Forecasting Market Volatility: The Role of Earnings Announcements

Abstract

This study examines whether information revealed by firms’ earnings announcements (EAs) forecasts short-run market-wide volatility in equity index prices. Using an exponential generalized autoregressive conditional heteroskedasticity model that includes controls for the information in an array of macroeconomic announcements, we find that EA information aggregated across firms forecasts market volatility at daily and weekly intervals. EA information’s forecasting power is greatest when more firms announce earnings on a given day, when EAs convey negative news, and for EA information about core earnings. Out-of-sample tests confirm that forecasts incorporating EA information better predict short-run market volatility than forecasts omitting EA information. We conclude that firm-level EAs are a significant source of systematic, market-wide information relevant for predicting near-term market volatility. Data Availability: All data are publicly available from sources cited in the text. JEL Classifications: E44; G12; M41.