Priscilla Liu conducts quantitative research in support of Rayliant’s localization strategies tailored for emerging markets, especially the China onshore market. Her research includes localized equity signal research, expected return prediction using machine learning models, portfolio optimization, and ESG investing. During her years at Rayliant, she has actively engaged in the design and continuous improvement of Rayliant’s active ETFs including RAYC, RAYE and RAYD. Her research findings on machine learning and portfolio optimization can be found from the JBIS paper ‘When Smart Beta Meets Machine Learning and Portfolio Optimization’.
Priscilla earned a joint undergraduate degree in Economics and Mathematics from New York University, and her Master of Financial Engineering from the UCLA Anderson School of Management.