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Neural Network-based Automatic Factor Construction

Author

Listed:
  • Jie Fang
  • Jianwu Lin
  • Shutao Xia
  • Yong Jiang
  • Zhikang Xia
  • Xiang Liu

Abstract

Instead of conducting manual factor construction based on traditional and behavioural finance analysis, academic researchers and quantitative investment managers have leveraged Genetic Programming (GP) as an automatic feature construction tool in recent years, which builds reverse polish mathematical expressions from trading data into new factors. However, with the development of deep learning, more powerful feature extraction tools are available. This paper proposes Neural Network-based Automatic Factor Construction (NNAFC), a tailored neural network framework that can automatically construct diversified financial factors based on financial domain knowledge and a variety of neural network structures. The experiment results show that NNAFC can construct more informative and diversified factors than GP, to effectively enrich the current factor pool. For the current market, both fully connected and recurrent neural network structures are better at extracting information from financial time series than convolution neural network structures. Moreover, new factors constructed by NNAFC can always improve the return, Sharpe ratio, and the max draw-down of a multi-factor quantitative investment strategy due to their introducing more information and diversification to the existing factor pool.

Suggested Citation

  • Jie Fang & Jianwu Lin & Shutao Xia & Yong Jiang & Zhikang Xia & Xiang Liu, 2020. "Neural Network-based Automatic Factor Construction," Papers 2008.06225, arXiv.org, revised Oct 2020.
    • Handle: RePEc:arx:papers:2008.06225
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    References listed on IDEAS

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    1. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    2. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    3. Zura Kakushadze, 2016. "101 Formulaic Alphas," Papers 1601.00991, arXiv.org, revised Mar 2016.
    4. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    5. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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    2. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    3. Xin Zhang & Lan Wu & Zhixue Chen, 2021. "Constructing long-short stock portfolio with a new listwise learn-to-rank algorithm," Papers 2104.12484, arXiv.org.

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