Quantitative Investing and Machine Learning
This course provides a rigorous, hands-on treatment of modern quantitative investing through the lens of machine learning and statistical computing. It bridges the gap between theoretical financial engineering and high-dimensional, data-driven alpha generation. Students will explore how classical financial economic frameworks are modernized, expanded, and challenged by advanced machine learning architectures. The primary focus is navigating the unique challenges of financial time series: low signal-to-noise ratios, non-stationarity, regime shifts, and market dynamics. Key topics include factor model construction and estimation, mean-variance optimization, risk modeling (including BARRA-style frameworks), rigorous backtesting methodologies, transaction cost modeling, performance attribution, and dynamic portfolio optimization. Working with industry-standard financial databases—such as CRSP, Compustat, and TAQ—students will design, implement, and backtest an original quantitative strategy.
讲师
日期
2026年09月14日 至 12月28日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周一 | 13:30 - 16:05 | Shuimo | - | - | - |
听众
Advanced Undergraduate
, Graduate
视频公开
公开
笔记公开
公开
语言
中文
, 英文
讲师介绍
I have been an Associate Professor at BIMSA since 2025. Prior to this role, I was an Assistant Professor at the Institute of Statistics and Big Data, Renmin University of China. My research lies at the intersection of AI and finance/business, focusing on FinTech and Business Analytics through innovative machine learning and data science methodologies. My interests include tail risk management, empirical asset pricing, portfolio optimization, derivatives, consumer credit, and related areas. Recently, I have also developed an interest in out-of-distribution (OOD) generalization and uncertainty quantification in machine learning. I publish in both finance/business and machine learning academic journals and conferences.