Department of Mathematical and Computational Sciences
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Browsing Department of Mathematical and Computational Sciences by Author "Chagwiza, Wilbert"
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Item Open Access Fundamental Analysis for Stocks using Extreme Gradient Boosting(2022-11-10) Gumani, Thanyani Rodney; Chagwiza, Wilbert; Kubjana, TlouWhen it comes to stock price prediction, machine learning has grown in popularity. Accurate stock prediction is a very difficult activity as financial stock markets are unpredictable and non-linear in nature. With the advent of machine learning and improved computational capabilities, programmed prediction methods have proven to be more effective in stock price prediction. Extreme gradient boosting(XGBoost) is the variant of the gradient boosting machine. XGBoost, an ensemble method of classification trees, is investigated for the prediction of stock prices based on the fundamental analysis. XGBoost outperformed the competition and had higher accuracy. The developed XGBoost model proved to be an effective model that accurately predicts the stock market trend, which is considered to be much better than conventional non-ensemble learning techniques.Item Open Access Modelling volatility, equity risk and extremal dependence of the BRICS Stock Markets(2022-07-15) Mukhodobwane, Rosinah Mphedziseni; Sigauke, Caston; Chagwiza, Wilbert; Garira, WinstonWith the use of empirical data of the BRICS (Brazil, Russia, India, China, and South Africa) stock markets, this thesis focuses on solving three main nancial and investment issues involving returns volatility, risk and extremal dependence via robust statistical modelling. The rst issue involves modelling nancial returns volatility (when the true distribution is unknown) using the univariate GARCH model under the assumptions of seven error distributions. The ndings, using two of the error distributions, show that the Chinese market has the highest volatility persistence, followed by the South African, Russian, Indian and Brazilian markets in that order. For risk modelling and analysis, the ndings show that the Russian market has the highest risk level, followed by the South African, Chinese, Brazilian and Indian markets, respectively. For the extremal dependence modelling, using the bivariate point process and conditional multivariate extreme value (CMEV) models, the ndings show varied levels of low extremal dependence structure whose outcomes are highly bene cial to investors, portfolio managers and other market participants who are interested in maximising their investment returns and nancial gains. However, it is observed that the point process was able to model many more extreme observations or exceedances that contribute to the likelihood estimation and it gives more information than the threshold excess method of the CMEV model.Item Open Access Predicting an Economic Recession Using Machine Learning Techniques(2022-11-10) Molepo, Mashaka Ruth; Chagwiza, Wilbert; Kubjana, Tloufew economic downturns were predicted months in advance. This research has the ability to give the best performing models to assist businesses in navigating prior recession periods. The study address the subject of identifying the most important variables to improve the overall performance of the algorithm that would effectively predict recessions. The primary aim of this study was to improve economic recession prediction using machine learning (ML) techniques by developing an inch-perfect and efficient prediction model in order to avoid greater government deficits, growing inequality, significantly decreased income, and higher unemployment. The study objective was to establish the relevant method for addressing imbalance data with suitable features selection strategy to enhance the performance of the machine learning algorithm developed. Furthermore, artificial neural network(ANN) and Random Forest (RF) were used in predicting economic recession using ML techniques. This study would not have been possible without the publicly available data from the online open source Kaggle, which provided ordinal categorical data for the specific data utilized. The major findings of this study were that the ML algorithm RF performed better at recession prediction than its rival ANN. Due to the fact that two ML algorithms in this research were employed , further ML tools can be used to improve the statistical components of the study.Item Open Access A stochastic programming framework for financial intermediaries liquidity in South Africa(2015-05) Chagwiza, Wilbert; Garira, W.; Moyo, S.See the attached abstract below