INTERNATIONAL JOURNAL OF ACCOUNTING, FINANCE AND SOCIAL TAXATION (IJAFST)
Machine Learning Algorithms for Financial Risk Prediction: A Performance Comparison
E-ISSN: 2316-422X
P-ISSN: 3982-0997
DOI: https://iigdpublishers.com/article/1078
This study evaluates the performance of various machine learning (ML) models in predicting and mitigating financial risks. Using data from Bloomberg, Thomson Reuters Eikon, Yahoo Finance, and FRED (2014-2023), we compare neural networks, decision trees, random forests, and support vector machines. Our findings show that neural networks and random forests outperform traditional models, offering superior predictive accuracy and robust risk mitigation strategies. The study provides practical insights for implementing ML algorithms in financial risk management, highlighting t he potential for enhanced decision-making and improved financial stability.
Lemuel Kenneth David, Jianling Wang, Idrissa I. Cisse & Vanessa Angel
Abdi, Hervé, (1999). Dominique Valentin, and Betty Edelman. Neural networks. No. 124. Sage.
Abdulla, Yaqoob Yusuf, & Adel Ismail Al-Alawi. (2024). "Advances in Machine Learning for Financial Risk Management: A Systematic Literature Review." 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS). IEEE. DOI: 10.1109/ICETSIS61505.2024.10459536
Breiman, Leo. (2001). "Random forests." Machine Learning 45 5-32. https://doi.org/10.1023/A:1010933404324
Davis, Fred D. (1989). "Technology acceptance model: TAM." Al-Suqri, MN, Al- Aufi, AS: Information Seeking Behavior and Technology Adoption 205: 219.
De Ville, Barry. (2013). "Decision trees." Wiley Interdisciplinary Reviews: Computational Statistics 5.6: 448-455. https://doi.org/10.1002/wics.1278
