Electronic Theses and Dissertations (Masters)

Permanent URI for this collectionhttps://hdl.handle.net/10539/38006

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Applying Machine Learning to Model South Africa’s Equity Market Index Price Performance
(University of the Witwatersrand, Johannesburg, 2023-07) Nokeri, Tshepo Chris; Mulaudzi, Rudzani; Ajoodha, Ritesh
Policymakers typically use statistical multivariate forecasting models to forecast the reaction of stock market returns to changing economic activities. However, these models frequently result in subpar performance due to inflexibility and incompetence in modeling non-linear relationships. Emerging research suggests that machine learning models can better handle data from non-linear dynamic systems and yield outstanding model performance. This research compared the performance of machine learning models to the performance of the benchmark model (the vector autoregressive model) when forecasting the reaction of stock market returns to changing economic activities in South Africa. The vector autoregressive model was used to forecast the reaction of stock market returns. It achieved a mean absolute percentage error (MAPE) value of 0.0084. Machine learning models were used to forecast the reaction of stock market returns. The lowest MAPE value was 0.0051. The machine learning model trained on low economic data dimensions performed 65% better than the benchmark model. Machine learning models also identified key economic activities when forecasting the reaction of stock market returns. Most research focused on whole features, few models for comparison, and barely focused on how different feature subsets and reduced dimensionality change model performance, a limitation this research addresses when considering the number of experiments. This research considered various experiments, i.e., different feature subsets and data dimensions, to determine whether machine learning models perform better than the benchmark model when forecasting the reaction of stock market returns to changing economic activities in South Africa.
Developing a Bayesian Network Model to Predict Students’ Performance Based on the Analysis of their Higher Education Trajectory
(University of the Witwatersrand, Johannesburg, 2024-08) Ramaano, Thabo Victor; Jadhav, Ashwini; Ajoodha, Ritesh
The Admission Point Score (APS) metric, utilised as a response to admit prospective students for an academic course, may appear effective in determining student success. In reality, almost 50% of students admitted to a science programme in a higher education institution failed to meet all the requirements necessary to complete the programme during the period of 2008 and 2015. This had a direct impact on the overall graduation throughput. Thus, the focus of this research was geared towards the adoption of a probabilistic graphical approach to advocate its mechanism as a viable alternative to the APS metric when determining student success trajectories at a higher education level. The purpose of this approach was to provide higher education institutions with a system to monitor students’ academic performance en-route to graduation from a probabilistic and graphical point of view. This research employed a probability distribution distance metric to ascertain how close the learned models were to the true model for varying sample sizes. The significance of these results addressed the need for knowledge discovery of dependencies that existed between the students’ module results in a higher education trajectory that spans three years.
Flood Susceptibility Modeling in the uMhlatuzana River Catchment using Computer Vision-Based Deep Learning Techniques
(University of the Witwatersrand, Johannesburg, 2024-10) Chirindza, Jonas; Ajoodha, Ritesh; Knight, Jasper
In this study, covolutional neural networks (CNN) models are employed for flood susceptibility modeling in the uMhalatuzana River catchment in KwaZulu-Natal, South Africa. The CNN models, including 1D-CNN, 2D-CNN, and 3D-CNN, pro-vide a detailed assessment of flood vulnerability in the region. The models use di- verse spatial information, such as topography, land use, and hydrological features, to estimate the likelihood of flooding in different areas of the catchment. The flood susceptibility maps within the uMhalatuzana River catchment, classified into five risk zones namely, ‘very low’, ‘low’, ‘moderate’, ‘high’ and ‘very high’ susceptibility zone, serve as proactive instruments for risk mitigation and disaster management. The 1D-CNN model displays strong overall performance in flood susceptibility modeling, evident in key metrics such as accuracy, precision, recall, area under curve (AUC) score, and F1-score. The results suggest that the model effectively captures patterns in the input data, emphasizing its potential for flood susceptibility modeling. Moreover, the 2D-CNN model outperforms the 1D-CNN, achieving higher values when evaluated using various performance metrics. Finally, the 3D-CNN model outperformed both the 1D-CNN and 2D-CNN, emphasizing its predictive abilities in flood susceptibility modelling. The flood susceptibility maps produced by the 1D-CNN model, shows that most of the study area exhibits very low flood susceptibility (96.4%), with localized areas of higher susceptibility, particularly in the very high-risk category (2.53%). The 2D CNN model demonstrates a more diverse risk distribution, with a substantial portion having very low susceptibility (74.19%) and significant areas of higher risk, notably in the very high-risk category (10.93%). The 3D-CNN model emphasizes a spatial pattern where a large portion has very low susceptibility (84.10%), but with a concentration of high and very high-risk areas, comprising 12.34% of the total area. Finally, the consistent identification of higher risk susceptibility areas enhances the robustness of the assessments. The models’ high accuracy and detailed risk assessments provide valuable tools for decision-makers, urban planners, and emergency response teams in the uMhalatuzana River catchment. The precision of the models facilitates informed strategies for flood risk management, including targeted interventions such as improved drainage systems and early warning systems.
Regime Based Portfolio Optimization: A Look at the South African Asset Market
(University of the Witwatersrand, Johannesburg, 2023-09) Mdluli, Nkosenhle S.; Ajoodha, Ritesh; Mulaudzi, Rudzani
Financial markets change their properties (i.e mean, volatility, correlation, and distribution) with time. However, traditional portfolio optimization strategies seek to create static, all weather portfolios oblivious to this and current economic conditions. This produces portfolios that are unable to predict events with excessive skewness and kurtosis. This research investigated the difference in portfolio percentage return, of portfolios that incorporate regimes against one that does not. HMMs, binary segmentation, and PELT algorithms were used to identify regimes in 7 macro-economic features. These regimes, with regimes identified by the SARB, were incorporated into Markowitz’s mean-variance optimization technique to optimize portfolios. The base portfolio, which did not incorporate regimes, produced the least return of 761% during the period under consideration. Portfolios using HMMs identified regimes, produced, on average, the highest returns, averaging 3211% whilst the portfolio using SARB identified regimes returned 1878% during the same period. This research, therefore, shows that incorporating regimes into portfolio optimization increases the percentage return of a portfolio. Moreover, it shows that, although HMMs, on average, produced the most profitable portfolio, portfolios using regimes based on data-driven techniques do not always out-perform portfolios using the SARB identified regimes.