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Comparing LLMs for Sentiment Analysis in Financial Market News

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  • Lucas Eduardo Pereira Teles
  • Carlos M. S. Figueiredo

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.

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  • Lucas Eduardo Pereira Teles & Carlos M. S. Figueiredo, 2025. "Comparing LLMs for Sentiment Analysis in Financial Market News," Papers 2510.15929, arXiv.org.
    • Handle: RePEc:arx:papers:2510.15929
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    1. Pekka Malo & Ankur Sinha & Pekka Korhonen & Jyrki Wallenius & Pyry Takala, 2014. "Good debt or bad debt: Detecting semantic orientations in economic texts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 782-796, April.
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