Title: TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets
Venue: NeurIPS 2025
Accepted: yes
Keywords: LLM Agent

Multi-Agent System

Human-Behavior

Computational Social Science

Computational Finance

Market Impact

Financial Market Simulation
Abstract: The study of social emergence has long been a central focus in social science. Traditional modeling approaches, such as rule-based Agent-Based Models (ABMs), struggle to capture the diversity and complexity of human behavior, particularly the irrational factors emphasized in behavioral economics. Recently, large language model (LLM) agents have gained traction as simulation tools for modeling human behavior in social science and role-playing applications. Studies suggest that LLMs can account for cognitive biases, emotional fluctuations, and other non-rational influences, enabling more realistic simulations of socio-economic dynamics. In this work, we introduce TwinMarket, a novel multi-agent framework that leverages LLMs to simulate socio-economic systems. Specifically, we examine how individual behaviors, through interactions and feedback mechanisms, give rise to collective dynamics and emergent phenomena. Through experiments in a simulated stock market environment, we demonstrate how individual actions can trigger group behaviors, leading to emergent outcomes such as financial bubbles and recessions. Our approach provides valuable insights into the complex interplay between individual decision-making and collective socio-economic patterns.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper attempts to create a financial network agent-based model. The paper evaluates macro-level economic behavior based on a belief-desire-intention agent framework. This framework takes in information about news and the current portfolio and makes trading decisions. This is evaluated by a number of financial metrics.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper opens an intriguing new direction by modeling stock-market trading with agent-based methods. Its premise is timely and warrants a series of deeper studies, and this manuscript represents a solid first step. Although the experimental analysis is framed for a financial audience, readers in machine learning would benefit from moving key implementation details now confined to Appendices F and G into the main text. Understanding how the agents are constructed and validated is just as important as the macro-level economic trends highlighted below.\n\nIn section 4.1 evaluation: Every empirical finance study should report standard baselines so that performance is interpretable. In particular, please include the buy-and-hold return on a broad index (e.g., S&P 500) and compare each agent’s return against it whenever results are discussed. The text also needs to clarify whether your claim is that TwinMarket (i) reproduces human-like trading behavior or (ii) merely produces strong financial performance. If the former, what evidence demonstrates fidelity to real traders?\n\nIs this a zero-sum trading environment? Then some agents need to do very poorly for others to get rich. If the agents are substantiated similarly, this may not be possible. The only evidence of the difference is the return for each agent. There needs to be more specific analysis of the belief and traits, maybe what you are looking for with desires and intentions is a utility function? This is not clearly defined.\n\nWhile the macro events convincingly illustrate information diffusion, the micro-level mechanics of order execution and price formation are evaluated only superficially. It is unclear if the instances of the agents with belief and intent are actually mimicking specific trader preferences or only show the macro market behavior when grouped together.\n\nQUESTIONS:\nSee Strengths and Weaknesses.\n\nLIMITATIONS:\nThe mentioned limitation is the trading scenarios, but as mentioned in strengths and weaknesses, the micro analysis of the agents behavior leaves much to be desired.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 1, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes TwinMarket, a multi-agent framework that leverages LLMs to simulate investor behavior in a stock market. It features a design driven by the belief-desire-intention framework. Experiments are performed to simulation a 5-month period in Mainland China stock market, demonstrating that LLM agents can effectively model real-world behaviors, validate behavioral theories, and reveal underlying mechanisms of social emergence.\n\nSTRENGTHS AND WEAKNESSES:\nS1. Using LLM agents to simulate human behavior is a promising direction and has received considerable attention recently. \n\nS2. The experiments are extensive, covering both micro- and macro- factors. Ablation studies are sufficient, and multiple LLMs are tested as well.\n\nS3. The paper is well-written and easy to read. \n\nW1. The simulation only covers a scenario of SSE50 starting from June 15, 2023 for five months. This is a bear market with relatively low volumes. It is encouraged that more scenarios are also evaluated to better understand the performance of the proposed framework. For example, bull markets and black swan events, which are rare (financial crisis, etc.) but have significant impacts on the market.\n\nW2. It is unclear whether the framework can model long-horizon behavior, especially for the transition of investment style. For example, an investor may be a day trader with very frequent transactions at the beginning and gradually become a long-term trader. This is a common practice for many investors, because in the long run, we don't have enough time to keep an eye on the market. Risk tolerance and resistance to rumors may also change over time. Moreover, even veterans may change their investment style on rare events, e.g., they tend to exhibit speculative behavior under bullish policies (like late Sep. 2024 in China stock market). \n\nW3. It is unknown whether the framework supports shorting and hedging. As far as I know, retail investors are not allowed to short stocks in Mainland China. So I suppose shorting was not implemented in the simulation. There are other ways of hedging besides shorting stocks, but I didn't find them in the paper either. \n\nW4. While the paper claims it scales up to 1000 agents, most experiments are conducted on 100 agents. In addition, some experimental settings are unclear (please see questions below), especially for the reason why the transactions made by 100/1000 agents (as retail investors, I suppose) will significantly affect the stock price.