The List / Categories
Financial agents connect models to memory, tools and decision environments. Multi-agent designs add coordination and communication, which should be evaluated separately from the strength of the underlying model.
For market decisions, continue with the trading guide; for knowledge-intensive tasks, use the RAG guide.
FinMem introduces an LLM-based trading agent with a layered memory system and dynamic character design that significantly outperforms deep reinforcement learning baselines in stock trading by effectively prioritizing time-sensitive financial data.
FinAgent is a multimodal LLM-based trading agent that integrates news, prices, and charts via a dual-level reflection mechanism and tool-augmented decision-making, significantly outperforming baselines in profit across stock and crypto markets.
TradingAgents introduces a multi-agent LLM framework that simulates professional trading firm dynamics through specialized analyst, researcher, trader, and risk management roles, achieving superior risk-adjusted returns compared to rule-based baselines on major tech stocks.
FinCon is an LLM-based multi-agent system with a hierarchical manager-analyst structure and conceptual verbal reinforcement that outperforms DRL and other LLM agents in single-stock trading and portfolio management by synthesizing multi-modal data and dynamically updating investment beliefs.
The paper proposes Heterogeneous Agent Discussion (HAD), a zero-shot framework using five specialized LLM agents guided by financial sentiment error types, which improves classification accuracy by 25-35% of the fine-tuning gap compared to naive prompting.
TradingGPT introduces a multi-agent LLM framework with layered memory and distinct character profiles to enhance automated stock and fund trading performance through collaborative debate and hierarchical information processing.
ABIDES is an open-source, high-fidelity agent-based discrete event simulation environment for equity markets that enables controlled experiments on market impact, co-location, and learning agents by replicating NASDAQ protocols with nanosecond precision.
The paper proposes the SEP framework, which uses a self-reflective agent and PPO to fine-tune LLMs for explainable stock prediction and portfolio construction without human annotations.
FinRobot is an open-source AI agent platform that integrates multi-source LLMs, financial Chain-of-Thought prompting, and a Smart Scheduler to automate complex financial analysis tasks such as equity research and market forecasting.
InvestorBench introduces a comprehensive benchmark and agent framework for evaluating LLM-based financial decision-making across stocks, cryptocurrencies, and ETFs, revealing that proprietary models significantly outperform open-source alternatives in complex, volatile markets.
CryptoTrade is a zero-shot LLM-based multi-agent system that integrates on-chain transaction statistics and off-chain news to guide daily cryptocurrency trading, outperforming time-series baselines but matching traditional technical indicators.
The paper proposes a multi-agent collaboration framework for financial investment research, demonstrating that an ensemble structure combining single agents for simple tasks and vertical multi-agents for complex risk analysis outperforms uniform architectures in accuracy and efficiency.
The paper proposes a three-stage LLM-driven multi-agent framework for automated alpha factor generation and dynamic weight optimization, achieving 53.17% cumulative return on the SSE50 index by adapting to market regimes.
This paper proposes an optimization framework for individual and household budgeting that integrates LLM-generated recommendations as initial feasible solutions, validated through a prospective verification mechanism and extended coevolutionary theory to enhance personalization and financial resilience.
HedgeAgents is a multi-agent system using LLMs to coordinate specialized asset experts and a fund manager through hedging conferences, achieving 400% total return and superior risk management across Bitcoin, stocks, and forex compared to baselines.
This survey systematically categorizes and reviews the applications of large language models in finance, covering linguistic tasks, sentiment analysis, time series forecasting, reasoning, and agent-based modeling, while providing a comprehensive collection of associated datasets, benchmarks, and code to facilitate research and adoption.
StockAgent is an LLM-driven multi-agent system that simulates stock trading to analyze how external factors like macroeconomic events and social sentiment influence agent behavior and profitability while mitigating test set leakage.
AlphaAgent is an autonomous multi-agent framework that uses LLMs to mine decay-resistant alpha factors by enforcing originality via AST similarity, hypothesis alignment, and complexity control, achieving superior risk-adjusted returns in CSI 500 and S&P 500 markets.
The Finance Agent Benchmark evaluates 20 LLMs on 537 expert-authored financial research tasks requiring tool use, revealing that even the best model (o3) achieves only 46.8% accuracy, highlighting significant gaps in autonomous financial analysis capabilities.
JAX-LOB is the first GPU-accelerated limit order book simulator that enables large-scale parallel reinforcement learning for optimal trade execution, achieving at least a 7x speedup over CPU-based implementations.
The paper introduces MarS, a financial market simulation engine powered by the Large Market Model (LMM), which generates realistic, interactive, and controllable order-level market trajectories to enable forecasting, risk detection, market impact analysis, and reinforcement learning agent training.
