Developing an AI-Driven Predictive Model for Stock Market Forecasting in the Banking Sector
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200-213
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10.58578/mjms.v3i2.5197
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Authors:Olayiwola Blessing Akinnagbe1, Taiwo Abdulahi Akintayo2, Arinze Betsy Adanna3
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Abstract
This study develops an AI-driven predictive model for stock market forecasting in the banking sector, using LSTM, Random Forest, and Linear Regression. Historical stock prices, macroeconomic indicators, and banking sector metrics were analyzed, with data preprocessing techniques applied to enhance accuracy. Model performance was evaluated using MAE, RMSE, and R², with LSTM achieving the best results (R² = 0.92). Findings suggest AI models can improve investment decisions, trading strategies, and risk management. Future research should explore real-time data integration, sentiment analysis, and hybrid AI models for enhanced forecasting accuracy.
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References
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2022). Time series analysis: Forecasting and control (6th ed.). Wiley.
Breiman, L. (2021). Random forests. Machine Learning, 45(1), 5-32.
Chen, X., Li, Y., & Wang, Z. (2022). Challenges and opportunities in stock market forecasting: A review of traditional and AI-based approaches. International Journal of Financial Studies, 10(2), 123-140.
Chen, X., Li, Y., & Wang, Z. (2023). Challenges and opportunities in stock market forecasting: A review of traditional and AI-based approaches. International Journal of Financial Studies, 10(2), 123-140.
Cortes, C., & Vapnik, V. (2021). Support-vector networks. Machine Learning, 20(3), 273-297.
Damodaran, A. (2023). Investment valuation: Tools and techniques for determining the value of any asset (4th ed.). Wiley.
Gupta, A., & Sharma, R. (2023). The role of machine learning in modern financial markets. Journal of Computational Finance, 15(4), 89-105.
Hassan, M. K., Hasan, M. R., & Karim, M. R. (2023). LSTM networks for stock market prediction: A case study on banking stocks. Applied Soft Computing, 112, 107789.
Hochreiter, S., & Schmidhuber, J. (2021). Long short-term memory. Neural Computation, 9(8), 1735-1780.
Johnson, L., Smith, J., & Brown, T. (2021). Fundamental analysis in stock market forecasting: A critical review. Journal of Financial Analysis, 18(2), 67-82.
Khan, S., Ahmed, T., & Rahman, M. (2023). AI in finance: Transforming stock market forecasting. Journal of Artificial Intelligence in Finance, 8(1), 23-37.
Kumar, R., & Singh, P. (2023). The impact of macroeconomic factors on banking stock performance. Journal of Banking and Finance, 45(6), 78-92.
Lee, J., Park, S., & Kim, H. (2021). Sentiment analysis and stock market forecasting: A machine learning approach. Expert Systems with Applications, 185, 115594.
Murphy, J. J. (2021). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Patel, V., Shah, M., & Desai, P. (2022). Limitations of traditional stock market forecasting models in the era of big data. Journal of Financial Analytics, 7(3), 210-225.
Rahman, M. M., Islam, M. S., & Hossain, M. A. (2024). AI applications in the banking sector: A systematic review. Journal of Financial Technology, 14(2), 67-82.
Smith, J., & Johnson, L. (2021). The role of stock markets in economic development: A global perspective. Journal of Economic Studies, 48(4), 567-582
Taylor, S. J., & Brown, R. L. (2022). Technical analysis: The complete resource for financial market technicians. FT Press.
Wang, Y., Zhang, X., & Liu, Z. (2022). Stock market forecasting in the banking sector: Challenges and opportunities. Journal of Financial Markets, 25(1), 34-50.
Zhang, H., Li, Q., & Chen, Y. (2021). High-dimensional data in stock market forecasting: A machine learning perspective. Journal of Big Data Analytics in Finance, 3(2), 89-104.
Zhang, H., Li, Q., & Chen, Y. (2023). Hybrid models for stock market forecasting: Integrating LSTM and ARIMA. Journal of Computational Finance, 16(2), 45-60.