6. "Resolving the Excessive Trading Puzzle: An Integrated Approach Based on Surveys and Transactions," with Hongqi Liu, Wei A. Xiong, and Wei Xiong, 2020, Online Appendix
The literature has provided over a dozen explanations for the widely documented excessive trading puzzle of retail investors trading so much that it 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 nationwide survey to elicit investors’ 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 trading motives. A horse race among survey- based trading motives suggests that perceived information advantage and gambling preference dominate other explanations. Moreover, other popular arguments, such as neglect of trading costs, do not contribute to excessive trading.
5. "Personality Differences and Investment Decision-Making," with Zhengyang Jiang and Hongjun Yan, 2020.
We administer a survey to thousands of affluent Americans about their personality traits and investment decisions. We show that the Big Five personality traits explain investment decisions through three distinct channels: expectations, risk preferences, and social interactions. Two personality traits—Neuroticism and Openness—exhibit remarkable power for explaining equity investments. Investors high in Neuroticism are more pessimistic about future stock returns and expect higher downside risks. In comparison, investors low in Openness are more risk averse. Consequently, both types tend to allocate less wealth to the stock market. We confirm these results out-of-sample using a representative panel of Australian households.
Due to their complex features, structured financial products can hurt the average investor. Are certain investors particularly vulnerable? Using account-level transaction data of retail struc- tured 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 substantially greater than from trading simple, non-structured funds. In an event study, we further confirm that part of this wealth transfer can be directly 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.
Revise and Resubmit, Review of Financial Studies
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.
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.
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.