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Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks

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  • Jiawei Wang
  • Zhen Chen

Abstract

Deep learning, a pivotal branch of artificial intelligence, has increasingly influenced the financial domain with its advanced data processing capabilities. This paper introduces Factor-GAN, an innovative framework that utilizes Generative Adversarial Networks (GAN) technology for factor investing. Leveraging a comprehensive factor database comprising 70 firm characteristics, Factor-GAN integrates deep learning techniques with the multi-factor pricing model, thereby elevating the precision and stability of investment strategies. To explain the economic mechanisms underlying deep learning, we conduct a subsample analysis of the Chinese stock market. The findings reveal that the deep learning-based pricing model significantly enhances return prediction accuracy and factor investment performance in comparison to linear models. Particularly noteworthy is the superior performance of the long-short portfolio under Factor-GAN, demonstrating an annualized return of 23.52% with a Sharpe ratio of 1.29. During the transition from state-owned enterprises (SOEs) to non-SOEs, our study discerns shifts in factor importance, with liquidity and volatility gaining significance while fundamental indicators diminish. Additionally, A-share listed companies display a heightened emphasis on momentum and growth indicators relative to their dual-listed counterparts. This research holds profound implications for the expansion of explainable artificial intelligence research and the exploration of financial technology applications.

Suggested Citation

  • Jiawei Wang & Zhen Chen, 2024. "Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-26, June.
  • Handle: RePEc:plo:pone00:0306094
    DOI: 10.1371/journal.pone.0306094
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    References listed on IDEAS

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    Cited by:

    1. Giovanni Ballarin & Jacopo Capra & Petros Dellaportas, 2025. "Multi-Horizon Echo State Network Prediction of Intraday Stock Returns," Papers 2504.19623, arXiv.org.

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