Abstract: Stock price prediction has always been a key topic of focus in both financial research and quantitative investment.To address the issues of mode collapse and weak generalization ability in traditional GAN models,and to improve stock price prediction accuracy,this study proposes a Group Cross-Adversarial model (GCA).The model includes multiple generators and multiple discriminators,and introduces a collaboration mechanism between the generators and discriminators to enhance the generalization ability of the generators.Additionally,knowledge distillation is used to further improve the prediction performance of the generators.The experiment selects daily data of 8 stocks (Industrial and Commercial Bank of China,Huaneng International,China Merchants Bank,and Qingdao Haier from the A-share market,and Alibaba,Amazon,JD,and Bank of America from the US stock market) from January 1,2015 to January 1,2025 as the research sample,and constructs a dataset of 24 feature variables,including market data and technical indicators.The results show that the proposed GCA model significantly outperforms the standalone GRU,LSTM,and Transformer models in terms of the three evaluation metrics－MAE,MAPE,and MSE.Additionally,it surpasses the GRU-GAN,LSTM-GAN,and Transformer-GAN models,which integrate GAN architectures,as well as the WGAN-GP and ResNLS models.Even without optimizing the original models with GAN,the introduction of the GCA framework still improves the prediction accuracy.Further discussion indicates that increasing the number of generator-discriminator pairs can further optimize prediction performance.

Key words: Stock price prediction,GAN,Time series prediction model,Multi-generator multi-discriminator model