The Value of Crowdsourced Earnings Forecasts

Abstract

Crowdsourcing—when a task normally performed by employees is outsourced to a large network of people via an open call—is making inroads into the investment research industry. We shed light on this new phenomenon by examining the value of crowdsourced earnings forecasts. Our sample includes 51,012 forecasts provided by Estimize, an open platform that solicits and reports forecasts from over 3,000 contributors. We find that Estimize forecasts are incrementally useful in forecasting earnings and measuring the market's expectations of earnings. Our results are stronger when the number of Estimize contributors is larger, consistent with the benefits of crowdsourcing increasing with the size of the crowd. Finally, Estimize consensus revisions generate significant two‐day size‐adjusted returns. The combined evidence suggests that crowdsourced forecasts are a useful supplementary source of information in capital markets.

The effect of carbon dissemination on cost of equity

“…In contrast, tweets disseminated by firms are short and independent of media adjustments, which make them most likely to be used for disseminating purposes rather than for providing comprehensive information. Finally, although previous studies (Bartov, Faurel, & Mohanram, ; Chen, De, Hu, & Hwang, ; Jame, Johnston, Markov, & Wolfe, ) examine the effect of user‐granted information over social media on capital market activity, we focus more on firm granted information. Prior work shows how firms' dissemination on Twitter improves market liquidity (Blankespoor et al, ; Prokofieva, ) and attenuates negative market reaction to product recalls (Lee, Hutton, & Shu, ) and acquisition announcements (Mazboudi & Khalil, ), to the best of our knowledge, no study has examined the effect of the Twitter dissemination of carbon‐specific information on the COE.…”

“Deliberated Intuition” in Stock Price Forecasting

“…Numerous factors with influence on individual decision making have been identified (e.g., Jackson et al, 2017;Nguyen, 2018;Tetlock & Gardner, 2015). However, there is limited literature about price predictions based on a crowdsourced approach (e.g., Chen et al, 2018;Jame et al, 2016, Kaplan 2001.…”

Does Monetary Incentive Lead to Better Stock Recommendations on Social Media?

“…Chen et al (2014) use articles and comments posted on Seeking Alpha and show that stock opinions on social media can predict the future performance of individual stocks in the long run. Jame et al (2016) use crowdsourced earnings forecasts from Estimize and show that earnings forecasts posted by amateur analysts are incrementally useful in forecasting earnings and measuring the market's expectations. Xie et al (2017) investigate how network structure plays a role in affecting the prediction accuracy of social media analytics for financial markets.…”