Ge Yu

Ge Yu is a Ph.D. candidate in finance at the Swedish House of Finance, Stockholm School of Economics. His research interests include asset management, machine learning and artificial intelligence, empirical asset pricing, and FinTech. His job market paper examines the connection and difference between portfolio differentiation and narrative differentiation in mutual funds using modern AI embedding methods. His other work explores exchange-traded funds (ETFs) and the impact of generative AI on financial markets.

Ge will be on the 2026-2027 Job Market.

Job market paper: From Words to Portfolios: Disentangling Narrative and Reality in Mutual Fund Differentiation

Abstract: 
This paper studies the difference between portfolio differentiation and narrative differentiation in mutual funds. Developing a transformer-based portfolio embedding model and combining it with pre-trained large language model embeddings of fund prospectuses, I construct two novel measures of mutual fund differentiation from portfolio holdings and prospectus narratives. The two measures are only weakly correlated, indicating that portfolio distinctiveness does not necessarily coincide with narrative distinctiveness. Prospectus uniqueness is associated with higher fees and, when past performance is strong, greater inflows, while portfolio uniqueness predicts future alpha only among funds with a strong track record. Together, these findings suggest that narrative differentiation in fund disclosures serves a marketing and pricing role, whereas portfolio differentiation is informative about future performance primarily among funds with strong prior records.