Stress index strategy enhanced with financial news sentiment analysis for the equity markets

ArXiv ID: 2404.00012 “View on arXiv”

Authors: Unknown

Abstract

This paper introduces a new risk-on risk-off strategy for the stock market, which combines a financial stress indicator with a sentiment analysis done by ChatGPT reading and interpreting Bloomberg daily market summaries. Forecasts of market stress derived from volatility and credit spreads are enhanced when combined with the financial news sentiment derived from GPT-4. As a result, the strategy shows improved performance, evidenced by higher Sharpe ratio and reduced maximum drawdowns. The improved performance is consistent across the NASDAQ, the S&P 500 and the six major equity markets, indicating that the method generalises across equities markets.

Keywords: Sentiment Analysis, Large Language Models, Risk-On Risk-Off, Financial Stress Indicator, Algorithmic Trading

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper focuses on implementing and testing a practical trading strategy using existing financial indicators (stress index, volatility, credit spreads) and an NLP tool (ChatGPT for sentiment analysis), with extensive backtesting across multiple markets and reported performance metrics (Sharpe ratio, drawdowns), indicating low theoretical math but high empirical implementation.

  flowchart TD
    A["Research Goal:<br>Enhance risk-on risk-off equity strategy"] --> B["Data Collection"]
    B --> C["Financial Stress Indicator<br>Volatility + Credit Spreads"]
    B --> D["GPT-4 Sentiment Analysis<br>Bloomberg Market Summaries"]
    C --> E["Computational Model:<br>Forecast Market Stress"]
    D --> E
    E --> F["Strategy Execution<br>Risk-On vs Risk-Off Signals"]
    F --> G["Key Outcomes<br>Improved Sharpe Ratio & Reduced Max Drawdown"]
    G --> H["Generalization<br>Validated on NASDAQ, S&P 500, 6 Major Markets"]