# Signal or Noise in Multi-Agent LLM-based Stock Recommendations?

arXiv: https://arxiv.org/abs/2604.17327 · HTML: https://mlsanity.com/paper/2604.17327/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 19 Apr 2026.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 19 of 19)

- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [FinGPT: Open-Source Financial Large Language Models](https://mlsanity.com/paper/2306.06031/) (2023)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://mlsanity.com/paper/2304.07619/) (2023)
- [Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)](https://mlsanity.com/paper/2402.10376/) (2024)
- [Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection](https://mlsanity.com/paper/2401.03737/) (2024)
- [Transforming Sentiment Analysis in the Financial Domain with ChatGPT](https://mlsanity.com/paper/2308.07935/) (2023)
- [MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents](https://mlsanity.com/paper/2502.00415/) (2025)
- [AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions](https://mlsanity.com/paper/2508.11152/) (2025)
- [Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research](https://mlsanity.com/paper/2411.04788/) (2024)
- [Profit Mirage: Revisiting Information Leakage in LLM-based Financial Agents](https://mlsanity.com/paper/2510.07920/) (2025)
- [Beyond the Black Box: Interpretability of LLMs in Finance](https://mlsanity.com/paper/2505.24650/) (2025)
- [AI in Investment Analysis: LLMs for Equity Stock Ratings](https://mlsanity.com/paper/2411.00856/) (2024)
- [RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets](https://mlsanity.com/paper/2510.14986/) (2025)
- [ATLAS: Adaptive Trading with LLM AgentS Through Dynamic Prompt Optimization and Multi-Agent Coordination](https://mlsanity.com/paper/2510.15949/) (2025)
- [Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks](https://mlsanity.com/paper/2602.23330/) (2026)
- [Measuring What LLMs Think They Do: SHAP Faithfulness and Deployability on Financial Tabular Classification](https://mlsanity.com/paper/2512.00163/) (2025)
- [The Stability Trap: Evaluating the Reliability of LLM-Based Instruction Adherence Auditing](https://mlsanity.com/paper/2601.11783/) (2026)
- [Could Large Language Models work as Post-hoc Explainability Tools in Credit Risk Models?](https://mlsanity.com/paper/2602.18895/) (2026)

## Built on by (most cited first, 1 of 1)

- [CyberAId: AI-Driven Cybersecurity for Financial Service Providers](https://mlsanity.com/paper/2605.01892/) (2026)

## Abstract (arXiv)

We present the first portfolio-level validation of MarketSenseAI, a deployed multi-agent LLM equity system. All signals are generated live at each observation date, eliminating look-ahead bias. The system routes four specialist agents (News, Fundamentals, Dynamics, and Macro) through a synthesis agent that issues a monthly equity thesis and recommendation for each stock in its coverage universe, and we ask two questions: do its buy recommendations add value over both passive benchmarks and random selection, and what does the internal agent structure reveal about the source of the edge? On the S&P 500 cohort (19 months) the strong-buy equal-weight portfolio earns +2.18%/month against a passive equal-weight benchmark of +1.15% (approximating RSP), a +25.2% compound excess, and ranks at the 99.7th percentile of 10,000 Monte Carlo portfolios (p=0.003). The S&P 100 cohort (35 months) delivers a +30.5% compound excess over EQWL with consistent direction but formal significance not reached, limited by the small average selection of ~10 stocks per month. Non-negative least-squares projection of thesis embeddings onto agent embeddings reveals an adaptive-integration mechanism. Agent contributions rotate with market regime (Fundamentals leads on S&P 500, Macro on S&P 100, Dynamics acts as an episodic momentum signal) and this agent rotation moves in lockstep with both the sector composition of strong-buy selections and identifiable macro-calendar events, three independent views of the same underlying adaptation. The recommendation's cross-sectional Information Coefficient is statistically significant on S&P 500 (ICIR=+0.489, p=0.024). These results suggest that multi-agent LLM equity systems can identify sources of alpha beyond what classical factor models capture, and that the buy signal functions as an effective universe-filter that can sit upstream of any portfolio-construction process.