FLAG-Trader integrates a partially fine-tuned LLM with Proximal Policy Optimization to enable a small 135M-parameter model to outperform larger proprietary models in single-asset stock and crypto trading.
Discovered through citation/reference expansion; connected to 2 source papers.
STOCKBENCH evaluates LLM agents in a contamination-free, multi-month stock trading environment, revealing that while top models like Kimi-K2 can outperform buy-and-hold baselines, most struggle with profitability and risk management compared to simple passive strategies.
The paper proposes FAMA, a neural-symbolic agent that uses LLMs with Cross-Sample Selection and Chain-of-Experience to mine interpretable alpha factors, achieving superior predictive performance and portfolio returns on S&P 500 data compared to symbolic and neural baselines.
This paper introduces an open-source simulation framework where heterogeneous LLM agents act as trading participants in a realistic market with a persistent order book, demonstrating that LLMs can execute specific strategies, generate emergent market dynamics like bubbles, and serve as a tool for experimental finance.
FinRobot is an open-source multi-agent LLM framework that automates equity research by integrating quantitative data processing, conceptual reasoning, and narrative synthesis to generate professional-grade investment reports.
MarketSenseAI 2.0 leverages a multi-agent LLM architecture with RAG to integrate SEC filings, earnings calls, and macroeconomic reports, achieving 125.9% cumulative returns on S&P 100 stocks (2023-2024) and a 33.8% higher Sortino ratio on S&P 500 stocks (2024) compared to benchmarks.
This 2025 review synthesizes 84 studies on LLM applications in equity markets, categorizing them by financial use cases and technical methods to identify trends in forecasting, sentiment analysis, and multi-agent trading while highlighting gaps in real-world validation and interpretability.
The paper introduces Agent Market Arena (AMA), a live multi-market benchmark demonstrating that agent architecture, rather than LLM backbone, primarily determines trading performance and behavioral patterns in real-time financial markets.
LiveTradeBench introduces a live trading environment for evaluating LLM agents in U.S. stocks and Polymarket, revealing that high general reasoning scores do not guarantee trading competence and that models exhibit distinct, market-specific portfolio styles.
DeepFund introduces a live, multi-agent benchmarking framework for LLM-driven fund investment that eliminates historical data leakage, revealing that most state-of-the-art models incur net losses in real-time trading, with only Grok 3 achieving profitability through prudent risk management.
MASS introduces a multi-agent simulation framework with backward optimization for end-to-end portfolio construction, demonstrating a scaling effect where increasing agent count up to 512 improves excess returns on the Chinese A-share market.
FinArena is a human-agent collaboration framework that integrates specialized LLM agents for time series, news, and financial statements with adaptive RAG and investor risk preferences to improve stock trend prediction and personalized investment decisions.
QuantAgent introduces a multi-agent LLM framework for high-frequency trading that decomposes OHLC data analysis into specialized indicator, pattern, trend, and risk agents, achieving superior directional accuracy and risk-adjusted returns across diverse asset classes compared to traditional baselines.
Discovered through citation/reference expansion; connected to 16 source papers.
StockSim is an open-source, dual-mode simulation platform that evaluates LLM trading agents using realistic order-book microstructure and candlestick data, revealing distinct strategic behaviors between models like GPT-o3 and GPT-o4-mini.
TradingGroup is a multi-agent quantitative trading system that integrates specialized agents for sentiment, fundamentals, and forecasting with self-reflection mechanisms and a dynamic risk management module, achieving superior backtesting performance over baselines and enabling effective parameter-efficient fine-tuning via an automated data-synthesis pipeline.
DeltaHedge is a multi-agent framework integrating transformer-based forecasting, sentiment analysis, and an ensemble of reinforcement learning agents to dynamically hedge equity portfolios with protective puts, significantly outperforming traditional and standalone RL baselines in risk-adjusted returns.
The paper introduces the Agent Trading Arena, a zero-sum multi-agent simulation framework that demonstrates LLM-based agents significantly outperform traditional baselines when using visual chart inputs and a reflection module to handle dynamic market microstructure.
Discovered through citation/reference expansion; connected to 1 source papers.
ATLAS introduces a multi-agent trading framework with Adaptive-OPRO, a dynamic prompt optimization method that consistently outperforms static prompts and reflection-based feedback across diverse market regimes and LLM families.
The CogAlpha framework leverages a seven-level agent hierarchy and LLM-driven evolutionary search to discover interpretable, robust alpha factors that outperform traditional machine learning and existing LLM-based baselines across multiple global stock markets.
This paper introduces a role-based multi-agent LLM framework that generates investment reports from earnings call transcripts, achieving 58.1% decision accuracy and outperforming professional analyst reports in human evaluations by prioritizing logical structure and practical utility over surface-level quality.
The paper develops an agentic framework where LLMs iteratively discover stochastic differential equations to estimate market risk, demonstrating that model-informed trading decisions significantly outperform standard sentiment-based LLM agents in both historical backtests and synthetic market simulations.
