Research

Working Papers

5. "Resolving the Excessive Trading Puzzle: An Intergrated Approach Based on Surveys and Transactions," with Hongqi Liu, Wei A. Xiong, and Wei Xiong, 2020, Online Appendix

The behavioral finance literature has provided over a dozen explanations for the so-called excessive trading puzzle – retail investors trade a lot even though more trading hurts their performance. It is difficult to use transaction data to differentiate these explanations as they share similar predictions by design. To confront this challenge, we design and administer a nation-wide survey among retail investors to elicit their responses to an exhaustive list of trading motives. By merging survey responses with account-level transaction data, we validate survey responses with actual trading behaviors and compare the power of survey-based and transaction-based measures of trading motives. A horse race among survey-based trading motives suggests that overconfidence in having information advantage and gambling preference quantitatively dominate other explanations for excessive trading. Moreover, other popular arguments such as neglect of trading cost do not contribute to excessive trading.


4. "Exploited by Complexity," with Paul Gao, Allen Hu, Peter Kelly, and Ning Zhu, 2020

Due to their complex features, structured financial products harm the average investor. But, can some investors benefit from this complexity? Using account-level transaction data of retail structured funds, we show that the rich (sophisticated) benefit from complexity at the expense of the poor (naive). The poor-to-rich wealth transfer that results from trading structured funds is an order of magnitude greater than the wealth transfer from trading simple, non-structured funds. In an event study, we further confirm that the wealth transfer can be partially attributed to investors' differing responses to complexity. In particular, when a market crash triggers funds into a restructuring process and their prices are expected to shrink by half on a given day, the poor and naive subset of investors fail to respond effectively.


3. "Price and Volume Dynamics in Bubbles," with Jingchi Liao and Ning Zhu, 2020, Online Appendix

We propose a framework to explain the sharp rise in prices and volume observed in historical financial bubbles. The model generates a novel mechanism for volume: due to the interaction between beliefs and preferences, investors are quick to buy assets with positive past returns, but also quick to sell them if the good returns continue. Using account-level transaction data on the 2014–2015 Chinese stock market bubble, we test the model's predictions about volume and find supportive evidence. We also empirically show that, consistent with the model, extrapolators are largely responsible for the price run-up and crash during the bubble.


2. "Positive Feedback Trading and Stock Prices," with Chen Wang, 2019

We show that mutual funds contribute to cross-sectional momentum and excess volatility through positive feedback trading. Stocks held by positive feedback funds exhibit much stronger momentum, almost doubling the returns from a simple momentum strategy. This “enhanced” momentum is robust to alternative measures of positive feedback trading and cannot be explained by other stock characteristics, ex-post firm fundamentals, fund flows, or herding. Moreover, enhanced momentum is almost fully reversed after one quarter, suggesting initial overshooting and subsequent reversal. We argue the most likely explanation is the price pressure from positive feedback trading. Finally, we relate positive feedback trading to mutual fund performance and show that it can positively predict a fund’s return from active management.


1. "Investor Behavior Under the Law of Small Numbers," 2017

I study how investors trade under the law of small numbers, the belief that even a small sample represents the characteristics of the underlying population. These investors expect short-term trends to reverse but long-term trends to continue. Using a simple model, I show that the law of small numbers can explain several well-documented trading phenomena: chasing long-term trends, bucking short-term trends, the disposition effect, and the V-shaped selling propensity. Moreover, I derive and successfully test the model's new predictions, and in doing so, I (1) provide evidence for heterogeneous investor horizons, (2) highlight how investors' extrapolation horizon and holding period can explain variation in their disposition effect, and (3) show that the V-shaped selling propensity is an aggregate phenomenon driven by separate groups of investors.