Artificial intelligence (AI) has become crucial in quantitative finance, driving improvements in investment modeling, strategy development, and risk assessment. This paper reviews recent advancements in AI-enhanced quantitative investing, emphasizing deep learning, reinforcement learning, and algorithmic optimization. Research by Ding and Qin achieved over 97% accuracy in stock predictions using Long Short-Term Memory (LSTM) networks, while Zhang's XGBoost framework improved high-frequency trading performance, yielding a mean squared error (MSE) of 0.1918. Xu et al. implemented a Kalman Filter-based PI clock servo, achieving nanosecond-level synchronization precision (59.37 ns) in multi-hop networks, enhancing scalability. In financial risk forecasting, Duan et al. increased K-Means clustering accuracy to 99.4%, reducing false alarm rates by nearly 48%. Additionally, optimization techniques like PPO, CVaR-based risk management, and quantum optimization have improved stability, risk control, and computational efficiency. However, challenges like data diversity, model interpretability, and regulatory oversight persist. In conclusion, integrating AI into quantitative finance enhances predictive accuracy and decision-making, transforming intelligent asset management amid changing risk landscapes.
Y. Richard Wang (Thu,) studied this question.