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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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    1. Bate He & Eisuke Kita, 2021. "The Application of Sequential Generative Adversarial Networks for Stock Price Prediction," The Review of Socionetwork Strategies, Springer, vol. 15(2), pages 455-470, November.
    2. Jun Zhang & Lan Li & Wei Chen, 2021. "Predicting Stock Price Using Two-Stage Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1237-1261, April.
    3. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    4. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    5. Xingyu Zhou & Zhisong Pan & Guyu Hu & Siqi Tang & Cheng Zhao, 2018. "Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, April.
    6. Ang, Andrew & Chen, Joseph, 2007. "CAPM over the long run: 1926-2001," Journal of Empirical Finance, Elsevier, vol. 14(1), pages 1-40, January.
    7. Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022. "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
    8. 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.
    9. Milena Vuletić & Felix Prenzel & Mihai Cucuringu, 2024. "Fin-GAN: forecasting and classifying financial time series via generative adversarial networks," Quantitative Finance, Taylor & Francis Journals, vol. 24(2), pages 175-199, January.
    10. Kot, Hung Wan & Tam, Lewis H.K., 2016. "Are stock price more informative after dual-listing in emerging markets? Evidence from Hong Kong-listed Chinese companies," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 31-45.
    11. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    12. Wen, Fenghua & Xu, Longhao & Ouyang, Guangda & Kou, Gang, 2019. "Retail investor attention and stock price crash risk: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 65(C).
    13. Jiawei Wang & Zhen Chen, 2023. "Exploring Low-Risk Anomalies: A Dynamic CAPM Utilizing a Machine Learning Approach," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
    14. Stephen R. Foerster & G. Andrew Karolyi, 1999. "The Effects of Market Segmentation and Investor Recognition on Asset Prices: Evidence from Foreign Stocks Listing in the United States," Journal of Finance, American Finance Association, vol. 54(3), pages 981-1013, June.
    15. Ma, Tian & Leong, Wen Jun & Jiang, Fuwei, 2023. "A latent factor model for the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 87(C).
    16. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
    17. Shun Chen & Lei Ge, 2019. "Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1507-1515, September.
    18. Wei, Zuobao & Varela, Oscar & Kabir Hassan, M., 2002. "Ownership and performance in Chinese manufacturing industry1," Journal of Multinational Financial Management, Elsevier, vol. 12(1), pages 61-78, February.
    19. Bali, Turan G. & Brown, Stephen J. & Murray, Scott & Tang, Yi, 2017. "A Lottery-Demand-Based Explanation of the Beta Anomaly," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(6), pages 2369-2397, December.
    20. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
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    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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