Crowdsourced Earnings Expectations and the Market Reaction to Street Earnings Surprises
57 Pages Posted: 4 Sep 2019 Last revised: 1 Sep 2020
Date Written: August 31, 2020
Crowdsourced earnings forecasts are less pessimistically biased than Wall Street (i.e., sell-side) analysts' forecasts before earnings announcements. Based on this observation, we examine how crowdsourced forecasts influence investors' evaluation of Street earnings surprises. Using crowdsourced estimates from Estimize, we find that investors discount positive Street earnings surprises when firms simultaneously miss crowdsourced expectations, and that the incremental price premium associated with meeting or (just) beating sell-side expectations is largely eliminated when firms miss the crowdsourced consensus. Consequently, we observe significant declines in insider selling activity after earnings announcements when firms meet or beat sell-side, but miss crowdsourced expectations. Using Estimize web traffic data, we also construct a measure of attention to crowdsourced information and confirm that investors use crowdsourced estimates in processing earnings news. Overall, we conclude that crowdsourced forecasts help investors unravel sell-side forecast bias and affect managers' ability to profit from meeting or beating sell-side expectations.
Keywords: Crowdsourcing, Estimize, sell-side analysts, forecast bias, earnings announcements, market reactions, insider trading
JEL Classification: G14, G20, M41
Suggested Citation: Suggested Citation