Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 30th Sept 2023, 07:50:15am CEST
Wisdom of the Institutional Crowd: Implications for Anomaly Returns
AJ Chen, Gerard Hoberg, Miao Ben Zhang
University of Southern California, United States of America
Discussant: Jonathan Brogaard (University of Utah)
We hypothesize that when price correction requires more capital than any one investor can provide, institutions coordinate trading via crowd-sourcing in the media. When the crowd reaches a consensus, synchronized trading occurs, prices are corrected, and anomaly returns result. We use over one million Wall Street Journal articles from 1980 to 2020 to develop a novel textual measure of institutional investors making predictions in the media (InstPred). We show that (i) both value and momentum anomaly returns are 34\% to 63\% larger when InstPred is higher, and (ii) institutional investors collectively trade the anomalies more aggressively when InstPred is higher. Our results are reinforced by tests using quasi-exogenous variation in temporal investor-WSJ connections and cannot be explained by existing measures such as document tone.
When Crowds Aren’t Wise: Biased Signals From Investor Social Networks and its Price Impact
Edna Lopez Avila, Charles Martineau, Jordi Mondria
University of Toronto, Canada
Discussant: Roberto Gomez (London Business School)
We examine information production surrounding earnings announcements on leading investment social networks. In aggregate, information production on social networks displays excessive positively skewed optimism about future outcomes on earnings announcements. Such biased optimism does not predict fundamentals on earnings announcements. It leads to buying pressure and price run-ups before earnings announcements, thus, distorting prices from fundamentals before earnings announcements with negative news. For rarer cases of extreme pessimism, we find selling price pressure before positive and negative earnings news. Our findings cast doubt on the wisdom of the crowd phenomenon from social networks in forecasting future fundamentals and having a beneficial role in price efficiency.