Accounting data, overvaluation, and the cross-section of volatility: industry sector evidence

Abstract

Purpose This study aims to investigate the variation in overvaluation proxies and volatility across industry sectors and time. Design/methodology/approach Using industry sector data from the S&P Capital IQ database, this study applies traditional cross-sectional regressions to investigate the relationship between overvaluation and volatility over the 2001–2020 time period. Findings This study finds that the most volatile industry sectors generally do not coincide with overvalued industry sectors in the cross-section, implying that there are limitations to price-multiple methods for forecasting future volatility. Rather, this study finds that historical volatility significantly increases the goodness-of-fit when modeling volatility in the cross section of industry sectors. The findings of this study imply that firms should increase disclosures and transparency about corporate practices to decrease downside risk that stems from bad news. In addition, the findings underline the consistency between market efficiency and high levels of volatility in periods of significant uncertainty. Originality/value This study proposes a novel approach to examining the cross section of volatility across time for industry sectors.