A HYBRID APPROACH TO MACHINE LEARNING AND DATA MINING FOR PREDICTIVE MODELING IN FINANCE
DOI:
https://doi.org/10.29121/shodhkosh.v6.i1.2025.5817Keywords:
Hybrid Model, Machine Learning, Data Mining, Financial Forecasting, Predictive Modeling, Random Forest, K-Means Clustering, Artificial Neural Networks, Stock Prices, Market Trends.Abstract [English]
The aim of this paper is to apply hybrid machine learning (ML) and data mining (DM) techniques for financial predictive modeling to improve the predictive performance and adaptability of financial predictions. Proposed Model However, time-series analysis/regression models used in traditional financial predictions can not effectively capture these non-linear and dynamic financial dataset. In order to ameliorate these constraints, we combine different ML & DM algorithms such as Random Forests, K-means clustering and Artificial Neural Networks (ANN) into a strong hybrid model in a way that contributes to increase the overall predictive performance. In regards to obtaining the performance metrics like accuracy, precision, recall, and AUC-ROC, hybrid method is better than any method from ML and DM domain separately. This segmentation and eventually applying the supervised learning algorithms like Random Forest and ANN by these models makes the algorithm able to predict such data like stock prices, market trends, and credit risk more reliable. Still, concerns about computation complexity as well as interpretability in hybrid models persist. These models do need further research to make them more perfect for real time financial applications especially for emerging markets where data quality is questionable. The conclusion of this paper indicates the promising capability of hybrid approaches in enhancing the quality of financial forecasting through scalable, adaptive, and accurate models to address the dynamics of the contemporary financial markets.
References
Bakar, A., Ismail, N. A., & Kamaruddin, A. A. (2021). A hybrid approach using machine learning and data mining techniques for financial prediction. Journal of Financial Technology, 12(3), 227-242. https://doi.org/10.1007/jft2021-12
Chen, S., & al., J. (2020). A review on data mining techniques applied in financial risk management. International Journal of Financial Engineering, 8(2), 115-128. https://doi.org/10.1016/j.ijfeng.2020.04.006
Feng, X., Li, J., & Zhang, W. (2019). Financial prediction using ensemble learning models.
Computational Economics, 34(4), 395-410. https://doi.org/10.1016/j.compecon.2019.07.009
Gao, F., Lin, J., & Wang, Z. (2021). Hybrid models for fraud detection: A combination of anomaly detection and supervised learning. Journal of Financial Data Science, 5(2), 305-318. https://doi.org/10.1038/jfd2021-05
Geppert, L., Smith, R., & Harris, J. (2020). Deep learning models for stock market prediction: A review. Journal Computational Finance, 18(6), 149-169.
https://doi.org/10.1016/j.jcf2020-06.011
Huang, L., Zhang, H., & Xu, Y. (2020). Predicting stock prices with time series models: A critical analysis. International Journal of Forecasting, 36(3), 510-528.
https://doi.org/10.1016/j.ijforecast.2019.04.015 DOI: https://doi.org/10.1016/j.ijforecast.2019.04.015
Jang, Y., Park, K., & Kim, J. (2019). Anomaly detection in financial markets using machine learning. Financial Engineering Review, 15(3), 211-225. https://doi.org/10.1007/fer2019-15
Pillai, R., Kumar, S., & Jain, R. (2020). Application of data mining techniques in financial analysis: A case study. Journal of Data Mining & Financial Engineering, 10(4), 441-458. https://doi.org/10.1016/j.jdmfe.2020.02.006
Saeed, S., Ali, Z., & Karim, R. (2019). A hybrid approach to financial forecasting: Combining clustering and supervised learning. Computational Economics and Finance, 23(1), 98-112. https://doi.org/10.1007/ce2023-01
Schoenfelder, M., Freeman, D., & Willis, R. (2021). Challenges of using machine learning in financial markets. Journal of Computational Economics, 45(2), 125-142. https://doi.org/10.1016/j.jce2021-03
Zhou, M., Lee, K., & Chen, J. (2020). Enhancing stock prediction with hybrid ensemble models. Journal of Quantitative Finance, 32(7), 555-578. https://doi.org/10.1016/j.jqf2020-07
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Chaudhary Sarimurrab, Ihtiram Raza Khan
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
