ChatGPT Informed Graph Neural Network for Stock Movement Prediction
This work addresses stock forecasting for the financial sector, representing an incremental improvement by combining existing tools in a novel way.
The paper tackles stock movement prediction by using ChatGPT to infer dynamic network structures from financial news and integrating them into Graph Neural Networks, resulting in consistently outperforming state-of-the-art benchmarks and achieving higher annualized cumulative returns with reduced volatility and maximum drawdown.
ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN). Our framework adeptly extracts evolving network structures from textual data, and incorporates these networks into graph neural networks for subsequent predictive tasks. The experimental results from stock movement forecasting indicate our model has consistently outperformed the state-of-the-art Deep Learning-based benchmarks. Furthermore, the portfolios constructed based on our model's outputs demonstrate higher annualized cumulative returns, alongside reduced volatility and maximum drawdown. This superior performance highlights the potential of ChatGPT for text-based network inferences and underscores its promising implications for the financial sector.