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

• Published in: PloS one Vol. 19; no. 6; p. e0306094
• Main Authors: Wang, Jiawei, Chen, Zhen
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.06.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial Intelligence - economics; Capital assets; China; Commerce - economics; Computational linguistics; Data processing; Datasets; Deep Learning; Explainable artificial intelligence; Forecasts and trends; Foreign exchange markets; Generative adversarial networks; Government business enterprises; Humans; Indicators; Investment analysis; Investment policy; Investment strategy; Investments; Investments - economics; Language processing; Liquidity; Machine learning; Macroeconomics; Models, Economic; Natural language interfaces; Neural networks; Neural Networks, Computer; Prices and rates; Pricing; Rates of return; Securities markets; Stock exchanges; Stock markets; Stock prices; Stocks; Voice recognition; Volatility; China
• DOI: 10.1371/journal.pone.0306094
• ISSN: 1932-6203

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.