Exploiting Crowdsourced Earnings Estimates and Stock Sentiments
September 16, 2015 - Investing Expertise, Sentiment Indicators
Are readily available crowdsourced firm earnings estimates and stock sentiment measurements exploitable? In the September 2015 revision of their paper entitled “Tweet Sentiments and Crowd-Sourced Earnings Estimates as Valuable Sources of Information Around Earnings Releases”, Jim Kyung-Soo Liew, Shenghan Guo and Tongli Zhang investigate whether earnings estimates from Estimize and sentiment measurements from iSentium usefully predict stock behavior after earnings announcements. Estimize aggregates inputs from students, independent researchers, private investors, sell-side professionals and buy-side analysts to generate earnings estimates. iSentium derives sentiment scores (ranging from -30 to +30) from real-time natural language processing of Twitter texts about stocks, market indexes and exchange-traded funds. The authors relate pre-announcement earnings estimates and sentiment to post-earnings announcement stock returns. Using Estimize and iSentium data as available, Wall Street consensus earnings estimates, actual firm quarterly earnings and associated stock returns for 16,840 earnings announcements during November 2011 through December 2014, they find that: Keep Reading