Jaewoo Kim, B. Schonberger, Charles E. Wasley, Yucheng Yang
{"title":"Forecasting Market Volatility: The Role of Earnings Announcements","authors":"Jaewoo Kim, B. Schonberger, Charles E. Wasley, Yucheng Yang","doi":"10.2308/tar-2021-0351","DOIUrl":null,"url":null,"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.","PeriodicalId":22240,"journal":{"name":"The Accounting Review","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Accounting Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2308/tar-2021-0351","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.
本研究探讨了企业盈利公告（EA）所揭示的信息是否能预测股票指数价格的短期全市场波动。通过使用一个指数广义自回归条件异方差模型（该模型包含了对一系列宏观经济公告信息的控制），我们发现，各公司的盈利公告信息总和可以预测每日和每周的市场波动。当某一天有更多公司公布盈利、当 EA 传达负面消息以及当 EA 信息涉及核心盈利时，EA 信息的预测能力最强。样本外测试证实，与忽略 EA 信息的预测相比，包含 EA 信息的预测能更好地预测短期市场波动。我们的结论是，公司层面的 EA 是与预测近期市场波动相关的系统性、全市场信息的重要来源。 数据可用性：所有数据均可通过文中引用的来源公开获取。 JEL 分类：E44; G12; M41.
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