  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.
