IDEAS home Printed from https://ideas.repec.org/a/pal/assmgt/v19y2018i1d10.1057_s41260-017-0059-2.html
   My bibliography  Save this article

Decoding stock market with quant alphas

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 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 [This is the last paper in the trilogy, which contains “Factor Models for Alpha Streams” (Kakushadze in J Invest Strateg 4(1): 83–109, 2014) and “How to Combine a Billion Alphas” (Kakushadze and Yu in J Asset Manag 18(1): 1–49, 2017a)]. Forecasting stock returns with quant alphas has implications for the investment industry.

Suggested Citation

  • Zura Kakushadze & Willie Yu, 2018. "Decoding stock market with quant alphas," Journal of Asset Management, Palgrave Macmillan, vol. 19(1), pages 38-48, January.
    • Handle: RePEc:pal:assmgt:v:19:y:2018:i:1:d:10.1057_s41260-017-0059-2
    DOI: 10.1057/s41260-017-0059-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41260-017-0059-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41260-017-0059-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zura Kakushadze & Willie Yu, 2017. "How to combine a billion alphas," Journal of Asset Management, Palgrave Macmillan, vol. 18(1), pages 64-80, January.
    2. Zura Kakushadze, 2015. "Heterotic Risk Models," Papers 1508.04883, arXiv.org, revised Jan 2016.
    3. Zura Kakushadze & Willie Yu, 2016. "Statistical Risk Models," Papers 1602.08070, arXiv.org, revised Jan 2017.
    4. Zura Kakushadze, 2014. "Factor Models for Alpha Streams," Papers 1406.3396, arXiv.org, revised Oct 2014.
    5. 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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zura Kakushadze & Willie Yu, 2017. "Decoding Stock Market with Quant Alphas," Papers 1708.02984, 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.
    3. Kakushadze, Zura & Yu, Willie, 2016. "Factor models for cancer signatures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 527-559.
    4. Zura Kakushadze & Willie Yu, 2017. "Dead Alphas as Risk Factors," Papers 1709.06641, arXiv.org.
    5. Zura Kakushadze & Willie Yu, 2018. "Betas, Benchmarks and Beating the Market," Papers 1807.09919, arXiv.org.
    6. Zura Kakushadze & Willie Yu, 2017. "*K-means and Cluster Models for Cancer Signatures," Papers 1703.00703, arXiv.org, revised Jul 2017.
    7. Zura Kakushadze & Willie Yu, 2016. "Statistical Industry Classification," Papers 1607.04883, arXiv.org, revised Dec 2018.
    8. Zura Kakushadze & Willie Yu, 2017. "Notes on Fano Ratio and Portfolio Optimization," Papers 1711.10640, arXiv.org, revised Apr 2018.
    9. Zura Kakushadze & Willie Yu, 2016. "Factor Models for Cancer Signatures," Papers 1604.08743, arXiv.org, revised Jan 2017.
    10. Zura Kakushadze & Willie Yu, 2019. "Machine Learning Risk Models," Papers 1903.06334, arXiv.org, revised Apr 2019.
    11. Zura Kakushadze, 2020. "Quant Bust 2020," Papers 2006.05632, arXiv.org.
    12. Zura Kakushadze & Willie Yu, 2016. "Statistical Risk Models," Papers 1602.08070, arXiv.org, revised Jan 2017.
    13. Zura Kakushadze & Willie Yu, 2017. "Open Source Fundamental Industry Classification," Data, MDPI, vol. 2(2), pages 1-77, June.
    14. Zura Kakushadze & Willie Yu, 2021. "ETF Risk Models," Papers 2110.07138, arXiv.org.
    15. Zura Kakushadze & Willie Yu, 2017. "How to combine a billion alphas," Journal of Asset Management, Palgrave Macmillan, vol. 18(1), pages 64-80, January.
    16. Zura Kakushadze & Willie Yu, 2016. "How to Combine a Billion Alphas," Papers 1603.05937, arXiv.org, revised Jun 2016.
    17. Zura Kakushadze, 2015. "A Spectral Model of Turnover Reduction," Econometrics, MDPI, vol. 3(3), pages 1-13, July.
    18. Zura Kakushadze, 2015. "Combining Alphas via Bounded Regression," Risks, MDPI, vol. 3(4), pages 1-17, November.
    19. Zura Kakushadze & Willie Yu, 2017. "Mutation Clusters from Cancer Exome," Papers 1707.08504, arXiv.org.
    20. Zura Kakushadze & Willie Yu, 2017. "Open Source Fundamental Industry Classification," Papers 1706.04210, arXiv.org, revised Dec 2017.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:assmgt:v:19:y:2018:i:1:d:10.1057_s41260-017-0059-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.