Effects of Information Overload on Financial Markets: How Much Is Too Much?

Abstract

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.