ChatGPT Informed Graph Neural Network for Stock Movement Prediction

Abstract

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.

Dataset is available at [https://github.com/ZihanChen1995/ChatGPT-GNN-StockPredict]. Accepted for the oral presentation at SIGKDD 2023 Workshop on Robust NLP for Finance

References in corpus (1)

• Training language models to follow instructions with human feedback

Cited by in corpus (4)

• A Scoping Review of ChatGPT Research in Accounting and Finance
• MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU
• Integrating Graphs with Large Language Models: Methods and Prospects
• Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection