# ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction

arXiv: https://arxiv.org/abs/2507.03435 · HTML: https://mlsanity.com/paper/2507.03435/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2025.

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- [TACLA: An LLM-Based Multi-Agent Tool for Transactional Analysis Training in Education](https://mlsanity.com/paper/2510.17913/) (2025)

## Abstract (arXiv)

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.