Quantifying differential interpretation of public information using financial analysts' earnings forecasts

Based on a standard Bayesian learning model, we propose a new measure of differential interpretation of public information, which is applicable to firms with analyst following. We validate our measure in the context of earnings announcements and provide evidence of its greater applicability, relative to a number of previously used proxies, such as the change in dispersion, Kandel and Pearson's (1995) metric, abnormal volume and the bid-ask spread. We find that the new measure of differential interpretation is related positively to other commonly used proxies, namely trading volume, disclosure informativeness, and the cost of capital, and is related negatively to disclosure readability and management guidance precision. This more precise measure of opinion divergence will enable researchers to pursue studies that were previously difficult to conduct.