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Predicting Extreme Returns And Portfolio Management Implications

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  • Andy Fodor
  • Kevin Krieger
  • Nathan Mauck
  • Greg Stevenson

Abstract

We consider which readily observable characteristics of individual stocks (e.g., option implied volatility, accounting data, analyst data) may be used to forecast subsequent extreme price movements. We are the first to explicitly consider the predictive influence of option implied volatility in such a framework, which we unsurprisingly find to be an important indicator of future extreme price movements. However, after controlling for implied volatility levels, other factors, particularly firm age and size, still have additional predictive power of extreme future returns. Furthermore, excluding predicted extreme return stocks leads to a portfolio that has lower risk (standard deviation of returns) without sacrificing performance.
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Suggested Citation

  • Andy Fodor & Kevin Krieger & Nathan Mauck & Greg Stevenson, 2013. "Predicting Extreme Returns And Portfolio Management Implications," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 36(4), pages 471-492, December.
    • Handle: RePEc:bla:jfnres:v:36:y:2013:i:4:p:471-492
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    File URL: http://hdl.handle.net/10.1111/jfir.12020
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    Cited by:

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    3. Feng Sun & Cheng Liu & Xiaoguang Zhou, 2017. "Analysis of industry risk premium with MVS three dimensions vector factor model," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1374814-137, January.

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G00 - Financial Economics - - General - - - General

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