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Enhancing the momentum strategy through deep regression

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  • 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
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    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.

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