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The Low-Volatility Anomaly And The Adaptive Multi-Factor Model

Author

Listed:
  • ROBERT A. JARROW

    (Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, NY 14853, USA)

  • RINALD MURATAJ

    (T. Rowe Price, Baltimore, MD 21202, USA)

  • MARTIN T. WELLS

    (Department of Statistics and Data Science, Cornell University, Ithaca, NY 14853, USA)

  • LIAO ZHU

    (Department of Statistics and Data Science, Cornell University, Ithaca, NY 14853, USA)

Abstract

The paper provides a new explanation of the low-volatility anomaly. We use the Adaptive Multi-Factor (AMF) model estimated by the Groupwise Interpretable Basis Selection (GIBS) algorithm to find those basis assets significantly related to low and high volatility portfolios. These two portfolios load on very different basis assets, indicating that volatility is not an independent risk, but that it is related to existing risk factors. The out-performance of the low-volatility portfolio is due to the (equilibrium) performance of these loaded risk factors, specifically, the better long-term performance of the asset classes bonds and real estate as contrasted with materials, precious metals, and the healthcare industry. Our methodology is applicable to any long–short anomaly but we focus on the low-volatility anomaly since it is formed explicitly on the risk characteristic rather than on embedded risks of other anomalies. The AMF model outperforms the Fama–French 5-factor model significantly both in-sample and out-of-sample.

Suggested Citation

  • Robert A. Jarrow & Rinald Murataj & Martin T. Wells & Liao Zhu, 2023. "The Low-Volatility Anomaly And The Adaptive Multi-Factor Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 26(04n05), pages 1-33, August.
  • Handle: RePEc:wsi:ijtafx:v:26:y:2023:i:04n05:n:s0219024923500206
    DOI: 10.1142/S0219024923500206
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    1. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    2. Ang, Andrew & Hodrick, Robert J. & Xing, Yuhang & Zhang, Xiaoyan, 2009. "High idiosyncratic volatility and low returns: International and further U.S. evidence," Journal of Financial Economics, Elsevier, vol. 91(1), pages 1-23, January.
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    4. Bien, Jacob & Tibshirani, Robert, 2011. "Hierarchical Clustering With Prototypes via Minimax Linkage," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1075-1084.
    5. Frazzini, Andrea & Pedersen, Lasse Heje, 2014. "Betting against beta," Journal of Financial Economics, Elsevier, vol. 111(1), pages 1-25.
    6. Stephen Reid & Jonathan Taylor & Robert Tibshirani, 2018. "A General Framework for Estimation and Inference From Clusters of Features," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 280-293, January.
    7. Bali, Turan G. & Brown, Stephen J. & Murray, Scott & Tang, Yi, 2017. "A Lottery-Demand-Based Explanation of the Beta Anomaly," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(6), pages 2369-2397, December.
    8. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    9. McInish, Thomas H & Wood, Robert A, 1986. "Adjusting for Beta Bias: An Assessment of Alternative Techniques: A Note," Journal of Finance, American Finance Association, vol. 41(1), pages 277-286, March.
    10. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    11. Shumway, Tyler, 1997. "The Delisting Bias in CRSP Data," Journal of Finance, American Finance Association, vol. 52(1), pages 327-340, March.
    12. Black, Fischer, 1972. "Capital Market Equilibrium with Restricted Borrowing," The Journal of Business, University of Chicago Press, vol. 45(3), pages 444-455, July.
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    Cited by:

    1. Liao Zhu & Haoxuan Wu & Martin T. Wells, 2021. "A News-based Machine Learning Model for Adaptive Asset Pricing," Papers 2106.07103, arXiv.org.
    2. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.

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