Comparing LLMs for Sentiment Analysis in Financial Market News
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909854071783424 |
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| author | Teles, Lucas Eduardo Pereira Figueiredo, Carlos M. S. |
| author_facet | Teles, Lucas Eduardo Pereira Figueiredo, Carlos M. S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparing LLMs for Sentiment Analysis in Financial Market News Teles, Lucas Eduardo Pereira Figueiredo, Carlos M. S. Statistical Finance Artificial Intelligence Computation and Language 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. |
| title | Comparing LLMs for Sentiment Analysis in Financial Market News |
| topic | Statistical Finance Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2510.15929 |