Crowdsourced earnings forecasts provide incremental information in measuring market earnings expectations and are less pessimistically biased compared to sell-side forecasts. Given these attributes, we examine whether crowdsourced earnings forecasts help investors adjust their responses to earnings news by increasing the salience of sell-side forecast bias. Using crowdsourced forecasts from Estimize, we first find that the market's response to earnings news is substantially attenuated in situations in which crowdsourced and sell-side forecasts lead to contrasting earnings surprise signals (i.e., firms meet or beat one forecast but miss the other). Next, we find that the market premium to meeting sell-side expectations is eliminated when firms miss crowdsourced expectations, and that investors anticipate and price the predictive value of differences between crowdsourced and sell-side forecasts before earnings announcements. Overall, we conclude that crowdsourced earnings forecasts can increase the salience of sell-side forecast bias and help investors unravel bias in earnings expectations.
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