TiMi is a rationality-driven multi-agent system that decouples strategy development from minute-level deployment, achieving superior risk-adjusted returns and low latency across 200+ trading pairs by leveraging specialized LLMs for semantic analysis, code generation, and mathematical optimization.
TradeTrap demonstrates that LLM-based trading agents are highly vulnerable to system-level perturbations, where small attacks on market intelligence, strategy, or state management can cause catastrophic portfolio drawdowns and unreliable behavior.
DeepFund introduces a live, multi-agent arena platform to evaluate LLM-based fund investment strategies in real-time, addressing critical flaws in existing benchmarks such as data leakage, theoretical disconnect, and excessive human intervention.
SimVC-CAS predicts startup financing success by simulating collective venture capital decision-making through LLM-based investor agents interacting over a co-investment network, achieving a 25% relative improvement in AP@10 over baselines.
This paper proposes a multi-agent LLM framework for Chinese Public REITs that decomposes analysis into four specialized agents and compares a general-purpose LLM (DeepSeek-R1) against a fine-tuned small model (Qwen3-8B), finding both significantly outperform buy-and-hold with superior risk-adjusted returns.
Discovered through citation/reference expansion; connected to 4 source papers.
FinSphere is a real-time stock analysis agent that integrates proprietary quantitative tools with an instruction-tuned LLM, achieving superior expert-rated analytical quality compared to general and domain-specific baselines.
QuantaAlpha is an evolutionary multi-agent framework for alpha mining that improves factor discovery through trajectory-level mutation and crossover, achieving superior predictive power and cross-market robustness compared to existing LLM-based agents.
The deployed MarketSenseAI multi-agent LLM system generates strong-buy recommendations that significantly outperform passive equal-weight benchmarks and random selection on the S&P 500, with performance driven by an adaptive synthesis of specialist agent signals rather than a single dominant factor.
This conceptual paper introduces Perceived Cognitive Assistance (PCA) to explain how LLMs alter retail investors' strategy selection and risk posture, proposing a moderated regression model and a Behavioral Shift Index (BSI) for future empirical validation.
This paper demonstrates that decomposing investment analysis into fine-grained, expert-defined tasks within a hierarchical multi-agent LLM system significantly improves risk-adjusted returns and interpretability compared to coarse-grained baselines in Japanese equity backtesting.
This paper presents an automated multi-agent framework for venture capital due diligence that integrates real-time web retrieval, reverse-engineered access to the Greek Business Registry for official financial filings, and a structural fallback mechanism to prevent hallucination by explicitly flagging data gaps.
Discovered through citation/reference expansion; connected to 3 source papers.
AlphaCrafter is a full-stack multi-agent framework that unifies LLM-driven factor discovery, regime-adaptive selection, and risk-constrained execution, demonstrating robust live trading performance on CSI 300 and S&P 500 indices by outperforming static and decoupled baselines.
AlphaQuanter introduces a single-agent reinforcement learning framework that optimizes tool-augmented decision-making for stock trading, achieving state-of-the-art performance and higher faithfulness than multi-agent or prompt-only baselines.
Discovered through citation/reference expansion; connected to 1 source papers.
Discovered through citation/reference expansion; connected to 3 source papers.
FactorEngine introduces a program-level factor mining framework that combines LLM-guided macro-evolution with Bayesian micro-optimization to discover executable, interpretable alpha factors from OHLCV data and financial reports, achieving state-of-the-art predictive and portfolio performance on Chinese A-share markets.
Discovered through citation/reference expansion; connected to 3 source papers.
Discovered through citation/reference expansion; connected to 2 source papers.
P1GPT introduces a layered multi-agent LLM workflow that fuses technical, fundamental, and news data through structured reasoning to generate interpretable trading signals, demonstrating superior risk-adjusted returns compared to baselines on major U.S. equities.
PolySwarm is a multi-agent LLM framework that deploys 50 diverse personas to trade prediction markets via Bayesian aggregation and KL-divergence-based arbitrage, outperforming single-model baselines in calibration.
PyFi introduces a pyramid-structured dataset and adversarial multi-agent synthesis framework to enable vision-language models to perform hierarchical financial image understanding, significantly improving accuracy on complex decision-support tasks through chain-of-thought fine-tuning.
TradeFM is a 524M-parameter generative Transformer that learns universal market microstructure dynamics from billions of trade events across >9K equities, achieving 2-3x lower distributional error than Hawkes baselines and zero-shot generalization to APAC markets.
The paper introduces the Agent Trading Arena, a closed-loop multi-agent simulation where LLMs trade in a zero-sum environment, demonstrating that visual inputs and reflection modules significantly improve numerical reasoning and trading performance over textual data and static baselines.