金融Essay格式要求：A FinTech Strategy for Momentum and Volatility Effect in Emerging Markets Stocks
本文是金融專業(yè)的Essay范例，題目是“A FinTech Strategy for Momentum and Volatility Effect in Emerging Markets Stocks（新興市場股票的動量和波動效應的金融科技策略）”，金融技術(FinTech)可以描述為“用于支持或協(xié)助銀行和金融服務的計算機程序和其他技術”，Hassnian, A(2017)。金融技術(FinTech)指改善金融服務交付和使用并使之自動化的新技術。金融科技新技術、機器學習/人工智能被用于預測金融決策的行為分析，并正在改變金融業(yè)務;創(chuàng)造新穎的服務和產品。
The project這個項目
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).
本研究的目的是通過創(chuàng)建一個FinTech模型(人工智能，機器學習)來研究動量，波動效應和新興市場之間的關系，并將其與傳統(tǒng)的統(tǒng)計模型(假設檢驗)進行比較。
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文獻綜述
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.
市場異常是指股票的表現(xiàn)與其假設價格軌跡之間的差異，建立了有效市場假說(EMH)。有效市場假說并不總是成立，金融市場異常現(xiàn)象的出現(xiàn)已經證明了這一點。動量異常效應可能是最難解釋和表示的。波動與不確定性相關，并對市場有影響。
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.
有效市場假說(EMH)受到波動和動量效應的挑戰(zhàn)。因為如果投資者能夠預測市場的動向，他們可能會獲得額外的優(yōu)勢。然而，研究和結論表明，這在很大程度上取決于人類在特定市場上的經驗。
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研究技術
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.
將在本項目中實施的方法將在項目期間開發(fā)并更詳細地解釋。然而，一個簡短的總結將被解釋為研究計劃的方法，將被用于研究。
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.
多層感知器神經網絡(MLP)是一類前饋人工神經網絡。多層感知器有時被稱為“香草”神經網絡，特別是當它們只有一個隱藏層時。在這個項目中，各種拓撲將試圖獲得一個適合研究方法的好拓撲。
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.
在這個項目中，我們將調查2015年1月1日和2019年12月31日的新興市場股票，利用金融科技策略評估其動量和波動性預測。數據將被仔細清理，以去除外來值，并使用將為研究構建的模型評估性能。該項目將使用先進的函數算法程序，如Phyton和R，并與其他新程序將允許執(zhí)行該項目進行和執(zhí)行。
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.
雖然最近在這個金融領域進行的研究很少，但該項目很重要，因為它將嘗試使用金融科技策略來研究新興市場股票的動量效應和波動性，并將提供一個新的視野，幫助更準確地預測和分析市場。創(chuàng)造聰明的模型，收集不同的人工智能/機器學習模型，以協(xié)調不同復雜的市場。因此，可以通過提高金融服務的效率和質量，削減成本，或早或晚建立金融技術的新場景和方法，為金融領域的學術界和社會做出貢獻。金融技術現(xiàn)在是一個非常重要的話題，因為它有助于金融領域的新發(fā)現(xiàn)和創(chuàng)造知識，這將有助于未來的研究。
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