Automated Market Intelligence: A Multi-Agent Approach with MCP

Abstract

The sheer volume and velocity of information in financial markets create significant challenges for timely and accurate analysis. This paper presents a multi-agent system that uses relation extraction to derive actionable intelligence from financial news, corporate press releases, and market filings. The proposed Agentic AI system combines four agents: (i) a machine learning agent for sentiment analysis (logistic regression), (ii) a corporate profile agent for baseline fact-checking (which relies on named entity recognition), (iii) a narrative consistency agent (using LLM prompt engineering), and (iv) a real-time market data analyzer that extracts relational triplets for claim verification. The system is orchestrated via the Model Context Protocol (MCP), offering shared context and live learning across components. Results demonstrate that the multi-agent ensemble achieves 95.3% accuracy with an F1 score of 0.964 in identifying verifiable market claims, significantly outperforming individual agents and traditional approaches. The weighted aggregation method, mathematically derived from individual agent misclassification rates, proves superior to algorithmic threshold optimization. The modular architecture makes the system easily scalable while maintaining details of the analytical processes.

Introduction

The manipulation of financial information has long influenced market perceptions and investor decisions. Distinguishing genuine signals from speculative noise is a major challenge due to mixed types of data—verifiable facts, forward-looking statements, and corporate narratives—all of which affect market sentiment.

To improve financial analysis, the authors propose a multi-agent AI system that integrates several specialized agents for robust claim verification in financial news, particularly focusing on headlines and summaries. The system combines:

• Sentiment Analysis Agent: Classifies text sentiment based on financial language.

• Corporate Profile Agent: Checks claims against trusted company data using entity recognition and knowledge bases.

• Narrative Consistency Agent: Evaluates logical coherence of new announcements with past corporate communications using large language models.

• Real-Time Market Data Analyzer: Extracts and verifies semantic relationships from live web data for up-to-date fact-checking.

Trained on balanced datasets from financial news and regulatory filings, the multi-agent ensemble achieves high accuracy (95.3%) and outperforms individual agents. The system’s orchestrator coordinates agent outputs weighted by reliability, and a human-in-the-loop module provides recommendations for further investigation through ranked sources and official filings.

While promising, challenges remain including dependency on real-time web data, sector-specific performance variation, and vulnerability to adversarial manipulation. Nonetheless, the modular multi-agent approach enhances transparency, robustness, and trustworthiness in financial market intelligence.

Conclusion

Deriving actionable market intelligence through relation extraction is a highly complex task, posing not just technical but also semantic and contextual problems. Agentic methods have proven to perform better, as multiple specialized agents work together like a team of financial analysts, each bringing a different expertise to bear on the problem. A key takeaway is that complementarity is invaluable in complex analytical tasks. No single technique, whether a lightweight sentiment classifier or a massive LLM, can handle the task of market intelligence alone. In line with the MCP, we learn that context is a \"first-class citizen\". Incremental learning and shared context passing allowed each agent to build upon the findings of its predecessors, creating a richer, more nuanced final analysis. Our work demonstrates that combining diverse AI approaches through MCP orchestration can achieve significant improvements in analytical accuracy for market intelligence. As market dynamics grow more sophisticated, the modularity and human-in-the-loop features of the proposed solution position it well for future enhancements and real-world deployment in financial institution

References

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Copyright

Copyright © 2025 Amaan Mithani, Amaan Ansari, Gaurav Ghop. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.