The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization

Modelling stock market behaviour with machine learning techniques

Abstract: This thesis investigates the performance of Machine Learning (ML) techniques in predicting stock market trends and returns. Previous studies (e.g. Bonga-Bonga and Muteba Mwamba, 2011) have shown that stock market returns can be predicted by fundamental variables. However, one of the most important question in finance is whether fundamental variables can explain changes in stock market trends (ups and downs). To answer this question, this thesis simultaneously use regression techniques to predict stock market returns and classification techniques to predict stock market trends/directions, using the fundamental variables. Four fundamental variables are considered for this study namely; price earning (PE) ratio, dividend yield (DY), inflation rate as measured by the CPI, and interest rate spread1 (SPREAD). The study is carried out in three different stock markets namely; the US S&P500 stock index, the UK FTSE100 index, and the South Africa ALSI index. Monthly stock market and fundamental variable data are collected from February 1996 to August 2017. The empirical analysis is therefore done in two frameworks. In the first framework the thesis deals with the prediction of stock market trends using ML classification techniques whereas in the second framework the thesis deals with the prediction of stock market returns using ML regression techniques. The ML techniques used for classification and regression analysis includes Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbours (KNN). Each ML technique is thereafter compared to a specific benchmark model in order to carry out a model-to-model comparison. The first benchmark is the Linear Discriminant Analysis (LDA) model used in the classification framework, and the second is the ARIMA-X model used in regression framework. Furthermore, the thesis makes use of the F-score measure, the confusion matrix and the ROC Curve to evaluate the performance of models in the classification framework, and the predicted mean square error in the regression framework. Using the ROC curve, the confusion matrix and the F-Score in the out-sample space, our results show that the Random Forest technique predicts stock market trends better than any other classification technique. Using the predicted mean square error, the SVM is found to be predicting stock market returns better than any other ML regression technique. The thesis makes use of the variable importance analysis in order to identify fundamental variables that drive stock market trends. Our findings show that inflation rate plays an insignificant role in driving stock market trends/directions while the PE ratio and the dividend yield are the significant drivers of stock market trends. Overall, our ML techniques have been found to forecast both stock market trends and returns better than traditional models.M.Com. (Financial Economics

Essays on international financial markets interdependence

This thesis consists of five chapters. Chapter one showcases the analysis of the three empirical studies presented in this thesis. Chapter two provides broad literature review. Chapter three investigates the transmission of information between developed and developing countries. In particular, foreign exchange market’s return and volatility spillovers channel. A fundamental question is whether the magnitude of return and volatility spillovers is bidirectional between developed and developing countries. In this chapter, I investigate the “static and dynamic” return and volatility spillovers transmission across developed and developing countries. Quoted against the U.S. dollar, I study twenty-three global currencies over 2005 – 2016. Focusing on the spillover index methodology, the generalised VAR framework is employed. The findings indicate no evidence of bidirectional return and volatility spillovers between developed and developing countries. However, a unidirectional volatility spillover from developed to developing countries is highlighted. Furthermore, the findings also document significant bidirectional volatility spillover within the European region (Eurozone and non-Eurozone currencies) with the British Pound (GBP) and the Euro (EUR) as the most significant transmitters of volatility. The findings reiterate the prominence of volatility spillover to financial regulators.Chapter four contributes to the out-of-sample’s stock returns forecasting problem and investigates both its econometric underpinnings and predictability. According to Welch and Goyal (2008) there is little or zero evidence of the effectiveness of both (in-sample and out-of-sample) models in predicting equity returns. Thus, using daily data, this chapter examines whether the U.S. S&P stock exchange follow a random walk process, which required by market efficiency. We use a model-comparison approach, which compares an ex-post forecasts from a naïve model against those obtained from numerous alternative models such as ARIMA models, random walk without drift and Simple exponential smoothing.Chapter five assesses the dynamic behaviour of credit and house prices in advanced modern economies over the last three decades. The analysis is based on the GMM panel VAR, and Fixed-effects estimated using annual data for the G7 countries over the period 1980-2017. Thus, the empirical analysis of this chapter attempts to offer some contribution to the contemporaneous issues affecting the macroeconomic performance by investigating the dynamic behaviour of credit, house prices, GDP, consumption, and loans to the private sector. The main finding here is the strong link between the dynamic behaviour of the aforementioned variables in advanced modern economies. Finally, chapter six concludes and discusses the research implications and future study

Yield Curves and Macro Variables Interactions and Predictions

This research is based on the yield curves and five macro variables, namely equity indices, FX rates, central banks’ policy rates, inflation rates and the GDP growth rates, for nine different markets, from different geographical regions. Our aim was to identify common trends in yield curves and macro variables behaviors, from two perspectives: the interaction and predictive power of the variables. Firstly, we studied the interaction between yield curves and macro variables based on: Granger Causality, Impulse Response Function and Variance Decomposition. Afterwards, we predicted yield curves based on ANN Regression Multitask learning, and lastly, we predicted our five macro variables based on three different ANN Classifiers, in order to generalize and present results that are not specific to a country, or region, or model. The most persistence trend, amongst the variables, was the association between the GDP, inflation, policy rate and the Level. Based on Multitask learning, we achieved a 1-mnth average yield curves prediction accuracy of 80.2% for all yield maturities and studied markets. Additionally, we found out that increasing the hidden nodes led to overfitting the data, hence, we recommend the use of a simple neural network architecture. Furthermore, we designed a model that computes the optimum number of hidden nodes based on: the number of input/output nodes and forecasted months ahead. The Independent Variable Contribution analysis increased the weight of Slope on average for all markets. Weighted KNN caused a deterioration in the prediction accuracy of macro variables, and K of KNN increased with the horizon forecasted. In terms of predictive power of the variables, the yield curve on its own had predictive powers over long term equity markets, and the policy rate seemed to be affected by macro variables in the short term. Furthermore, the inflation and GDP were dominated by their own past values