Integrating Generative AI into Financial Market Prediction for Improved Decision Making
Authors: Chang Che, Zengyi Huang, Chen Li, Haotian Zheng, Xinyu Tian
Abstract: This study provides an in-depth analysis of the model architecture and key technologies of generative artificial intelligence, combined with specific application cases, and uses conditional generative adversarial networks ( cGAN ) and time series analysis methods to simulate and predict dynamic changes in financial markets. The research results show that the cGAN model can effectively capture the complexity of financial market data, and the deviation between the prediction results and the actual market performance is minimal, showing a high degree of accuracy.
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