Stock Market Prediction with Generative AI and GNNs

Objectives to cover:

1. Introduction
2. Literature Review
   • Stock Market Prediction Models: Overview of traditional and recent models.
   • Generative AI in Finance: Latest advances in AI-driven stock prediction.
3. Methodology
   • Data Collection & Preprocessing: Data sources and cleaning steps.
   • Model Architecture: Combining GNN with generative models.
4. Results and Analysis
   • Performance Comparison: Generative model vs. traditional models.
   • Interdependency Impact: Influence on prediction accuracy.
5. Discussion
   • Market Insights: Findings on stock relationships.
   • Model Challenges: Data noise, complexity limitations.
6. Conclusion and Future Work
   • Summary: Key findings recap.
   • Accuracy Potential: Improvements for financial forecasts.

Reference:  APA style