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: 19th May 2024, 05:27:11am CEST
1University of Colorado Boulder, United States of America; 2Norwegian School of Economics
Discussant: Ryan Israelsen (Michigan State University)
We develop a new, topics based method of classifying content by identifying the characteristics of named entities through their wikipedia entry. We apply the method to a new corpora - the closed caption data from three major TV news networks (CNN, FOXNEWS and MSNBC) and three business TV news networks (Bloomberg TV, FOX Business and CNBC). TV news networks have converged around content discussed over the last decade, but diverged in sentiment around the topics of politics, race, religion, gender, and sexual orientation. Polarization in modern news media appears to be more about how topics are presented rather than what topics are discussed. Across business news networks we find starker differences in content: CNBC and Bloomberg appear to be focused more on economics and market related content relative to FOX Business, which maintains the political tilt of its news only affiliate.
Gender Bias and Crowd-Sourced Financial Information
Vineet Bhagwat1, Chukwuma Dim1, Sara E. Shirley2, Jeffrey R. Stark2
1George Washington University; 2Middle Tennessee State University
Discussant: Russell Jame (University of Kentucky)
The capacity to aggregate information from diverse perspectives has positioned social finance forums as a potent source of signals that shape investors’ beliefs and actions. We investigate how investors react to the information provided by male and female non-professional analysts on social platforms and the financial market consequences. Although male and female contributors exhibit similar informativeness and skills, female-authored perspectives receive significantly lower engagement, lower trust, and higher disagreement from platform users. The higher disagreement is subsequently associated with higher abnormal trading volume and lower price efficiency. Additional analysis indicates that the platform consensus becomes less informative about future cash flows when female contributors quit due to their less favorable engagement.
