Forecasting Market Volatility: The Role of Earnings Announcements

• 成果类型: Article
• 署名作者: Kim, Jaewoo; Schonberger, Bryce; Wasley, Charles; Yang, Yucheng (John)
• 署名单位: University of Oregon; University of Colorado System; University of Colorado Boulder; University of Rochester; Chinese University of Hong Kong
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/TAR-2021-0351
• 发表日期: 2024
• 页码: 251-279
• 关键词: Macroeconomic news; Aggregate earnings; information-content; business cycles; STOCK; returns; POLICY; RISK

摘要

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 firmlevel EAs are a significant source of systematic, market-wide information relevant for predicting near-term market volatility.