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- Vol. 5 Issue 3
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A Machine Learning Risk-Return Model for Price Prediction on the Nairobi Securities Exchange
Authors
1 2 School of Computing and Mathematics, The Co-operative University of Kenya, Kenya. 3 School of Pure and Applied Sciences, University of Embu, Kenya.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 633-638
- Article Type
- Original Article
References
1. Achia, T. N. O., Wangombe, A., & Anyika, E. (2008). Time-series modeling of returns from the NSE 20-share index: An empirical study of the impact of political climate on market volatility. East African Society of Operation Research Proceedings.
2. Bajja, S., Naeem, M. A., & Karim, S. (2024). Predicting stock market crises in African economies: A machine learning approach. Emerging Markets Review, 58, 101089.
3. Bathia, A. R., & Padhy, J. P. (2025). Deep learning and machine learning models enabled mean-variance portfolio optimisation in the Indian stock market. Empirical Economics Letters, 23(Special Issue 1), 165-194.
4. Chacha, M. H., Anapapa, A., & Mutuguta, J. (2022). Forecasting of banking sector securities prices in Kenya using machine learning technique. Asian Journal of Probability and Statistics, 18(1), 19-30.
5. Grudniewicz, J., & Ślepaczuk, R. (2023). Application of machine learning in algorithmic investment strategies on global stock markets. Research in International Business and Finance, 66, 102052.
6. Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. Journal of Portfolio Management, 30(5), 15-29.
7. López de Prado, M. (2018). Advances in financial machine learning. John Wiley & Sons.8. Macharia, K., Atitwa, E., Mugo, D., & Kawira, M. (2025). Modeling stock price trends and volatility in emerging markets using ARIMA and GARCH approaches. International Journal of Advanced and Applied Sciences, 12(7), 134-143.
9. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
10. Naeem, M. A., Karim, S., & Bajja, S. (2024). Predicting stock market crises in African economies: A machine learning approach. Emerging Markets Review, 58, 101089.
11. Talhartit, I., Ait Jillali, S., & El Kabbouri, M. (2025). Stock price forecasting: A comparative study of LSTM and ANN models on the Casablanca Stock Exchange. Archives of Business Research.
12. Wanjawa, B. (2021). Nairobi Securities Exchange (NSE) all stocks prices 2013-2020 (Version 2) [Data set]. Mendeley Data. https://doi.org/10.17632/73rb78pmzw.2
13. Yeldho, N., Thomas, D., Kurian, V. G., Arathy, C., & Nair Biju, A. V. (2024). Are machine learning models effective in predicting emerging markets? Investigating the accuracy of predictions in emerging stock market indices. Quality & Quantity, 59, 839-904.
2. Bajja, S., Naeem, M. A., & Karim, S. (2024). Predicting stock market crises in African economies: A machine learning approach. Emerging Markets Review, 58, 101089.
3. Bathia, A. R., & Padhy, J. P. (2025). Deep learning and machine learning models enabled mean-variance portfolio optimisation in the Indian stock market. Empirical Economics Letters, 23(Special Issue 1), 165-194.
4. Chacha, M. H., Anapapa, A., & Mutuguta, J. (2022). Forecasting of banking sector securities prices in Kenya using machine learning technique. Asian Journal of Probability and Statistics, 18(1), 19-30.
5. Grudniewicz, J., & Ślepaczuk, R. (2023). Application of machine learning in algorithmic investment strategies on global stock markets. Research in International Business and Finance, 66, 102052.
6. Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. Journal of Portfolio Management, 30(5), 15-29.
7. López de Prado, M. (2018). Advances in financial machine learning. John Wiley & Sons.8. Macharia, K., Atitwa, E., Mugo, D., & Kawira, M. (2025). Modeling stock price trends and volatility in emerging markets using ARIMA and GARCH approaches. International Journal of Advanced and Applied Sciences, 12(7), 134-143.
9. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
10. Naeem, M. A., Karim, S., & Bajja, S. (2024). Predicting stock market crises in African economies: A machine learning approach. Emerging Markets Review, 58, 101089.
11. Talhartit, I., Ait Jillali, S., & El Kabbouri, M. (2025). Stock price forecasting: A comparative study of LSTM and ANN models on the Casablanca Stock Exchange. Archives of Business Research.
12. Wanjawa, B. (2021). Nairobi Securities Exchange (NSE) all stocks prices 2013-2020 (Version 2) [Data set]. Mendeley Data. https://doi.org/10.17632/73rb78pmzw.2
13. Yeldho, N., Thomas, D., Kurian, V. G., Arathy, C., & Nair Biju, A. V. (2024). Are machine learning models effective in predicting emerging markets? Investigating the accuracy of predictions in emerging stock market indices. Quality & Quantity, 59, 839-904.
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