---
title: Sentiment Analysis is Virtually Useless in Financial Forecasting
url: https://www.ml-quant.com/papers/ssrn/4545418/
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 4545418
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4545418
featured: 2023-08-24
citations: 0
topic: LLMs & Text
---


# Sentiment Analysis is Virtually Useless in Financial Forecasting

The paper criticizes the overemphasis on sentiment analysis in financial forecasting, arguing that its practical use is often overstated and misleading, and calls for more methodological rigor and transparency in its application.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4545418
- Identifier: SSRN 4545418
- 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.