Paper: arXiv 2404.00012

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.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.5: no out-of-sample test; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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"]

Cited References (53)

  1. Giving Content to Investor Sentiment: The Role of Media in the Stock Market (2007) — link
  2. Textual analysis of stock market prediction using breaking financial news: The AZFin text system (2009)
  3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018) — link
  4. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models (2019) — link
  5. GPT-4 Technical Report (2023) — link
  6. A review of ChatGPT AI’s impact on several business sectors (2023)
  7. S enti W ord N et 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining (2010) — link
  8. FinEntity: Entity-level Sentiment Classification for Financial Texts (2023) — link
and 45 more
  1. Practical Text Classification With Large Pre-Trained Language Models (2018) — link
  2. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks (2011)
  3. BloombergGPT: A Large Language Model for Finance (2023) — link
  4. The Impact of Macroeconomic News Sentiment on Interest Rates (2022) — link
  5. Trading strategies to exploit blog and news sentiment (2010)
  6. DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks (2023)
  7. Stock Market Prediction Based on Financial News Text Mining and Investor Sentiment Recognition (2022) — link
  8. Googling investor’s sentiment, financial stress and dynamics of European market indexes: a Markov chain analysis (2020) — link
  9. Deep learning for sentiment analysis: A survey (2018)
  10. The response of different investor types to macroeconomic news (2019)
  11. The evolution of Fintech: A new post-crisis paradigm (2015)
  12. Can ChatGPT Decipher Fedspeak? (2023) — link
  13. How to Learn and Teach Economics with Large Language Models, Including GPT (2023) — link
  14. Language Models and Cognitive Automation for Economic Research (2023) — link
  15. Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models (2023) — link
  16. Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence (2023) — link
  17. Ceo behavior and firm performance (2020) — link
  18. Large language models can rate news outlet credibility (2023) — link
  19. Sentiment Analysis Applied to News from the Brazilian Stock Market (2022) — link
  20. A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment (2023) — link
  21. Predicting Price Trends Using Sentiment Analysis: A Study of StepN’s SocialFi and GameFi Cryptocurrencies (2023) — link
  22. Investment Strategy via Lead Lag Effect using Economic Causal Chain and SSESTM Model (2022) — link
  23. Investment strategy via analyst report text mining (2023) — link
  24. Risk-On / Risk-Off: Financial market response to investor fear (2015)
  25. Time-varying relationship of news sentiment, implied volatility and stock returns (2016) — link
  26. Application of Machine Learning With News Sentiment in Stock Trading Strategies (2023) — link
  27. Equilibrium Data Mining and Data Abundance (2020) — link
  28. Textual sentiment in finance: A survey of methods and models (2014)
  29. Negative news and investor trust: The role of $ Firm and # CEO Twitter use (2018)
  30. Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps (2024) — link
  31. Sentiment Analysis in Financial News: A Review (2016)
  32. Social Media and News Sentiment Analysis for Advanced Investment Strategies (2023)
  33. Sentiment Analysis in Financial Texts (2023)
  34. A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment (2023)
  35. Sentiment Correlation in Financial News Networks and Associated Market Movements (2023)
  36. A new financial stress indicator: properties and conditional asset price behavior (2014)
  37. Natural language based financial forecasting: a survey (2018)
  38. Mutual Fund Performance (1966)
  39. Adjusting for Risk in Portfolio Performance Measurement (1975)
  40. Asset allocation: Management Style and Performance Measurement (1992)
  41. The Sharpe ratio (1994)
  42. Calmar ratio: A smoother tool (1991)
  43. Measuring economic policy uncertainty (2016)
  44. In search of attention (2011)
  45. All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors (2008)

0 of 53 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.