ChatGPT Informed Graph Neural Network for Stock Movement Prediction

Zihan Chen, Lei Nico Zheng, Cheng Lu, Jialu Yuan and Di Zhu

Papers from arXiv.org

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.

Date: 2023-05, Revised 2023-09
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-fmk
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Citations: View citations in EconPapers (8)

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