Comparing LLMs for Sentiment Analysis in Financial Market News
Abstract
This article presents a comparative study of large language models (LLMs) in the task of sentiment analysis of financial market news. This work aims to analyze the performance difference of these models in this important natural language processing task within the context of finance. LLM models are compared with classical approaches, allowing for the quantification of the benefits of each tested model or approach. Results show that large language models outperform classical models in the vast majority of cases.
Links & Resources
Authors
Cite This Paper
Teles, L. E. P., Figueiredo, C. M. S. (2025). Comparing LLMs for Sentiment Analysis in Financial Market News. arXiv preprint arXiv:2510.15929.
Lucas Eduardo Pereira Teles and Carlos M. S. Figueiredo. "Comparing LLMs for Sentiment Analysis in Financial Market News." arXiv preprint arXiv:2510.15929 (2025).