Crowdsourced Earnings Expectations and the Salience of Sell-Side Forecast Bias

Abstract

Researchers and market participants traditionally rely on sell-side analyst forecasts to determine the news content of an earnings announcement, despite widespread evidence that analysts' short-term forecasts are pessimistically biased. This paper examines whether crowdsourced earnings forecasts, which are less biased and not subject to the conflicts of interest in sell-side research, raise investor awareness of analyst forecast bias around earnings announcements. We find investors discount the earnings news of firms that meet or beat analyst expectations when earnings simultaneously miss crowdsourced expectations, and that this discount increases with investor attention to crowdsourced forecasts. We also demonstrate that the market efficiently prices the predictable bias in analyst-based earnings surprises based on information in crowdsourced forecasts. Lastly, we find that missing crowdsourced expectations limits managers' ability to profit from insider sales after meeting or beating analyst expectations. Overall, we conclude that crowdsourced earnings forecasts help investors better process and debias earnings news.