Finance Seminar: Xing Huang, Washington University in St. Louis
Extrapolative Beliefs in the Cross-section: What Can We Learn from the Crowds?
Abstract: Using novel data from a crowdsourcing platform for ranking stocks, we investigate how individuals form expectations about future stock returns in the cross-section. In each contest on this platform, participants rank 10 stocks based on their perceived future performance of these stocks over the course of the contest (usually one week). We find that, when forming expectations, investors extrapolate from past returns, with more weight on more recent returns, especially when recent returns are negative. The extrapolation bias is stronger among Forcerank users who are not financial professionals. Moreover, consensus rankings negatively predict future stock returns in the cross-section, more so among stocks with low institutional ownership and a high degree of extrapolative bias, consistent with the asset pricing implications of extrapolative beliefs. This return predictability extends to large stocks that are not covered on the platform and is not driven by liquidity-shock-induced price reversals. Finally, the residual component of the consensus rankings orthogonal to past stock returns also negatively predicts future returns, suggesting that investor sentiment is above and beyond return extrapolation.
Paper written with Zhi Da and Lawrence Jin.
Finance Seminars at Caltech are funded through the generous support of The Ronald and Maxine Linde Institute of Economic and Management Sciences (lindeinstitute.caltech.edu) and Stephen A. Ross.
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