A Generative Adversarial Network-Based Investor Sentiment Indicator: Superior Predictability for the Stock Market

Shiqing Qiu, Yang Wang, Zong Ke (), Qinyan Shen, Zichao Li, Rong Zhang and Kaichen Ouyang ()
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Shiqing Qiu: School of Mathematical Sciences, Chengdu University of Technology, Chengdu 610059, China
Yang Wang: Department of Mathematics, University of Chicago, Chicago, IL 60637, USA
Zong Ke: Faculty of Science, National University of Singapore, Singapore 119077, Singapore
Qinyan Shen: Department of Statistics, University of South Carolina, Columbia, SC 29201, USA
Zichao Li: The Department of Management Science and Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Rong Zhang: Graduate School of Management, University of California, Davis, CA 95616, USA
Kaichen Ouyang: Department of Mathematics, University of Science and Technology of China, Hefei 230026, China

Mathematics, 2025, vol. 13, issue 9, 1-31

Abstract: Investor sentiment has a profound impact on financial market volatility; however, it is difficult to accurately capture the complex nonlinear relationships among sentiment proxies with the existing methods. In this study, we propose a novel investor sentiment indicator, S GAN , which uses generative adversarial networks (GANs) to extract the nonlinear latent structure from eight sentiment proxies from February 2003 to September 2023 in the Chinese A-share market. Unlike traditional linear dimensionality reduction methods, GANs are able to capture complex market dynamics through adversarial training, effectively reducing noise and improving prediction accuracy. The empirical analyses show that S GAN significantly outperforms existing methods in both in-sample and out-of-sample prediction capabilities. The GAN-based investment strategy achieves impressive annualized returns and provides a powerful tool for portfolio construction and risk management. Robustness tests across economic cycles, industries, and U.S. markets further validate the stability of S GAN . These findings highlight the unique advantages of GANs as sentiment-driven financial forecasting tools, providing market participants with new ways to more accurately capture sentiment-shifting trends and develop effective investment strategies.

Keywords: investor sentiment; generative adversarial networks (GANs); dimensionality reduction; return predictability; financial mathematics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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