1. Thinking Outside the Borders: Investors' Underreaction to Foreign Operations
2015, Review of Financial Studies 28, 3109-3152.
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I use industry-level returns in foreign markets to examine the hypothesis that value-relevant foreign information slowly diffuses into the stock prices of U.S. multinational firms. A trading strategy that exploits foreign information generates abnormal returns of 0.8% monthly. I find that the market responds more slowly in periods with lower media coverage of foreign news and to information from more linguistically and culturally distant countries. These results suggest that both investors’ inattention and lack of understanding of foreign information slow the incorporation of new information into prices. I further separate these two mechanisms by examining market responses to earnings surprises.
2. Rushing into American Dream? House Prices Growth and the Timing of Homeownership
(with Sumit Agarwal, Luojia Hu)
2016, Review of Finance 20, 2183-2218.
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We use the New York Fed Consumer Credit Panel data set to empirically examine how past house price growth influences the timing of homeownership. We find that the median individual in metropolitan areas with the highest quartile house price growth becomes a homeowner 5 years earlier than that in areas with the lowest quartile house price growth. The result is consistent with a life cycle housing-demand model in which high past price growth increases expectations of future price growth thus accelerating home purchases at young ages. We show that extrapolative expectations formed by homebuyers are a necessary channel to explain the result.
3. Which Factors Matter to Investors? Evidence from Mutual Fund Flows
(with Brad Barber, Terrance Odean)
2016, Review of Financial Studies 29, 2643-2676.
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When assessing a fund manager’s skill, sophisticated investors will consider all factors (priced and unpriced) that explain cross-sectional variation in fund performance. We investigate which factors investors attend to by analyzing mutual fund flows as a function of recent returns decomposed into alpha and factor-related returns. Surprisingly, investors attend most to market risk (beta) when evaluating funds and treat returns attributable to size, value, momentum, and industry factors as alpha. Using proxies for investor sophistication (wealth, distribution channels, and periods of high investor sentiment), we find that more sophisticated investors use more sophisticated benchmarks when evaluating fund performance.
4. Mark Twain’s Cat: Industry Investment Experience, Categorical Thinking and Stock Selection
2019, Journal of Financial Economics 131, 404-432.
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This paper examines the effect of prior investment experience in specific industries on subsequent investment decisions. Using households’ trading records from a large discount broker between 1991 and 1996, I find that prior success in a given industry increases the likelihood of subsequent purchases in the same industry. The effect is stronger for more recent experiences and for less sophisticated or diversified investors, and is not wealth enhancing. The results suggest investors categorize industries at a highly resolved level, finer than the Fama-French 10-industry classification. Similar effects are also apparent for size- and value-based categories, but at smaller magnitudes.
5. Harnessing the Wisdom of Crowds
(with Zhi Da)
2020, Management Science 66, 1847-1867.
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We empirically examine the impact of herding on the accuracy of consensus earnings forecasts from a crowd-based platform (Estimize.com). We monitor user information set by tracking user viewing activities and running experiments. We find that the more public information a user views, the less weight she puts on her private information. While this improves the accuracy of individual forecasts, it reduces the accuracy of the consensus forecast. The experiments convince Estimize.com to switch to a “blind” platform from November 2015. Overall, our findings suggest that the wisdom of crowds can be better harnessed by encouraging independent voices from the participants, and that more public information disclosure may not always improve group decision making.
6. Extrapolative Beliefs in the Cross-section: What Can We Learn from the Crowds?
(with Zhi Da, Lawrence Jin)
2021, Journal of Financial Economics 140, 175-196.
[ Link ]
Using novel data from a crowdsourcing platform for ranking stocks, we investigate how investors form expectations about stock returns over the next week. We find that investors extrapolate from stocks' recent past returns, with more weight on more recent returns, especially when recent returns are negative, salient, or from a dispersed cross-section. Such extrapolative beliefs are stronger among nonprofessionals and large stocks. Moreover, consensus rankings negatively predict returns over the next week, more so among stocks with low institutional ownership and a high degree of extrapolation. A trading strategy that sorts stocks on investor beliefs generates an economically significant profit.
7. Attention-Induced Trading and Returns: Evidence from Robinhood Users
(with Brad Barber, Terrance Odean, Chris Schwarz)
2021, Accepted at Journal of Finance
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We study the influence of financial innovation by fintech brokerages on individual investors’ trading and stock prices. Using data from Robinhood, we find that Robinhood investors engage in more attention-induced trading than other retail investors. For example, Robinhood outages disproportionately reduce trading in high-attention stocks. While this evidence is consistent with Robinhood attracting relatively inexperienced investors, we show that it can also be partially driven by the app’s unique features. Consistent with models of attention-induced trading, intense buying by Robinhood users forecast negative returns. Average 20-day abnormal returns are -4.7% for the top stocks purchased each day.