Working Paper
Can Machines Understand Human Decisions? Dissecting Stock Forecasting Skill
[Paper] • Revising for invited re-submission at Journal of Finance
Presentations: CICF, AsianFA Annual Conference, Hawaii Accounting Research Conference, MFA, FMA, SFA,
Renmin University, Baruch College, Nankai University,
Fudan University, University of Arizona, Georgia State University, University of Georgia, Iowa State University, University of Minnesota,
Xiamen University, Huazhong University of Science and Technology, Beijing University, Atlanta PhD Consortium, Shanghai Jiaotong Univeristy, CHUK-Shenzhen
We use machine learning (ML) to provide a novel methodology to determine analysts' skills
and effectively aggregate the forecasting opinions of analysts to form a crowd wisdom-based earnings
forecast. Our machine-identified skilled analysts persistently outperform expert-picked star analysts.
We find that machines rely on nonlinear interactions of analyst characteristics, such as past skill and efforts, to make predictions,
unlike human experts, who lean more on relation-based information such as brokerage size.
Decoding Mutual Fund Performance: Dynamic Return Patterns via Deep Learning
[Paper] [Slide] Presentations: FMA, SFA, Economics of Financial Technology Conference, Atlanta Rising Scholar Symposium in Finance, MFA, Georgia State University, Saint Louis University, Hofstra University
I employ a state-of-the-art sequential deep learning model to understand and predict dynamic patterns in mutual fund returns. The model predicts sequences of future returns and offers interpretable insights. A long-short portfolio based on the model’s prediction generates a 2.8% annualized Carhart 4-factor alpha, and this abnormal performance is persistent for up to four years. The model captures dynamic features of mutual fund strategies related to company fundamentals and macroeconomic states. Fund returns are most informative when they happen after earnings announcements for stocks held by the funds. Historical performance and macroeconomic variables are the most important determinants of future fund return patterns and performance.
The Impact of AI Adoption on Hedge Fund Performance
[Slide] Presentations: Global Finance Association, FMA , SFA
We examine the impact of AI adoption on hedge fund performance and investment strategies. We find that AI adoption improves
hedge fund performance by 2.64% annually. AI adoption also reduces fund risk and increases shape ratio and information ratio. The adoption of AI is
associated with a greater number of holdings in the portfolio and less concentration in the local
stocks.
Why do actively managed mutual funds hold ETFs? Evidence on liquidity management
Presentations: FMA, Georgia State University
I find that investment managers in actively managed mutual funds trade exchange-traded funds (ETFs) for liquidity management. The results show that funds that do not use index ETFs
exhibit lower returns when they experience fund flow. The performance of funds that use index ETFs, however, is
independent of investor’s liquidity demands.