# Integrating Generative AI into Financial Market Prediction for Improved Decision Making

arXiv: https://arxiv.org/abs/2404.03523 · HTML: https://mlsanity.com/paper/2404.03523/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2024.

## Built on by (most cited first, 4 of 4)

- [Opportunities and Challenges of Generative-AI in Finance](https://mlsanity.com/paper/2410.15653/) (2024)
- [Adjust for Trust: Mitigating Trust-Induced Inappropriate Reliance on AI Assistance](https://mlsanity.com/paper/2502.13321/) (2025)
- [Large Language Models for Predictive Analysis: How Far Are They?](https://mlsanity.com/paper/2505.17149/) (2025)
- [Match Chat: Real Time Generative AI and Generative Computing for Tennis](https://mlsanity.com/paper/2509.12592/) (2025)

## Abstract (arXiv)

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.