Mingyang (Yang) Liu is a quantitative researcher and Visiting Researcher at the Imperial Centre of Excellence in Quantitative Finance at Imperial College London. He recently completed his Ph.D. in Quantitative Finance at Imperial Business School, where his research focused on asset pricing, financial econometrics, systematic investment strategies, and large-dimensional factor models.

His research interests span cross-sectional asset pricing, systematic macro and cross-asset modelling, portfolio construction, and machine learning applications in finance. His work combines factor models, regime-aware modelling, and statistical learning methods to study how systematic investment strategies behave across changing market conditions, with particular emphasis on portfolio robustness, risk diagnostics, and implementation-aware quantitative research.

During his doctoral research, Mingyang developed large-scale predictive modelling frameworks across equities and cross-asset markets, studying the interaction between latent risk factors, macroeconomic regimes, and portfolio behaviour under market stress. His recent work also explores regime-switching models, structural-break detection, and machine-learning approaches for signal generation and risk management across equities, rates, FX, commodities, and volatility markets.

Prior to Imperial, Mingyang worked as a research assistant in the Department of Economics at Columbia University, where he contributed to macroeconomic forecasting research using robust factor-estimation techniques, including Robust Principal Component Analysis (RPCA) and Stable Principal Component Pursuit (SPCP), to improve estimation under data corruption, missing observations, and outlier contamination.

Mingyang holds an MRes in Finance with Distinction from Imperial College London, an MA in Economics (Financial Econometrics, STEM) from Columbia University, and a BS in Financial Mathematics and Economics from the University of Michigan.

Outside research, he enjoys mountain climbing, Texas Hold’em, and landscape photography.