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Cross-Sectional Skewness
[Endogenous information flows and the clustering of announcements]

Author

Listed:
  • Sangmin S Oh
  • Jessica A Wachter

Abstract

What distribution best characterizes the time series and cross-section of individual stock returns? To answer this question, we estimate the degree of cross-sectional return skewness relative to a benchmark that nests many models considered in the literature. We find that cross-sectional skewness in monthly returns far exceeds what this benchmark model predicts. However, cross-sectional skewness in long-run returns in the data is substantially below what the model predicts. We show that fat-tailed idiosyncratic events appear to be necessary to explain skewness in the data. (JEL, G10, G11, G12, G13, G14).

Suggested Citation

  • Sangmin S Oh & Jessica A Wachter, 2022. "Cross-Sectional Skewness [Endogenous information flows and the clustering of announcements]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 12(1), pages 155-198.
    • Handle: RePEc:oup:rasset:v:12:y:2022:i:1:p:155-198.
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    File URL: http://hdl.handle.net/10.1093/rapstu/raab023
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    More about this item

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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