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Working Papers
Working Papers
with Hee-Seo Han and Daniel Weagley
with Hee-Seo Han and Daniel Weagley
We analyze the large set of trading heuristics discussed in top finance journal articles over the past 75 years using both retail and institutional trade-level data. We find that retail investors use about 70% of the heuristics more often than would be expected based on counterfactual simulated trading data. Retail trades using more heuristics are associated with lower future returns, with more than half of stock selection heuristics negatively linked to future performance. Institutions, in contrast, use only 15% of the heuristics more often than in the counterfactual data and benefit from their use. We model and empirically analyze the decision to use heuristics. We find heuristic use is more likely to persist for higher utility and better performing heuristics. We also find heuristic use is greater among female and larger retail investors.
with Ning Zhu
with Ning Zhu
Investors perceive cash in savings accounts (“cold cash”) differently from cash recycled in brokerage accounts (“hot cash”). We propose a novel “temperature” framework for dynamic and repeated mental account refreshing process. We find individual investors buy stocks more cautiously with colder cash. Exploiting a quasi-natural experiment of the Chinese IPO-lottery reform, we show the effect is causal. An online experiment indicates the differential loss aversion is a potential channel. Building on observational and experimental findings, we propose a model featuring preferences with temperature-dependent sensitivity to future gains-and-losses, which generates our empirical patterns and provides a cash-temperature perspective for other puzzles.
with Yucheng Liang
with Yucheng Liang
This study explores how the historical price path of a stock shapes investors’ perceptions of risk. In a series of experiments, we present participants with real and fabricated stock price charts and elicit their risk perceptions. We document that a parsimonious set of three conceptually independent features, i.e., recency, clustering, and sign, can explain large proportions of the variation in risk perceptions. We find that the effects of these three features are partially mediated by their impact on perceived volatility, which is not fully explained by Bayesian inference. In addition, a significant portion of the features’ effects on risk perceptions occurs independently of perceived volatility. Using U.S. real stock and mutual fund data, we show that these three features help explain cross-sectional variations in returns, trading volume, and future volatility of individual stocks, and predict mutual fund negative flows.
This paper studies how investors evaluate themselves and its implications. In my model, investors form subjective beliefs about both the stock currently held in portfolio and their trading talent and update their beliefs through learning with fading memory. I calibrate the memory decay parameters to account-level trading records and show that beliefs updating for talent is about 7 times more sensitive to return signals than that for the stock in portfolio. Consequently, the model predicts that stock replacing typically happens after a good performance of the existing stock, providing a dual learning perspective on the disposition effect. This framework also accounts for the widely documented performance-contingent trading intensity and investor attrition, which cannot be reconciled with the decreasing-gain property of the standard Bayesian learning.
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