The rapid advancement of artificial intelligence (AI) technology has brought revolutionary changes to the field of quantitative investment. This study systematically examines the application scenarios, practical challenges, and future trends of AI in quantitative investment. First, the paper reviews the evolutionary trajectory of AI technologies, spanning from early expert systems to contemporary deep reinforcement learning, while analyzing breakthrough developments in specialized financial AI tools and core technical capabilities. Second, the research focuses on key AI applications in quantitative investment, including multi-factor model optimization, high-frequency market risk management, multimodal data integration, and algorithmic trading enhancement. Empirical evidence demonstrates that AI technologies can significantly improve strategy performance and expand the boundaries of traditional methodologies. However, AI applications still face challenges such as model overfitting, interpretability limitations, regulatory lag, and computational costs. Finally, the paper outlines future trends in technological convergence and application scenario development. This research provides a systematic framework for understanding the paradigm shift in quantitative investment driven by AI, while offering practical references for institutional investors, individual users, and regulatory bodies.
Boshan Chen (2025) studied this question.