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Machine learning in financial markets: A critical review of algorithmic trading and risk management
1 Independent Researcher, St Louis, Missouri, USA.
2 Department of Business Administration, Skinner School of Business, Trevecca Nazarene University, USA.
3 Kings International School Port-Harcourt, Rivers State Nigeria.
4 Ignition Ajuru University of Education, Rivers State, Nigeria.
5 Independent Researcher, Nashville, Tennessee, USA.
6 Independent Researcher, Nigeria.
Review
International Journal of Science and Research Archive, 2024, 11(01), 1853–1862.
Article DOI: 10.30574/ijsra.2024.11.1.0292
Publication history:
Received on 04 January 2024; revised on 12 February 2024; accepted on 14 February 2024
Abstract:
The integration of machine learning (ML) techniques in financial markets has revolutionized traditional trading and risk management strategies, offering unprecedented opportunities and challenges. This paper provides a comprehensive and critical review of the application of ML in algorithmic trading and risk management within the realm of financial markets. The review begins by exploring the evolution of algorithmic trading, highlighting the paradigm shift from traditional rule-based strategies to ML-driven approaches. Various ML algorithms, including neural networks, decision trees, and ensemble methods, are examined in the context of their application to predictive modeling, pattern recognition, and signal generation for trading purposes. The paper also delves into the challenges and limitations associated with the adoption of ML in financial markets. Issues such as overfitting, data bias, and model interpretability are discussed, emphasizing the importance of addressing these concerns to ensure robust and reliable trading systems. Furthermore, ethical considerations and potential regulatory implications of ML-driven trading strategies are considered in the context of market fairness and stability. In the realm of risk management, the review scrutinizes the role of ML in assessing and mitigating financial risks. The paper evaluates the effectiveness of ML models in identifying market trends, measuring portfolio risk, and optimizing asset allocation. Additionally, it examines the potential impact of ML on systemic risk and the need for adaptive risk management frameworks in dynamic market conditions. The synthesis of findings underscores the transformative impact of ML on financial markets, showcasing its potential to enhance trading strategies and risk management practices. However, the review also highlights the importance of addressing inherent challenges and ethical considerations to ensure the responsible and sustainable integration of ML in the financial domain. This critical review provides valuable insights into the current state of machine learning in financial markets, offering a foundation for future research directions and the development of best practices in algorithmic trading and risk management.
Keywords:
Machine Learning; Financial Markets; Algorithmic Trading; Risk Management; Predictive Modeling.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
