ChatGPT Informed Graph Neural Network for Stock Movement Prediction

ArXiv ID: 2306.03763 “View on arXiv”

Authors: Unknown

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.

Keywords: Large Language Models (LLM), Graph Neural Networks (GNN), Network Inference, Stock Movement Forecasting, Portfolio Construction

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper leverages advanced deep learning architectures (GNNs, LSTMs) and NLP techniques, but the mathematics is primarily application-oriented rather than heavily theoretical, lacking dense derivations or new theoretical proofs. Empirical rigor is high due to the use of a real-world dataset (DOW 30), detailed backtesting with portfolio construction, and reporting of specific metrics (F1 scores, annualized returns, volatility, drawdown).

  flowchart TD
    A["Research Goal<br/>& Question"] --> B["Data & Inputs<br/>Temporal Financial News"]
    B --> C["Methodology Steps<br/>Extract Evolving Network Structures using ChatGPT"]
    C --> D["Computational Processes<br/>Integrate Networks into GNN Model"]
    D --> E["Key Findings & Outcomes<br/>Superior Stock Movement Prediction"]
    E --> F["Portfolio Construction<br/>Higher Returns & Reduced Risk"]

    subgraph Key_Research_Goal
        A
    end
    subgraph Methodology
        C
    end
    subgraph Computation
        D
    end
    subgraph Outcomes
        E
        F
    end