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.
@article{arxiv.2507.03435,
title = {ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction},
author = {Jarosław A. Chudziak and Michał Wawer},
journal= {arXiv preprint arXiv:2507.03435},
year = {2025}
}
Comments
10 pages, 8 figures, 1 table. This is the accepted version of the paper presented at the 38th Pacific Asia Conference on Language, Information and Computation, Tokyo, Japan