Risk Management in AI-Based Quantitative Trading: A Comprehensive Review and Perspective

Abstract

This paper offers an expanded review and perspective on risk management in artificial intelligence (AI)-based quantitative trading. As machine learning and deep learning approaches gain influence in financial markets, their reliability and adaptability under volatile conditions have become major concerns. This review follows a structured approach, beginning with a recall of the background and motivation for studying AI in quantitative finance. The main body integrates recall with viewpoints by reviewing literature, empirical studies, and case evidence, while adding critical perspectives on data dependence, overfitting, market instability, and systemic risk. Finally, the conclusion presents a viewpoint on how AI can be more effectively embedded into financial systems, highlighting the need for hybrid models, explainability, regulation, and robust oversight. This comprehensive treatment aims to contribute to academic debates while offering practical guidance to practitioners, regulators, and policymakers.