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.

翻译：ChatGPT 在各种自然语言处理任务中展现了卓越的能力。然而，其从时序文本数据（尤其是金融新闻）中推断动态网络结构的潜力，仍是一个尚未探索的前沿领域。在本研究中，我们提出了一种新型框架，利用ChatGPT的图推理能力增强图神经网络。该框架能够从文本数据中灵活提取不断演化的网络结构，并将其整合到图神经网络中，用于后续预测任务。股票运动预测的实验结果表明，我们的模型持续优于基于深度学习的最先进基准模型。此外，基于模型输出构建的投资组合展现出更高的年化累计收益，同时降低了波动率和最大回撤。这一优越性能凸显了ChatGPT在基于文本的网络推理中的潜力，并强调了其在金融领域的应用前景。