Ravele, T.Sigauke, C.Ndogmo, J. C.Nengovhela, Vhukhudo2026-06-172026-06-172026-05-19Nengovhela, V. 2026. A comparative evaluation of machine learning models for stock price prediction and uncertainity estimation. . .https://univendspace.univen.ac.za/handle/11602/3198M.Sc. in e-ScienceDepartment of Mathematical and Computational SciencesThis study compares machine learning models for stock price prediction and uncertainty estimation using high-frequency one-minute stock data. The research looks at how different models perform across developed and emerging markets, which helps with model selection for practical financial forecasting. Four models were tested for point forecasting: Random Forest (RF), Gradient Boosting (GB), Multi-Layer Perceptron (MLP), and a hybrid stacking ensemble composed of multiple base learners. For uncertainty quantification, three interval prediction methods were used: Bootstrap Residuals, Quantile Regression Forests (QRF), and Conformalised Quantile Regression (CQR). The analysis used one-minute stock price data from Microsoft Corporation (MSFT) as a developed market example and Standard Bank Group (SBK.JO) as an emerging market example, covering the period from 3rd to 26th September 2025. The results show that GB performed best for point forecasts in both markets. For MSFT, GB had RMSE of 0.2875 and MAE of 0.1869, while for SBK.JO it achieved RMSE of 25.9248 and MAE of 14.3638. Statistical tests using the Diebold-Mariano and Giacomini-White frameworks confirmed that GB significantly outperformed the other models. For interval prediction, QRF gave sharper intervals in the relatively stable developed market, while CQR achieved better coverage in the more volatile emerging market. The Hybrid Stacking model showed some advantages in volatile conditions but didn’t consistently beat well-tuned individual models. These findings suggest that ensemble methods like GB are still very effective for financial forecasting, and that uncertainty quantification methods should be chosen based on market volatility. The study provides practical guidance for selecting forecasting methods depending on market conditions and data characteristics, which should help both researchers and practitioners working in financial risk management.1 online resource (viii, 90 leaves): illustrationsenUniversity of VendaConformalised Quantile RegressionEmerging marketsUCTDFinancial forecastingMachine LearningDissertationNengovhela V. A comparative evaluation of machine learning models for stock price prediction and uncertainity estimation. []. , 2026 [cited yyyy month dd]. Available from:Nengovhela, V. (2026). <i>A comparative evaluation of machine learning models for stock price prediction and uncertainity estimation</i>. (). . Retrieved fromNengovhela, Vhukhudo. <i>"A comparative evaluation of machine learning models for stock price prediction and uncertainity estimation."</i> ., , 2026.TY - Dissertation AU - Nengovhela, Vhukhudo AB - This study compares machine learning models for stock price prediction and uncertainty estimation using high-frequency one-minute stock data. The research looks at how different models perform across developed and emerging markets, which helps with model selection for practical financial forecasting. Four models were tested for point forecasting: Random Forest (RF), Gradient Boosting (GB), Multi-Layer Perceptron (MLP), and a hybrid stacking ensemble composed of multiple base learners. For uncertainty quantification, three interval prediction methods were used: Bootstrap Residuals, Quantile Regression Forests (QRF), and Conformalised Quantile Regression (CQR). The analysis used one-minute stock price data from Microsoft Corporation (MSFT) as a developed market example and Standard Bank Group (SBK.JO) as an emerging market example, covering the period from 3rd to 26th September 2025. The results show that GB performed best for point forecasts in both markets. For MSFT, GB had RMSE of 0.2875 and MAE of 0.1869, while for SBK.JO it achieved RMSE of 25.9248 and MAE of 14.3638. Statistical tests using the Diebold-Mariano and Giacomini-White frameworks confirmed that GB significantly outperformed the other models. For interval prediction, QRF gave sharper intervals in the relatively stable developed market, while CQR achieved better coverage in the more volatile emerging market. The Hybrid Stacking model showed some advantages in volatile conditions but didn’t consistently beat well-tuned individual models. These findings suggest that ensemble methods like GB are still very effective for financial forecasting, and that uncertainty quantification methods should be chosen based on market volatility. The study provides practical guidance for selecting forecasting methods depending on market conditions and data characteristics, which should help both researchers and practitioners working in financial risk management. DA - 2026-05-19 DB - ResearchSpace DP - Univen KW - Conformalised Quantile Regression KW - Emerging markets KW - Financial forecasting KW - Machine Learning LK - https://univendspace.univen.ac.za PY - 2026 T1 - A comparative evaluation of machine learning models for stock price prediction and uncertainity estimation TI - A comparative evaluation of machine learning models for stock price prediction and uncertainity estimation UR - ER -