---
title: When Does Bad News Cause Mispricing? A Historical View
url: https://www.ml-quant.com/papers/ssrn/4544851/
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: SSRN 4544851
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4544851
featured: 2023-08-24
citations: 0
topic: LLMs & Text
---


# When Does Bad News Cause Mispricing? A Historical View

The research suggests that news media sentiment can predict returns during periods of high volatility, low returns, high economic policy uncertainty, and heavily skewed returns.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4544851
- Identifier: SSRN 4544851
- Released: 2023-08-18
- First featured: Quant Letter No. 13 (2023-08-24): https://www.ml-quant.com/issues/2023-08-24/
- Citations (Semantic Scholar): 0
- 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.
- [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.
- [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.
- [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.
- [To the Moon: Analyzing Collective Trading Events on the Wings of Sentiment Analysis](https://www.ml-quant.com/papers/arxiv/2308.09968/): A study finds a strong correlation between Twitter activity and stock volatility, but a weak connection between tweet sentiment and stock performance, suggesting Reddit has a more significant impact on these events.
- [Can Machine Learning Catch Economic Recessions Using Economic and Market Sentiments?](https://www.ml-quant.com/papers/ssrn/4553506/): The paper uses machine learning to predict US economic recessions using market sentiment and economic indicators, using the ARIMA method for backcasting.
