AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908429398835200 |
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| author | Fons, Elizabeth Kochkina, Elena Kaur, Rachneet Zeng, Zhen Hlavaty, Berowne Smiley, Charese Vyetrenko, Svitlana Veloso, Manuela |
| author_facet | Fons, Elizabeth Kochkina, Elena Kaur, Rachneet Zeng, Zhen Hlavaty, Berowne Smiley, Charese Vyetrenko, Svitlana Veloso, Manuela |
| contents | This paper explores the potential of large language models (LLMs) to generate financial reports from time series data. We propose a framework encompassing prompt engineering, model selection, and evaluation. We introduce an automated highlighting system to categorize information within the generated reports, differentiating between insights derived directly from time series data, stemming from financial reasoning, and those reliant on external knowledge. This approach aids in evaluating the factual grounding and reasoning capabilities of the models. Our experiments, utilizing both data from the real stock market indices and synthetic time series, demonstrate the capability of LLMs to produce coherent and informative financial reports. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00718 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Fons, Elizabeth Kochkina, Elena Kaur, Rachneet Zeng, Zhen Hlavaty, Berowne Smiley, Charese Vyetrenko, Svitlana Veloso, Manuela Computation and Language This paper explores the potential of large language models (LLMs) to generate financial reports from time series data. We propose a framework encompassing prompt engineering, model selection, and evaluation. We introduce an automated highlighting system to categorize information within the generated reports, differentiating between insights derived directly from time series data, stemming from financial reasoning, and those reliant on external knowledge. This approach aids in evaluating the factual grounding and reasoning capabilities of the models. Our experiments, utilizing both data from the real stock market indices and synthetic time series, demonstrate the capability of LLMs to produce coherent and informative financial reports. |
| title | AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.00718 |