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Decoding Stock Market with Quant Alphas

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  • Zura Kakushadze
  • Willie Yu

Abstract

We give an explicit algorithm and source code for extracting expected returns for stocks from expected returns for alphas. Our algorithm altogether bypasses combining alphas with weights into "alpha combos". Simply put, we have developed a new method for trading alphas which does not involve combining them. This yields substantial cost savings as alpha combos cost hedge funds around 3% of the P&L, while alphas themselves cost around 10%. Also, the extra layer of alpha combos, which our new method avoids, adds noise and suboptimality. We also arrive at our algorithm independently by explicitly constructing alpha risk models based on position data.

Suggested Citation

  • Zura Kakushadze & Willie Yu, 2017. "Decoding Stock Market with Quant Alphas," Papers 1708.02984, arXiv.org.
    • Handle: RePEc:arx:papers:1708.02984
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    File URL: http://arxiv.org/pdf/1708.02984
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    References listed on IDEAS

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    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. Zura Kakushadze & Willie Yu, 2016. "Statistical Risk Models," Papers 1602.08070, arXiv.org, revised Jan 2017.
    3. Zura Kakushadze, 2014. "Factor Models for Alpha Streams," Papers 1406.3396, arXiv.org, revised Oct 2014.
    4. Zura Kakushadze, 2015. "Heterotic Risk Models," Papers 1508.04883, arXiv.org, revised Jan 2016.
    5. Zura Kakushadze & Willie Yu, 2016. "Multifactor Risk Models and Heterotic CAPM," Papers 1602.04902, arXiv.org, revised Mar 2016.
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    Cited by:

    1. Zura Kakushadze & Willie Yu, 2017. "Dead Alphas as Risk Factors," Papers 1709.06641, arXiv.org.
    2. Zura Kakushadze & Willie Yu, 2018. "Dead alphas as risk factors," Journal of Asset Management, Palgrave Macmillan, vol. 19(2), pages 110-115, March.

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