LinkedIn Profile What excites me most about quantitative investing is the full research process: discovering a source of alpha, understanding what drives it, and figuring out how to turn it into a portfolio. My research interests sit at the intersection of alpha research and portfolio construction. At Everbright Securities, I studied cross-sectional equity signals using point-in-time fundamentals, earnings momentum, analyst revisions, and industry data. Later, at Bridge Trust, I moved from individual signals toward multi-asset portfolio research, building a regime-aware allocation framework and studying how forecasts, risk budgets, and portfolio constraints interact. I am equally excited by the tools we use to answer these questions. My background in economics and statistics has made me comfortable starting from economic intuition, while statistical learning and machine learning give me ways to explore nonlinear relationships, richer datasets, and problems that traditional models may not capture well. I am especially interested in adapting new research methods to alpha discovery and portfolio decisions rather than treating the model itself as the end goal. At MSCF, I hope to keep pushing along that path: finding better signals, expanding the research toolkit, and building better portfolios. I’d be glad to connect with researchers and investors who share that curiosity.
