---
title: News Sentiment Analysis Limitations
url: https://www.ml-quant.com/papers/ssrn/5086825/
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 5086825
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5086825
featured: 2025-01-23
citations: unknown
topic: LLMs & Text
---


# News Sentiment Analysis Limitations

The paper explores the difficulties of using news sentiment analysis to predict next-day stock returns, proposing a multilevel approach that incorporates sentiment analysis across individual stocks, industries, and the overall economy.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5086825
- Identifier: SSRN 5086825
- Released: 2024-11-12
- First featured: Quant Letter No. 83 (2025-01-23): https://www.ml-quant.com/issues/2025-01-23/
- 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.
- [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.
- [A Systematic Literature Review on Sentimental Analysis using Machine Learning for Preprocessing, Feature Extraction and Classification](https://www.ml-quant.com/papers/ssrn/5061012/): The study reviews sentiment analysis methods in Natural Language Processing, covering data collection, preprocessing, feature extraction, and classification.