IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v19y2019i7p1121-1133.html
   My bibliography  Save this article

Enhancing the momentum strategy through deep regression

Author

Listed:
  • Saejoon Kim

Abstract

Momentum is a pervasive and persistent phenomenon in financial economics that has been found to generate abnormal returns not explainable by the traditional asset pricing models. This paper investigates some variations of the existing momentum strategies to increase profit and gain other desirable properties such as low kurtosis, small negative skewness and small maximum drawdown. We investigate these by using regression that is based on the latest techniques from deep learning such as stacked autoencoders and denoising autoencoders. Empirical results indicate that our regression-based variations can generate increased returns, and improved higher-order moments and maximum drawdown characteristics. Furthermore, our results reveal such improved performance can only be attained through the use of the latest deep learning technologies.

Suggested Citation

  • Saejoon Kim, 2019. "Enhancing the momentum strategy through deep regression," Quantitative Finance, Taylor & Francis Journals, vol. 19(7), pages 1121-1133, July.
  • Handle: RePEc:taf:quantf:v:19:y:2019:i:7:p:1121-1133
    DOI: 10.1080/14697688.2018.1563707
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2018.1563707
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/14697688.2018.1563707?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. Tom Liu & Stephen Roberts & Stefan Zohren, 2023. "Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies," Papers 2307.05522, arXiv.org.
    2. Li, Bo & Liu, Zhenya & Teka, Hanen & Wang, Shixuan, 2023. "The evolvement of momentum effects in China: Evidence from functional data analysis," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Kieran Wood & Stephen Roberts & Stefan Zohren, 2021. "Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection," Papers 2105.13727, arXiv.org, revised Dec 2021.
    4. Bryan Lim & Stefan Zohren & Stephen Roberts, 2019. "Enhancing Time Series Momentum Strategies Using Deep Neural Networks," Papers 1904.04912, arXiv.org, revised Sep 2020.
    5. Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2021. "Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention," Papers 2105.10019, arXiv.org, revised Jan 2022.
    6. Gabriel Borrageiro, 2022. "Sequential asset ranking in nonstationary time series," Papers 2202.12186, arXiv.org, revised Oct 2022.
    7. Nozomu Kobayashi & Yoshiyuki Suimon & Koichi Miyamoto & Kosuke Mitarai, 2023. "The cross-sectional stock return predictions via quantum neural network and tensor network," Papers 2304.12501, arXiv.org, revised Feb 2024.
    8. Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2020. "Building Cross-Sectional Systematic Strategies By Learning to Rank," Papers 2012.07149, arXiv.org.

    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:7:p:1121-1133. 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.