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Dead alphas as risk factors

Author

Listed:
  • Zura Kakushadze

    (Quantigic® Solutions LLC
    Free University of Tbilisi)

  • Willie Yu

    (Duke-NUS Medical School)

Abstract

We give an explicit algorithm and source code for extracting equity risk factors from dead (a.k.a. “flatlined” or “hockey-stick”) alphas and using them to improve performance characteristics of good (tradable) alphas. In a nutshell, we use dead alphas to extract directions in the space of stock returns along which there is no money to be made (and/or those bets are too volatile). In practice, the number of dead alphas can be large compared with the number of underlying stocks and care is required in identifying the aforesaid directions.

Suggested Citation

  • Zura Kakushadze & Willie Yu, 2018. "Dead alphas as risk factors," Journal of Asset Management, Palgrave Macmillan, vol. 19(2), pages 110-115, March.
    • Handle: RePEc:pal:assmgt:v:19:y:2018:i:2:d:10.1057_s41260-017-0064-5
    DOI: 10.1057/s41260-017-0064-5
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    References listed on IDEAS

    as
    1. Zura Kakushadze & Willie Yu, 2017. "Decoding Stock Market with Quant Alphas," Papers 1708.02984, arXiv.org.
    2. Zura Kakushadze & Willie Yu, 2017. "How to combine a billion alphas," Journal of Asset Management, Palgrave Macmillan, vol. 18(1), pages 64-80, January.
    3. Zura Kakushadze, 2016. "101 Formulaic Alphas," Papers 1601.00991, arXiv.org, revised Mar 2016.
    4. Zura Kakushadze & Willie Yu, 2016. "Statistical Risk Models," Papers 1602.08070, arXiv.org, revised Jan 2017.
    5. Zura Kakushadze, 2015. "Heterotic Risk Models," Papers 1508.04883, arXiv.org, revised Jan 2016.
    6. Zura Kakushadze & Willie Yu, 2016. "Multifactor Risk Models and Heterotic CAPM," Papers 1602.04902, arXiv.org, revised Mar 2016.
    Full references (including those not matched with items on IDEAS)

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