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Automatic Financial Feature Construction

Author

Listed:
  • Jie Fang
  • Shutao Xia
  • Jianwu Lin
  • Yong Jiang

Abstract

In automatic financial feature construction task, the state-of-the-art technic leverages reverse polish expression to represent the features, then use genetic programming (GP) to conduct its evolution process. In this paper, we propose a new framework based on neural network, alpha discovery neural network (ADNN). In this work, we made several contributions. Firstly, in this task, we make full use of neural network overwhelming advantage in feature extraction to construct highly informative features. Secondly, we use domain knowledge to design the object function, batch size, and sampling rules. Thirdly, we use pre-training to replace the GP evolution process. According to neural network universal approximation theorem, pre-training can conduct a more effective and explainable evolution process. Experiment shows that ADNN can remarkably produce more diversified and higher informative features than GP. Besides, ADNN can serve as a data augmentation algorithm. It further improves the the performance of financial features constructed by GP.

Suggested Citation

  • Jie Fang & Shutao Xia & Jianwu Lin & Yong Jiang, 2019. "Automatic Financial Feature Construction," Papers 1912.06236, arXiv.org, revised Oct 2020.
    • Handle: RePEc:arx:papers:1912.06236
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    File URL: http://arxiv.org/pdf/1912.06236
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    References listed on IDEAS

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    1. Zura Kakushadze, 2016. "101 Formulaic Alphas," Papers 1601.00991, arXiv.org, revised Mar 2016.
    2. Changqing Cheng & Akkarapol Sa-Ngasoongsong & Omer Beyca & Trung Le & Hui Yang & Zhenyu (James) Kong & Satish T.S. Bukkapatnam, 2015. "Time series forecasting for nonlinear and non-stationary processes: a review and comparative study," IISE Transactions, Taylor & Francis Journals, vol. 47(10), pages 1053-1071, October.
    3. Gan, Lirong & Wang, Huamao & Yang, Zhaojun, 2020. "Machine learning solutions to challenges in finance: An application to the pricing of financial products," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
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    Cited by:

    1. Gürdal Ertek & Lakshmi Kailas, 2021. "Analyzing a Decade of Wind Turbine Accident News with Topic Modeling," Sustainability, MDPI, vol. 13(22), pages 1-34, November.
    2. Jie Fang & Shutao Xia & Jianwu Lin & Zhikang Xia & Xiang Liu & Yong Jiang, 2019. "Alpha Discovery Neural Network based on Prior Knowledge," Papers 1912.11761, arXiv.org, revised Nov 2020.

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