Performance Evaluation of Self-Organizing Feature Maps and Support Vector Machines in Predicting Stock Prices: A Comparative Study
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Abstract
Stock market forecasting plays a critical role in guiding investors and policymakers in dynamic financial environments. Despite advancements in predictive modeling, the comparative evaluation of machine learning techniques, such as Self-Organizing Feature Maps (SOFM) and Support Vector Machines (SVM), within the Nigerian Exchange Group (NGX) context has been limited. This study addresses this gap by investigating the performance of SOFM and SVM in predicting stock prices for five NGX-listed companies: United Bank for Africa (UBA), First Bank of Nigeria Holdings (FBNH), Guaranty Trust Bank (GTB), Nestlé (NESTLE), and Dangote Cement (DANGCEM), spanning financial, consumer goods, and industrial sectors. The dataset consisted of approximately 2,665 daily stock price observations (about 533 per company), covering the period 2011–2019. Following data cleaning and quality checks, preprocessing included Min-Max normalization and transformation into time-series matrices to ensure robustness and consistency. The dataset was divided into training (2011–2016, about 70%) and testing (2017–2019, about 30%) periods. SOFM was utilized for clustering and pattern recognition, while SVM incorporated technical indicators such as moving averages and price fluctuations. Implementation was conducted in MATLAB R2018a with a custom graphical user interface (GUI) for result visualization. Results revealed that SVM consistently outperformed SOFM across all datasets. For the UBA dataset, SVM achieved superior metrics, including an accuracy of 90.63%, specificity of 88.89%, and F1-score of 92.82%, with a computation time of 33.10 seconds. In comparison, SOFM demonstrated slightly lower performance with an accuracy of 88.13%, specificity of 85.19%, and an F1-score of 90.91%, and a computation time of 47.02 seconds. These findings establish SVM as a reliable and efficient model for stock price prediction on the NGX. Future research could explore hybrid models and broader datasets to enhance predictive accuracy and applicability in real-time investment strategies and risk management.
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References
Yang, R., Yu, L., Zhao, Y., Yu, H., Xu, G., Wu, Y., & Liu, Z. (2020). Big data analytics for financial Market volatility forecast based on support vector machine. International Journal of Information Management, 50, 452-462.
Hachicha, F. (2024). Sentiment investor, exchange rates, geopolitical risk and developing stock market: evidence of co-movements in the time-frequency domain during RussiaUkraine war. Review of Behavioral Finance, 16(3), 486-509.
Okediran, O. O., & Oguntoye, J. P. (2023). Analysis of critical success factors for information security management performance. LAUTECH Journal of Engineering and Technology, 17(1), 175-186.
Oyewole, A. T., Adeoye, O. B., Addy, W. A., Okoye, C. C., Ofodile, O. C., & Ugochukwu, C. E. (2024). Predicting stock market movements using neural networks: a review and application study. Computer Science & IT Research Journal, 5(3), 651-670.
Olayiwola, D. S., Olayiwola, A. A., Oguntoye, J. P., Awodoye, O. O., Ganiyu, R. A., & Omidiora, E. O. (2023). Development of a Fingerprint Verification and Identification System Using a Gravitational Search Algorithm-Optimized Deep Convolutional Neural Network. Adeleke University Journal of Engineering and Technology, 6(2), 296-307.
Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464-1480.
Ola B. O, Oguntoye J. P., Awodoye O. O. & Oyewole M. O. (2020). Development of a Plant Disease Classification System using an Improved Counter Propagation Neural Network. International Journal of Computer Applications (0975 – 8887). 175(20), 19-26
Hamori, S., & Hamori, N. (2010). Introduction of the euro and the monetary policy of the European Central Bank. World Scientific.
Shen, J., & Shafiq, M. O. (2020). Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of big Data, 7, 1-33.
Saravanan, K., Prakash, R. B., Balakrishnan, C., Kumar, G. V. P., Subramanian, R. S., & Anita, M. (2023, December). Support Vector Machines: Unveiling the Power and Versatility of SVMs in Modern Machine Learning. In 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 680-687). IEEE.
Ogundepo O. Y., Omeiza I. O. A. & Oguntoye J. P. (2022). Optimized Textural Features for Mass Classification in Digital Mammography Using a Weighted Average Gravitational Search Algorithm. International Journal of Electrical and Computer Engineering (IJECE). 12 (5): pp 1-12.
Chen, L., Zhang, W., & Liu, H. (2022). A hybrid model of SVM and feature selection for stock market forecasting. Journal of Financial Data Science, Vol. 9, Issue 1, pp. 55–72.
Atipaga, U. F., Alagidede, I., & Tweneboah, G. (2024). On the connectedness of stock returns and exchange rates in emerging and frontier markets in Africa. Economic Notes, 53(3), e12249.
Adetunji A. B., Oguntoye J. P., Fenwa O. D. & Omidiora E. O. (2015): Facial Expression Recognition Based on Cultural Particle Swamp Optimization and Support Vector Machine. LAUTECH Journal of Engineering and Technology. 10(1), 94-102.
Adetunji A. B., Oguntoye J. P., Fenwa O. D. and Omidiora E. O. (2018): Reducing the Computational Cost of SVM in Face Recognition Application Using Hybrid Cultural Algorithm. IOSR Journal of Computer Engineering (IOSR-JCE). 20 (2), 36-45.
Heo, J., & Yang, J. Y. (2016). Stock price prediction based on financial statements using SVM. International Journal of Hybrid Information Technology, 9(2), 57-66.
Liu, Z., Dang, Z., & Yu, J. (2020, November). Stock price prediction model based on RBF-SVM algorithm. In 2020 International Conference on Computer Engineering and Intelligent Control (ICCEIC) (124-127)
Ji, S. (2024). Predict stock market price by applying ANN, SVM and Random Forest. In SHS Web of Conferences (196, 02005). EDP Sciences.
Osama, A., Saeid, H., Mohsen, S., & Eldin, S. S. (2024). Comparative Analysis of Stock Price Prediction Using Machine Learning. In 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) (69-75).
Huang, C. L., & Tsai, C. Y. (2009). A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting. Expert Systems with applications, 36(2), 1529-1539.
Syukur, A., & Marjuni, A. (2020). Stock Price Forecasting Using Univariate Singular Spectral Analysis through Hadamard Transform. International Journal of Intelligent Engineering & Systems, 13(2).
Sukma, N., & Namahoot, C. S. (2024). An Algorithmic Trading Approach Merging Machine Learning with Multi-Indicator Strategies for Optimal Performance. IEEE Access.
Chhajer, P., Shah, M., & Kshirsagar, A. (2022). The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction. Decision Analytics Journal, 2, 100015.
Bouasabah, M. (2024). Analysis of machine learning's performance in stock market prediction, compared to traditional technical analysis indicators. International Journal of Data Analysis Techniques and Strategies, 16(1), 32-46.
Hussain, M., Liu, S., Hussain, W., Liu, Q., Hussain, H., & Ashraf, U. (2024). Application of Deep Learning for Reservoir Porosity Prediction and Self Organizing Map for Lithofacies Prediction. Journal of Applied Geophysics, 230, 105502.
Oguntoye, J. P., Ajagbe, S. A., Adedeji, O. T., Awodoye, O. O., Adetunji, A. B., Omidiora, E. O., & Adigun, M. O. (2025). An Improved Chicken Swarm Optimization Techniques Based on Cultural Algorithm Operators for Biometric Access Control. Computers, Materials & Continua, 84(3): 1-19.