---
title: News Sentiment and Investment Risk
url: https://www.ml-quant.com/papers/repec/eee-ecolet-v-247-y-2025-i-c-s0165176524006086/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:ecolet:v:247:y:2025:i:c:s0165176524006086
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0165176524006086%3Bh%3Drepec%3Aeee%3Aecolet%3Av%3A247%3Ay%3A2025%3Ai%3Ac%3As0165176524006086
featured: 2025-03-05
citations: unknown
topic: LLMs & Text
---


# News Sentiment and Investment Risk

The research reassesses the effect of news sentiment on stock return volatility, finding that both positive and negative firm-specific and macroeconomic news significantly impact intraday stock return volatility, with GPT-4 potentially outperforming RavenPack in classification accuracy.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0165176524006086%3Bh%3Drepec%3Aeee%3Aecolet%3Av%3A247%3Ay%3A2025%3Ai%3Ac%3As0165176524006086
- Identifier: RePEc:eee:ecolet:v:247:y:2025:i:c:s0165176524006086
- Released: 2025-03-05
- First featured: Quant Letter No. 87 (2025-03-05): https://www.ml-quant.com/issues/2025-03-05/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: LLMs & Text

## Related

- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://www.ml-quant.com/papers/ssrn/4412788/): ChatGPT predicts stock market returns using sentiment analysis, outperforming traditional methods.
- [Sentiment trading with large language models](https://www.ml-quant.com/papers/doi/10-1016-j-frl-2024-105227/): The OPT model, a large language model, has proven superior in predicting stock market returns using sentiment analysis of U.S. financial news, outdoing traditional methods like the Loughran-McDonald dictionary model.
- [Designing Heterogeneous LLM Agents for Financial Sentiment Analysis](https://www.ml-quant.com/papers/arxiv/2401.05799/): A study suggests using large language models without fine-tuning for financial sentiment analysis, offering a design framework that enhances accuracy.
- [Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models](https://www.ml-quant.com/papers/arxiv/2306.12659/): A new approach improves financial sentiment analysis by addressing limitations of language models.
- [Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?](https://www.ml-quant.com/papers/arxiv/2503.04873/): The article investigates the use of large language models in financial sentiment analysis, showing their ability to learn and address related challenges.
- [An End-To-End LLM Enhanced Trading System](https://www.ml-quant.com/papers/arxiv/2502.01574/): The project presents a trading system that uses Large Language Models to analyze market sentiment in real-time, using data from financial news and social media to create trading signals.