Dispersed Information and Asset Prices: Theory and Measurement joint with Elias Albagli and Aleh Tsyvinski

Abstract:

We argue that noisy information aggregation of dispersed information, along the lines of Grossman and Stiglitz (1980), Hellwig (1980), and Diamond and Verrecchia (1981), provides a unified explanation for several empirical asset pricing phenomena, including excess volatility, returns to skewness and forecast disagreement in equity and bond markets. In contrast to most of the existing literature on noisy information aggregation, we do not impose any parametric restrictions on preferences, information or return distributions, but instead offer a unified, general characterization of asset prices through the lens of an information-adjusted risk-neutral measure, and show that this risk-neutral measure displays excess weight on tail risks. We then calibrate the magnitude of information aggregation frictions using data on firms’ earnings forecasts and show that the model replicates both qualitatively and quantitatively the observed returns to skewness and disagreement.