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, 09:23:26am CEST
1Universidad de Chile; 2Pontificia Universidad Católica de Chile, Chile; 3Federal Reserve Board
Discussant: Theis Ingerslev Jensen (Copenhagen Business School)
Motivated by cognitive theories verifying that investors have limited capacity to process information, we study the effects of information overload on stock market dynamics. We construct an information overload index using textual analysis tools on daily data from The New York Times since 1885. We structure our empirical analysis around a discrete-time learning model, which links information overload with asset prices and trading volume when investors are attention constrained. We find that our index is associated with lower trading volume and predicts higher market returns for up to 18 months, even after controlling for standard predictors and other news-based measures. Information overload also affects the cross-section of stock returns: Investors require higher risk premia to hold small, high beta, high volatile, and unprofitable stocks. Such findings are consistent with theories emphasizing that information overload increases information and estimation risk and deteriorates investors’ decision accuracy amid their limited attention.
(Almost) 200 Years of News-Based Economic Sentiment
Jules H. van Binsbergen1,3, Svetlana Bryzgalova2,4, Mayukh Mukhopadhyay2, Varun Sharma5
1Wharton; 2London Business School; 3NBER; 4CEPR; 5Nanyang Business School
Discussant: Simona Abis (Columbia University)
Using the text of 200 million pages of 13,000 US local newspapers and state-of-the-art machine learning methods, we construct a novel 170-year-long time series measure of economic sentiment at the country and state levels, that expands the existing measures in both the time series (by more than a century) and the cross-section. We show that our measure predicts economic fundamentals such as GDP (both nationally and locally), consumption, and employment growth, even after controlling for commonly used predictors. Our measure is distinct from the information in expert forecasts, and leads its consensus value. We use the text to isolate information about current and future events and show that it is the latter that drives our predictability results.