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Analyst Disagreement and Aggregate Volatility Risk

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  • Barinov, Alexander

Abstract

The paper explains why firms with high dispersion of analyst forecasts earn low future returns. These firms beat the capital asset pricing model in periods of increasing aggregate volatility and thereby provide a hedge against aggregate volatility risk. The aggregate volatility risk factor can explain the abnormal return differential between high- and low-disagreement firms. This return differential is higher for firms with abundant real options, and this fact can be explained by aggregate volatility risk. Aggregate volatility risk can also explain why the link between analyst disagreement and future returns is stronger for firms with high short-sale constraints.

Suggested Citation

  • Barinov, Alexander, 2013. "Analyst Disagreement and Aggregate Volatility Risk," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(6), pages 1877-1900, December.
    • Handle: RePEc:cup:jfinqa:v:48:y:2013:i:06:p:1877-1900_00
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    Cited by:

    1. Ackert, Lucy F. & Kluger, Brian D. & Qi, Li, 2019. "Implied volatility and investor beliefs in experimental asset markets," Journal of Financial Markets, Elsevier, vol. 43(C), pages 121-136.
    2. Barinov, Alexander, 2023. "Profitability anomaly and aggregate volatility risk," Journal of Financial Markets, Elsevier, vol. 64(C).
    3. Barinov, Alexander, 2017. "Institutional ownership and aggregate volatility risk," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 20-38.
    4. Sheng, Xuguang (Simon) & Thevenot, Maya, 2015. "Quantifying differential interpretation of public information using financial analysts’ earnings forecasts," International Journal of Forecasting, Elsevier, vol. 31(2), pages 515-530.
    5. David Veenman & Patrick Verwijmeren, 2022. "The Earnings Expectations Game and the Dispersion Anomaly," Management Science, INFORMS, vol. 68(4), pages 3129-3149, April.
    6. Barinov, Alexander, 2015. "Why does higher variability of trading activity predict lower expected returns?," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 457-470.
    7. Max Schreder & Pawel Bilinski, 2022. "Information Quality and the Expected Rate of Return: A Structural Equation Modelling Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(2), pages 139-170, June.
    8. Barinov, Alexander, 2018. "Stocks with extreme past returns: Lotteries or insurance?," Journal of Financial Economics, Elsevier, vol. 129(3), pages 458-478.
    9. Barinov, Alexander & Wu, Juan (Julie), 2014. "High short interest effect and aggregate volatility risk," Journal of Financial Markets, Elsevier, vol. 21(C), pages 98-122.
    10. Chung, Kee H. & Wang, Junbo & Wu, Chunchi, 2019. "Volatility and the cross-section of corporate bond returns," Journal of Financial Economics, Elsevier, vol. 133(2), pages 397-417.

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