ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918083288891392 |
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| author | Chudziak, Jarosław A. Wawer, Michał |
| author_facet | Chudziak, Jarosław A. Wawer, Michał |
| contents | This paper presents ElliottAgents, a multi-agent system leveraging natural language processing (NLP) and large language models (LLMs) to analyze complex stock market data. The system combines AI-driven analysis with the Elliott Wave Principle to generate human-comprehensible predictions and explanations. A key feature is the natural language dialogue between agents, enabling collaborative analysis refinement. The LLM-enhanced architecture facilitates advanced language understanding, reasoning, and autonomous decision-making. Experiments demonstrate the system's effectiveness in pattern recognition and generating natural language descriptions of market trends. ElliottAgents contributes to NLP applications in specialized domains, showcasing how AI-driven dialogue systems can enhance collaborative analysis in data-intensive fields. This research bridges the gap between complex financial data and human understanding, addressing the need for interpretable and adaptive prediction systems in finance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03435 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction Chudziak, Jarosław A. Wawer, Michał Computational Engineering, Finance, and Science 91G68 I.2.6; J.4 This paper presents ElliottAgents, a multi-agent system leveraging natural language processing (NLP) and large language models (LLMs) to analyze complex stock market data. The system combines AI-driven analysis with the Elliott Wave Principle to generate human-comprehensible predictions and explanations. A key feature is the natural language dialogue between agents, enabling collaborative analysis refinement. The LLM-enhanced architecture facilitates advanced language understanding, reasoning, and autonomous decision-making. Experiments demonstrate the system's effectiveness in pattern recognition and generating natural language descriptions of market trends. ElliottAgents contributes to NLP applications in specialized domains, showcasing how AI-driven dialogue systems can enhance collaborative analysis in data-intensive fields. This research bridges the gap between complex financial data and human understanding, addressing the need for interpretable and adaptive prediction systems in finance. |
| title | ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction |
| topic | Computational Engineering, Finance, and Science 91G68 I.2.6; J.4 |
| url | https://arxiv.org/abs/2507.03435 |