Author(s)

Jianhua Dong

Corresponding Author

Jianhua Dong

Abstract

This document examines the use of Artificial Intelligence, specifically Generative Adversarial Networks (GANs), in stock market forecasting, highlighting its impact, advantages, and challenges. It addresses concerns about data quality, biases, and overfitting in GAN models. The structured approach includes a literature review, method explanations, and experimental analysis, offering insights into the implications of AI in stock market forecasting. We would provide a critical analysis of the literature, identifying gaps in current research and suggesting future directions for the application of GANs in financial markets. It would conclude with reflections on the broader implications of integrating advanced AI technologies in stock market forecasting, considering both the potential benefits and the limitations. This comprehensive approach would provide a nuanced understanding of the subject, offering insights into the current state and future prospects of AI in financial forecasting.

Keywords

GAN (Generative Adversarial Networks), Stock Forecasting, Artificial Intelligence