Leveraging Generative Artificial Intelligence for Financial Market Trading Data Management and Prediction

Authors

  • Xinzhu Bai Tourism Management, Tianjin University of Finance and Economics, Tianjin, China
  • Shikai Zhuang Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
  • Hangyu Xie Statistics, Rice University, Houston, TX, USA
  • Lingfeng Guo Business Analytics, Trine University, AZ, USA

Keywords:

Generative artificial intelligence, Financial markets, Data management, Forecasting

Abstract

The paper explores using generative artificial intelligence (AI) in financial market data management and forecasting. By integrating multiple data sources and feature extraction techniques, such as fundamental analysis, technical indicators, global economic data, and sentiment analysis, generative AI constructs a comprehensive deep learning framework that significantly enhances financial data management efficiency and market forecasts' accuracy. Specifically, technologies like generative adversarial networks (Gans) and variational autoencoders (VAE) demonstrate substantial data augmentation and model optimisation potential. The application value of the model in real-time market prediction and trading strategy optimization is further amplified through reinforcement learning methods.

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Published

2024-06-30

How to Cite

Bai, X., Zhuang, S., Xie, H., & Guo, L. (2024). Leveraging Generative Artificial Intelligence for Financial Market Trading Data Management and Prediction. Journal of Artificial Intelligence and Information, 1, 32–41. Retrieved from https://woodyinternational.com/index.php/jaii/article/view/34