Theses and Dissertations

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Browsing Theses and Dissertations by Author "Chagwiza, W."

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    Credit Card Fraud Detection using Boosted Random Forest Algorithm
    (2023-10-05) Mashamba, Thanganedzo Beverly; Chagwiza, W.; Garira,W.
    Financial fraud is a growing concern with far-reaching concerns in financial institutions, government, and corporate organizations, leading to substantial monetary losses. The primary cause of financial loss is credit card fraud; it affects issuers and clients, which is a significant threat to the business as clients will run to their competitors, wherein they will feel secure. Solving fraud problems is beyond human capability, so financial institutions can utilize machine learning algorithms to detect fraudulent behaviour by learning through credit card transactions. This thesis develops the boosted random forest, integrating an adaptive boosting algorithm into a random forest algorithm, such that the performance of a model is improved in predicting credit card fraudulent transactions. The confusion matrix is used to evaluate the performance of the models, wherein random forest, adaptive boosting and boosted random forest were compared. The results indicated that the boosted random forest outperformed the individual models with an accuracy of 99.9%, which corresponded with the results from confusion matrix. However random forest and adaptive boosting had 100% and 99% respectively, which did not correspond to the results on confusion matrix, meaning the individual models need to be more accurate. Thus, by implementing the proposed approach to a credit card management system, financial loss will be reduced to a greater extent.
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    Equity Risk Premium Prediction using Machine Learning Techniques and its Application in Portfolio Optimisation
    (2026-05-19) Muthai, Costa; Chagwiza, W.; Mphephu, N.; Netshikweta, R.
    This research project delves into the domain of finance and investment management, with a focus on accurately predicting equity risk premiums and their subsequent application in portfolio optimisation - in the South African Context. The primary objective is to establish a robust framework that employs machine learning techniques to predict equity premiums effectively and utilise these predictions for portfolio optimisation. The study aims to implement two efficient variable selection techniques from the field of machine learning—specifically, Elastic Net Regression and Random Forests—to identify the most influential variables capturing variations in equity returns. These selected variables will then be integrated into Support Vector Regression and eXtreme Gradient Boosting models to predict equity returns and assess their predictive efficacy. Spanning a comprehensive dataset from January 1990 to December 2022, the study sources data from various repositories, including the IRESS Expert database, Federal Reserve Economic Data, and Yahoo Finance. This dataset encompasses monthly closing values for 10 companies listed on the Johannesburg Stock Exchange, 31 economic variables, and 10 international indices, facilitating a robust and multifaceted analysis. Methodologically, the research integrates machine learning techniques within an Arbitrage Pricing Theory framework. It rigorously assesses models using subsets of variables against those utilising the entire set, thoroughly examining their performance metrics and robustness. Key findings highlight the effectiveness of Random Forests in selecting pivotal variables for constructing both linear and non-linear equity premium prediction models. Notably, models trained with selected subsets exhibit superior performance, underscoring the critical importance of variable selection for heightened predictive accuracy. Significant recommendations stemming from this study emphasise the importance of diversification strategies in mitigating risks associated with concentrated positions. Furthermore, it stresses the necessity for a balanced approach in portfolio strategies to optimise outcomes. The research suggests a potential avenue for further exploration: a deeper investigation into sophisticated ensemble methods or advanced neural network architectures. This exploration aims to enhance model accuracy, generalisability, and interpretability, essential for establishing trust in complex models within the domain of finance and investment management.
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    Share Price Prediction for Increasing Market Efficiency using Random Forest
    (2022-11-10) Mbedzi, Tshinanne Angel; Chagwiza, W.; Garira, W.
    The price of a single share of a collection of sell-able shares, options, or other financial assets, shall be the price of a share price. The share price is unpredictable since it primarily depends on buyers’ and sellers’ expectations. Share is a primary and secondary market equity security. In this study we will use machine learning techniques to predict the share price for increasing market efficiency. In addition, it is important for us to build a models to create appropriate features to improve the performance of the models. The random forest and the recurrent neural network will be used to achieve this. To fix class imbalance, we analyse preprocessing of the data set, like the selection of the features using filter and wrapper methods and selected oversampling techniques. The model’s performance will be evaluated using Mean absolute error (MAE), Mean square error (MSE), Root mean square error (RMSE), Relative MAE (rMAE), and Relative RMSE (rRMSE). The performance of the RNN and Rf algorithms was compared for the prediction of the closing price. The Rf model was found to be the best model for predicting the stock price (closing price). This research project together with its findings will have an impact in increasing market efficiency. This will also promote potential economic growth.