\n\nQUESTIONS:\nQ1. Do you have results on the distribution of (1) the number of stocks in the portfolio, (2) assets, and (3) positions, across the 100 agents?\n\nQ2. For predictive accuracy, RMSE, MAE, and correlation are measure on what aspect? Stock price?\n\nQ3. In Figure 11(c) and Figure 13, I wonder how the transactions made by the 100/1000 agents affect the index price. In the real world, it is unlikely that the transactions made by 100/1000 retail investors have a strong impact on the index price. Institutions may cause such an impact. But I don't think they are BDI-driven, and the underlying factors of their behavior are more complex than individuals. \n\nQ4. Based on my knowledge, a trading lot equals 100 shares in Mainland China. That is, when buying stocks, the quantity must be in multiples of 100 shares. Are the agents aware of this rule in the simulation? \n\nQ5. Is cash a dynamic value? For example, investors may get salary paid every month and use it in the market. \n\nQ6. For information propagation, do opinion leaders have stronger trading power (i.e., more assets) and use such trading power to affect the market? While this may contradict my opinion in Q3, I'm just curious about the setting in the experiments.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel multi-agent framework called TwinMarket that uses a group of LLM agents to simulate the trading and message posting in a socio-economic environment. The simulation is grounded in the belief-desire-intention (BDI) framework, and partially utilizes the real-world trading data collected from various sources.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- This paper presents a very interesting experiment using LLM agents to role-play different human individuals in a trading system as well as on a social media platform. The simulation reveals findings that align well with existing micro-/macro-level economic phenomena observed from real-world data.  \n- The simulation has been analyzed in multiple facets, including comparison with traditional ABM baselines, analysis of how individual agents’ behavior contributes to a macro-level phenomenon, and ablation/scalability of the designed components.\n- If the codebase is released, it'd be a valuable resource for social science researchers.\n\nWeakness:\n- The simulation is only compared with traditional ABMs. Additionally, the ablation study only compared variants w/o BDI and without heterogeneity. However, it remains unclear how sensitive the simulation is to other design components (e.g., belief update strategy, graph structure, neighbor similarity estimation).   \n- The experiments are prioritized to analyze the socioeconomic phenomenon of the simulation (e.g., rumor propagation, bias polarization). This work is another successful demo of LLM agents in social simulation. The findings may be valuable for social science researchers; however, it remains unclear when and why LLM agents may succeed or fail in such a simulation, and how the framework generalizes to other simulation applications. In other words, the intellectual merits of this work to the ML community are unclear. How can we translate this finding to develop better role-playing agents or better training/test-time algorithms?\n- Another concern is data privacy. The user profiles are collected from several stock forums. However, the potential data privacy, copyright, or ethical issues are not clearly discussed. It's unclear whether it's illegal to use this data.\n\nQUESTIONS:\n- Some key design components lack sufficient clarity. For example, is a belief represented as a sentiment score (1-5) in the prompt? How does each agent update its belief? How are desires and intentions represented? What input data is used for predicting the next action? What is the trading intensity used for?\n- Some figures are hard to interpret. For example, there are still different peaks between the red and blue curves in Fig. 7. How does the mismatch tell us? Another example is Fig. 3. What's the definition of wealth share? How is it computed from the observational data? I understand that these are financial indices/metrics, but having a reference or a short description would improve the readability.\n- The hot score is computed in a heuristic way. How reliable is this score in recommending related posts?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents TwinMarket, a multi-agent simulation framework using LLM-driven agents embedded in a BDI (Belief-Desire-Intention) architecture to model investor behavior and social interactions in financial markets. The authors claim the approach better captures behavioral biases and emergent market phenomena like bubbles and volatility clustering, validated via stylized facts and ablation studies.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n**Actually quite ambitious:** the framework does a good job incorporating social interactions and heterogeneity in agents.\n\n**Impressively extensive experiments:** micro-level (wealth inequality, belief updates) and macro-level validations (stylized facts) are thorough and cover a wide range of real-world phenomena. The ablation studies are solid; they clearly demonstrate the necessity of both BDI modeling and agent heterogeneity. Scalability is well-explored, scaling up to 1,000 agents with plausible dynamics.\n\n**Weaknesses:**\n\n**The real world gap:** The simulation still lacks real-time adaptive feedback from genuine external data. It's effectively a closed system, making it questionable for studying real markets.\n\n**Quantitative benchmarking:** The authors show stylized fact metrics (e.g., kurtosis, leverage effect coefficients) approximate real market data. Did the authors perform any hypothesis tests or confidence interval analyses comparing simulated and real distributions?\n\nQUESTIONS:\n**Ungrounded Assumption:** In section 3.3, is it realistic to assume users with similar trading patterns are more likely to interact? Is there any reference to ground this assumption? Smilarly for time-weighted trading intensity, is this kind of modeling from BDI literature? If yes, a proper referencce is needed.\n\n**Missing discussion:** [1] also uses BDI for agent modeling. It worth a discussion in the main paper.\n\n[1] Can large language model agents simulate human trust behavior?\n\n**Quantitative benchmarking:** The authors show stylized fact metrics (e.g., kurtosis, leverage effect coefficients) approximate real market data. Did the authors perform any hypothesis tests or confidence interval analyses comparing simulated and real distributions?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-06"}