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Option characteristics as cross-sectional predictors

Author

Listed:
  • Neuhierl, Andreas
  • Tang, Xiaoxiao
  • Varneskov, Rasmus Tangsgaard
  • Zhou, Guofu

Abstract

We provide the first comprehensive analysis of option information for pricing the cross-section of stock returns by jointly examining extensive sets of firm and option characteristics. Using portfolio sorts and high-dimensional methods, we show that certain option measures have significant predictive power, even after controlling for firm characteristics, earning a Fama-French three-factor alpha in excess of 20% per annum. Our analysis further reveals that the strongest option characteristics are associated with information about asset mispricing and future tail return realizations. Our findings are consistent with models of informed trading and limits to arbitrage.

Suggested Citation

  • Neuhierl, Andreas & Tang, Xiaoxiao & Varneskov, Rasmus Tangsgaard & Zhou, Guofu, 2022. "Option characteristics as cross-sectional predictors," LawFin Working Paper Series 37, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).
    • Handle: RePEc:zbw:lawfin:37
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Asset Pricing; Factor Models; High-dimensional Methods; Option Characteristics;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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