Integrating Large Language Models in Financial Investments and Market Analysis: A Survey
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909674147676160 |
|---|---|
| author | Mahdavi, Sedigheh Jiating Chen Joshi, Pradeep Kumar Guativa, Lina Huertas Singh, Upmanyu |
| author_facet | Mahdavi, Sedigheh Jiating Chen Joshi, Pradeep Kumar Guativa, Lina Huertas Singh, Upmanyu |
| contents | Large Language Models (LLMs) have been employed in financial decision making, enhancing analytical capabilities for investment strategies. Traditional investment strategies often utilize quantitative models, fundamental analysis, and technical indicators. However, LLMs have introduced new capabilities to process and analyze large volumes of structured and unstructured data, extract meaningful insights, and enhance decision-making in real-time. This survey provides a structured overview of recent research on LLMs within the financial domain, categorizing research contributions into four main frameworks: LLM-based Frameworks and Pipelines, Hybrid Integration Methods, Fine-Tuning and Adaptation Approaches, and Agent-Based Architectures. This study provides a structured review of recent LLMs research on applications in stock selection, risk assessment, sentiment analysis, trading, and financial forecasting. By reviewing the existing literature, this study highlights the capabilities, challenges, and potential directions of LLMs in financial markets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01990 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Large Language Models in Financial Investments and Market Analysis: A Survey Mahdavi, Sedigheh Jiating Chen Joshi, Pradeep Kumar Guativa, Lina Huertas Singh, Upmanyu General Finance Artificial Intelligence Machine Learning Large Language Models (LLMs) have been employed in financial decision making, enhancing analytical capabilities for investment strategies. Traditional investment strategies often utilize quantitative models, fundamental analysis, and technical indicators. However, LLMs have introduced new capabilities to process and analyze large volumes of structured and unstructured data, extract meaningful insights, and enhance decision-making in real-time. This survey provides a structured overview of recent research on LLMs within the financial domain, categorizing research contributions into four main frameworks: LLM-based Frameworks and Pipelines, Hybrid Integration Methods, Fine-Tuning and Adaptation Approaches, and Agent-Based Architectures. This study provides a structured review of recent LLMs research on applications in stock selection, risk assessment, sentiment analysis, trading, and financial forecasting. By reviewing the existing literature, this study highlights the capabilities, challenges, and potential directions of LLMs in financial markets. |
| title | Integrating Large Language Models in Financial Investments and Market Analysis: A Survey |
| topic | General Finance Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.01990 |