Research

My research interests primarily lie in the fields of investments and FinTech. Specifically, I am keen on leveraging economics and AI techniques to enhance investors' understanding of issues pertinent to financial markets. 

Working Papers

[1] Heads I Win, Tails It’s Chance: Mutual Fund Performance Self-attribution (Job Market Paper)

This paper investigates the presence of self-attribution bias among mutual fund managers and evaluates its impacts on trading outcomes. I develop a novel GPT-based Natural Language Processing (NLP) architecture designed to extract attribution information from mutual funds' self-assessments of performance in their shareholder reports. On average, mutual fund managers exhibit a significant self-attribution bias—they are 40.6% more likely to attribute performance contributors versus performance detractors to internal factors. Funds displaying stronger self-attribution bias tend to engage in excessive trading in the subsequent reporting period, which negatively impacts their performance. In addition, funds exhibit a higher self-attribution bias following better performance, despite the fact that biased attribution only influences fund flows when funds perform poorly. Overall, these findings suggest that biased attribution likely stems from cognitive bias rather than strategic choices.

Presented at: Junior Academics Research Seminars (JARS) in Finance 2024 (scheduled), University of South Carolina, University of South Florida, Georgia State University.

Featured on: Morningstar, VettaFi.

[2] Visual Information in the Age of AI: Evidence from Corporate Executive Presentations (with Sean Cao, Yichen Cheng, Yusen Xia, and Baozhong Yang) 

This paper constructs and studies a comprehensive data set comprised of corporate executive presentations. Executive presentations are unique in that they provide an abundance of visual information about a firm’s project designs and production plans. In the aggregate, these presentations allow us to explore the value of visual information and examine how market participants with varying levels of technological access respond to such information. Using a state-of-the-art deep learning model, we extract forward-looking operational information from presentation slide images. We find that short-term abnormal returns are positively associated with forward-looking operational information, but not with backward-looking or financial information. AI-equipped financial institutions respond strongly to visual signals, whereas other institutions and retail investors do not. Our study provides novel evidence that AI adoption rewards investors with an informational advantage, creating a potential AI divide among market participants.

Presented at: NBER Big Data and High-Performance Computing for Financial Economics 2024 (scheduled), 2024 Financial Accounting and Reporting Section (FARS) Midyear Meeting, 2024 Hawaii Accounting Research Conference, The 2023 Research Conference on Capital Market Research in the Era of AI, CICF 2023, 2023 Hong Kong Conference for Fintech, AI, and Big Data in Business, 2023 Association for the Advancement of Artificial Intelligence Conference (AAAI-23), 16th NYCU Finance Conference, AllianceBernstein, Balyasny Asset Management, Wolfe Research, PanAgora Asset Management, CKGSB, Georgia State University, University of Maryland, HKUST, University of Turku, University in Helsinki, Kent State University, Renmin University, Santa Clara University.

Featured on: Columbia Law School Blog.

[3] The Politicization of Social Responsibility (with Todd A. Gormley and Manish Jha) 

Institutional investors' support for socially responsible investment (SRI) proposals is lower for firms headquartered in Republican-led states. The lower support concentrates in recent years, which coincides with when politicians became more vocal regarding firms’ SRI activities, among larger institutions and firms, and during months of high political polarization. The shift in investor support also occurs within states following changes in political leadership. Support for SRI proposals is 10 percentage points lower in the same state when it is led by Republicans instead of Democrats. The findings suggest that state-level politics and the politicization of SRI impacts institutional investors’ voting decisions

Presented at: NBER Corporate Finance Conference 2024 (scheduled), Clemson ESG and Policy Research Conference 2024, University of Toronto, London Business School, Washington University St. Louis, University of Alabama, The University of British Columbia, Northwestern University, University of Georgia, Georgia State University.