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Consecutive Earnings Surprises: Small and Large Trader Reactions

The Accounting Review 2012 87(5), 1709-1736
ABSTRACT Prior research demonstrates that investors respond differently to earnings surprises that are part of a string of consecutive earnings increases or surprises than to those that are not. To shed light on who values these patterns, I compare trading responses of small and large traders to earnings surprises that occur during a series of positive or negative surprises. I find that the relative intensity of small traders' trading response (and, to a lesser extent, that of medium traders) to earnings surprises generally increases as a series progresses. Small traders respond more negatively to the second (third) negative surprise in a series than to the first (second), and more positively for the first three surprises in a positive series. Moreover, I find that announcement-period returns are related to the trading of small and medium traders. These results suggest that less sophisticated smaller traders, responding to earnings series, contribute to previously documented pricing patterns. Data Availability: All data used in this study, with the exception of data obtained from an anonymous discount brokerage firm, are publicly available from the sources indicated in the text.

Local Bias in Google Search and the Market Response around Earnings Announcements

The Accounting Review 2017 92(4), 115-143
ABSTRACT We examine the impact of distance on internet search, and the effect of the “local bias” in search on the stock market response around earnings announcements. We find significant local bias in search behavior. Motivated by theories explaining local bias, local information advantage, and familiarity bias, we predict and find that firms with higher local bias in search experience higher bid-ask spreads, lower trading volumes, and lower earnings response coefficients at the time of earnings announcements, consistent with non-local investors relying more than locals on public information announcements. Consistent with local information advantage, we find that in the week prior to the announcement, firms with higher local bias have higher bid-ask spreads, higher trading volumes, and returns that are more predictive of the coming earnings surprise. Consistent with familiarity bias, firms with higher local bias in search experience stronger post-earnings announcement drift. We use unique predictions, propensity score matching, and two-stage least squares to identify the effects of local bias separately from the effects of overall visibility. Overall, we show there is significant local bias in search, and that this local bias has a significant impact on the market response around earnings announcements.

Private Firm Investment and Public Peer Misvaluation

The Accounting Review 2019 94(6), 31-60
ABSTRACT We study how public firm misvaluation affects private peer firm investments. An economic competition hypothesis predicts a negative relation because misvaluation-induced new investment by public firms crowds out investment by private peers that share common input or output markets. An alternative shared sentiment hypothesis predicts a positive relation because private firm stakeholders share in the sentiment associated with misvaluation in public markets. Misvaluation is proxied using both the price-to-fundamental ratio and an exogenous instrument obtained from mutual fund flows. The evidence is consistent with the shared sentiment hypothesis, and robust to alternative treatments for growth opportunities. Private firms finance misvaluation-induced investment primarily internally or externally with debt, not equity. Finally, misvaluation-induced investment increases future return on investment for private firms, in contrast with public firms. Overall, these findings suggest that overvaluation in public markets increases private firm investments and has beneficial effects on private firm investments by relaxing financing constraints. JEL Classifications: G32; M41. Data Availability: Data are available from sources identified in the paper.