A multi-agent LLM equity system produces statistically significant outperformance on S&P 500 stocks, with strong-buy portfolios returning +2.18% monthly versus +1.15% for the equal-weight benchmark over 19 months.
A Detailed Trading Simulation Setup In this appendix we explain our trading simulation setup in enough detail to make our results reproducible
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AgentSteerTTS proposes a multi-agent framework with adversarial disentanglement, dual-stream anchoring via acoustic prototypes, and fast-slow feedback to achieve intent-faithful expressive TTS for composite instructions.
LLM agents (hawkish, dovish, debate) outperform a deterministic z-score rule agent in Sharpe ratio for commodity ETF portfolios by 0.04-0.044, with advantage concentrated in the soft-landing sub-period and preserved up to 30bp trading costs.
MadEvolve uses LLMs for evolutionary optimization of trading strategies and reports significant backtest improvements on Bitcoin tasks including signal feature evolution and joint strategy optimization.
Reproducibility audit of 30 LLM trading papers shows execution assumptions under-reported relative to agent architectures, illustrated by a 10-equity example where frictions compress returns.
This review synthesizes LLM uses in stock forecasting and catalogs key practical pitfalls from a hedge-fund viewpoint.
citing papers explorer
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Signal or Noise in Multi-Agent LLM-based Stock Recommendations?
A multi-agent LLM equity system produces statistically significant outperformance on S&P 500 stocks, with strong-buy portfolios returning +2.18% monthly versus +1.15% for the equal-weight benchmark over 19 months.
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AgentSteerTTS: A Multi-Agent Closed-Loop Framework for Composite-Instruction Text-to-Speech
AgentSteerTTS proposes a multi-agent framework with adversarial disentanglement, dual-stream anchoring via acoustic prototypes, and fast-slow feedback to achieve intent-faithful expressive TTS for composite instructions.
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Macro Economists in the Machine: A Multi-Agent LLM Framework for Commodity-Related ETF Portfolio Construction
LLM agents (hawkish, dovish, debate) outperform a deterministic z-score rule agent in Sharpe ratio for commodity ETF portfolios by 0.04-0.044, with advantage concentrated in the soft-landing sub-period and preserved up to 30bp trading costs.
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MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models
MadEvolve uses LLMs for evolutionary optimization of trading strategies and reports significant backtest improvements on Bitcoin tasks including signal feature evolution and joint strategy optimization.
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Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems
Reproducibility audit of 30 LLM trading papers shows execution assumptions under-reported relative to agent architectures, illustrated by a 10-equity example where frictions compress returns.
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A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective
This review synthesizes LLM uses in stock forecasting and catalogs key practical pitfalls from a hedge-fund viewpoint.