IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v19y2019i9p1555-1565.html
   My bibliography  Save this article

Detection of false investment strategies using unsupervised learning methods

Author

Listed:
  • Marcos López de Prado
  • Michael J. Lewis

Abstract

In this paper we address the problem of selection bias under multiple testing in the context of investment strategies. We introduce an unsupervised learning algorithm that determines the number of effectively uncorrelated trials carried out in the context of a discovery. This estimate is critical for computing the familywise false positive probability, and for filtering out false investment strategies.

Suggested Citation

  • Marcos López de Prado & Michael J. Lewis, 2019. "Detection of false investment strategies using unsupervised learning methods," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1555-1565, September.
    • Handle: RePEc:taf:quantf:v:19:y:2019:i:9:p:1555-1565
    DOI: 10.1080/14697688.2019.1622311
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2019.1622311
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14697688.2019.1622311?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrew Paskaramoorthy & Terence van Zyl & Tim Gebbie, 2020. "A Framework for Online Investment Algorithms," Papers 2003.13360, arXiv.org.
    2. Gang Huang & Xiaohua Zhou & Qingyang Song, 2020. "Deep reinforcement learning for portfolio management," Papers 2012.13773, arXiv.org, revised Apr 2022.
    3. Joel da Costa & Tim Gebbie, 2020. "Learning low-frequency temporal patterns for quantitative trading," Papers 2008.09481, arXiv.org.
    4. Jiří Witzany, 2021. "A Bayesian Approach to Measurement of Backtest Overfitting," Risks, MDPI, vol. 9(1), pages 1-22, January.
    5. Benjamin R. Auer, 2022. "On false discoveries of standard t-tests in investment management applications," Review of Managerial Science, Springer, vol. 16(3), pages 751-768, April.

    More about this item

    Statistics

    Access and download statistics

    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:taf:quantf:v:19:y:2019:i:9:p:1555-1565. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    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.