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

arXiv: https://arxiv.org/abs/2404.00012 · HTML: https://mlsanity.com/paper/2404.00012/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://mlsanity.com/paper/2304.07619/) (2023)
- [Accuracy and Political Bias of News Source Credibility Ratings by Large Language Models](https://mlsanity.com/paper/2304.00228/) (2023)
- [Practical Text Classification With Large Pre-Trained Language Models](https://mlsanity.com/paper/1812.01207/) (2018)
- [Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps?](https://mlsanity.com/paper/2401.05447/) (2024)

## Built on by (most cited first, 2 of 2)

- [Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning](https://mlsanity.com/paper/2409.11408/) (2024)
- [FinMarBa: A Market-Informed Dataset for Financial Sentiment Classification](https://mlsanity.com/paper/2507.22932/) (2025)

## Abstract (arXiv)

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.