معرفی
Chuanping Sun is a Lecturer in Finance at Bayes Business School, City, University of London. He earned his PhD in Economics from Queen Mary University of London and has held visiting positions at New York University and FGV Sao Paulo. His research bridges machine learning and empirical finance, with a focus on robust factor selection in asset pricing models.
His research interests include:
- Empirical Asset Pricing
- Financial Econometrics
- Machine Learning in Finance
- Portfolio Choice
- Cross-Sectional Asset Returns
- Factor Models
His recent work investigates the impact of factor correlations on model robustness, proposing a correlation-robust machine learning approach (using OWL shrinkage) to identify key drivers of asset returns, including the market, liquidity, momentum, and profitability factors. His research shows that traditional methods like LASSO and Fama-MacBeth often fail to detect the market factor due to high correlations, while his approach maintains robustness and delivers superior out-of-sample portfolio performance.
His two recent publications in the Journal of Empirical Finance (2024, 2022) demonstrate his focus on methodological innovation in high-dimensional financial datasets, particularly in handling correlated factors and exploiting stock return correlations.
He has received internal research funding from City University of London's Pump Priming Fund, supporting his ongoing projects in machine learning applications in finance. While no formal advisees are listed, his role as a lecturer and research supervisor (as indicated by his CRediT contribution) suggests involvement in student mentorship.
Chuanping Sun is affiliated with the Finance department at Bayes Business School, where he contributes to research and teaching in quantitative finance and econometrics.
