in Latin American emerging markets.
Efficient Market Hypothesis (EMH), have been challenged by the volatility and momentum effects. Because investors may take extra advantage if they can predict the movement of the market. However, studies and concluded that this are highly dependent on human experience upon a specific market.
With the advance in new technologies such Artificial Intelligence (AI), new methods could be used as an alternative statistical tool to predict these phenomena and make comparisons regarding its efficiency and accuracy.  Machine Learning is an application of Artificial Intelligence (AI) that study algorithms and statistical model that computer system use to perform a specific task, relying on patterns and inference instead. Machine learning is capable of automatically recognizing potentially useful patterns in financial data according with Li, Z.; Tam, V.
According with Lingaraja K. (2014), the emerging markets consist of retail investors and other stake holders who would expect to get higher benefits for their investments taking higher risk. According with Morgan Stanley Capital International (MSCI), the emerging markets are group into three categories; Americas, Europe, Middle East and Africa, and Asia. Investing in emerging markets are treated as highly volatile and therefore have a great growth potential. 
Research techniques
The methodology that it will be implemented in this project is going to be developed and explained in more detail during the project. However, a brief summary is going to be explained for the research proposal for the methodology that is going to be used for the study.
Breiman (2001) introduced the random forest (RF) algorithm as an ensemble approach that can also be thought of as a form of nearest neighbour predictor. Decision Trees (DT) is a random forest machine learning technique. Decision Trees (DT) algorithms is an approach that uses a set of binary rules to calculate a target class or value. Given training data, decision tree can learn decision rules inferred from the data features during the training process.
Support Vector Machine (SVM) are a supervised learning models with associated learning algorithms that analyse data used for classification and regression analysis. Support Vector Machine (SVM), is known as one of the powerful machine learning algorithms.
Multilayer Perceptron Neural Network (MLP), is a class of feedforward artificial neural network. Multilayer perceptron is sometimes referred to as “vanilla” neural networks, especially when they have single hidden layer. In this project, various topologies are going to try to acquire a good one that fit in the research methodology.
Long Short-Term Memory Neural Network (LSTM), is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, Long Short-Term Memory has feedback connections. LSTM is expected to be a suitable algorithm for financial prediction.
In this project, we are going to investigate the emerging markets stocks from 01/01/2015 and 31/12/2019 to evaluate their momentum and volatility prediction using FinTech strategies.  The data is going to be cleaning carefully to remove exotic values and evaluate the performance using the model that is going to be constructed for the research. The project is going to be conducted and perform using the advance function algorithms programs such Phyton and R, and with other new program that will allow to perform the project.
The research proposal is going to be developed in three years from October 2020 at the City University of London, CASS Business School. During the first year a theoretical framework is going to be conducted. In the second year, a prediction model is going to be constructed for momentum effect and volatility in emerging markets using FinTech strategy such Artificial Intelligence / Machine Learning. Additionally, data is going to be collected and clean in order to be able to be implemented in the model. In the third year, results and conclusions of the research are going to be presented. On the other hand, papers are going to be written about the topic and in finance in order to support the research, as wells as assisting to conferences to present and learn about new findings in the financial area that could contribute to the project. During the project, some teaching assistant duties are going to be done to enrich the academic work for this project.
Although few research have conducted in recent times in this field of finance, the project is important because is going to attempt the momentum effect and volatility in emerging market stock using FinTech strategies and going to provide a new horizon helping to predict and analyse  the market more accurately, creating clever models that gathering different Artificial Intelligence / Machine Learning models to harmonize different intricate markets. Hence, could contribute to the academia and the society in the finance field by improving efficiency and quality of financial services, cutting costs and sooner or later establish FinTech new scenarios and approaches. Financial Technology is a very important topic nowadays because contributes to new findings in finance and creating knowledge that it’s going to help for future research.
References and bibliography.

Asness, C., Moskowitz, T., and Pedersen, L. 2013. Value and Momentum Everywhere. The Journal of Finance, 68(3), 929–985.
Breiman, Leo. 2001. Random Forests. Machine Learning 45: 5–32.
Hassnian, Ali. 2017. Conference: International Conference on Business, Economics and Finance (icbef). At University Brunei Darussalam.
Lingarja, Kasilingam.  2014. The Stock Market Efficiency of Emerging Markets: Evidence from Asia Region. Department of Commerce and Financial Studies, Bharathidasan University. India, 158.
Muga, L.; Santamaría, R. The momentum effect in Latin American emerging markets. Emerg. Mark. Financ. Trade 2007, 43, 24–45.
Stulz, R.; Brown, G.; Bartram, S. 2011. Why are U.S. stocks more volatile? Charles A. Dice Center for Research in Financial Economics. Fisher College of Business.
Zhixi Li and Vincent Tam. 2018. A Machine Learning View on Momentum and Reversal Trading. Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.