Tweet Sentiments and Crowd-Sourced Earnings Estimates as Valuable Sources of Information Around Earnings Releases

Abstract

In this work we examine the confluence of two important financial social media databases -- Estimize and iSentium. Both data capture “crowdsourced” information that has begun to appear increasingly more important for financial market research. In particular we investigate the event of the earnings announcement. First, we confirm that crowdsourced/Estimize’s consensus earnings have more accuracy ( 60%) than Wall Street’s consensus earnings and this has been robust over the past two years 2013-2014. Second, we document that the objectivity of the crowd has been one reason why it is more accurate. Wall Street’s consensus is biased due to the “lowballing” phenomenon pervasive in the industry. Wall Street’s consensus are ~70% lower than the actual reported earnings versus ~56-57% lower from the crowd’s consensus. Third, we find economically and statistically significant evidence that tweet sentiment contains distinct information that is not contained in the traditional pre-announcement variables such as Forecasts Error, Earnings Surprise, Bias, Coverage, Track Record, and Earnings Volatility. Fourth, we show that Tweet sentiment prior to the earnings announcement date can actually predict post-announcement risk-adjusted excess returns over the short-term (few days). This predictive relationship holds even in the presence of the Earnings Surprise variable. Fascinatingly enough the market quickly incorporates this information and after only a few days the statistical significance of this relationship wanes. We estimate that gross of costs, the “alpha” from tweet sentiments post-earnings announcement may be as high as ~10-20% per year.