Machine learning algorithms for financial risk prediction: A performance comparison
DOI:
https://doi.org/10.65453/ijar.v9i2.1226Keywords:
Financial Risk Management, Machine Learning, Neural Networks, Decision, Trees, Random, Forests, Support, Vector, Machines, Predictive Analytics, Portfolio Management, Data-driven Decision MakingAbstract
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 the potential for enhanced decision-making and improved financial stability.
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Copyright (c) 2024 Lemuel Kenneth David, Jianling Wang, Idrissa I. Cisse, Vanessa Angel
This work is licensed under a Creative Commons Attribution 4.0 International License.
