TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets
Authors: Yuzhe YANG
Yifei Zhang
Minghao Wu
Kaidi Zhang
Yunmiao Zhang
Honghai Yu
Yan Hu
Benyou Wang
Venue: NeurIPS 2025 poster ()
Keywords: LLM Agent
Multi-Agent System
Human-Behavior
Computational Social Science
Computational Finance
Market Impact
Financial Market Simulation
Primary area: machine_learning_for_sciences
TL;DR: We introduce TwinMarket, an LLM-based multi-agent framework that simulates socio-economic systems by modeling how individual behaviors interact to produce emergent market dynamics like bubbles and crashes.

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.

TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets
Yuzhe Yang'; Yifei 1 Wu
Kaidi Zhang', Yunmiao Zhang". Honghai Yu Yan Hu Benyou Wang"!
The Chinese University of Hong Kong, Shenzhen, Nanjing University
Jiangsu Key Laboratory of Digital Finance
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World Knowledge
Market
Action
Action
Environment
News
Query
Trading
21
Stock
Search
Trading System
Posts
Intention
System
Recommandarion
Update
Drive
update
Aozerata
Belief
Policy
Making
Social Environment
Figure Overview of Tainearket environment where each user has a unique persona within the social network, interacts with the environment in real-time, and influences it through their actions. This framework enables the study of emergent social phenomena.
Abstract
The study of social emergence has long been central focus in social science. Traditional modeling approaches, such rule-based Agent-Based Models (ABMs), struggle to capture the diversity and complexity of human behavior, particularly the irrational factors emphasized in behavioral economies. Recently, large lan- guage 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, novel multi-agent framework that leverages LLMs to simulate socio-ccomomic 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, demonstrate how individual actions 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.
"Equal contribution. 'Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Recent advances in large language models (LLMs) have showcased their remarkable capacity to capture complex patterns in language, decision-making, and social interactions [1. 2]. These capa- bilities have positioned LLMs as powerful instruments for agent-based, simulations in the study of human behavior, supporting a wide range of social science applications. including conversational dynamics [3-6], emotional responses [7], and creative reasoning [8-10]. These developments open new frontiers for social science research, enabling the modeling of fine-grained individual behavior at the micro-level and the simulation of complex, emergent phenomena at the macro-level.
Financial markets, with their unique combination of high-resolution transactional data, observable collective phenomena (e.g. herding behavior & market crashes), and direct socio-economic relevance, provide an ideal testbed for studying how micro-level behaviors propagate to macro-level dynamics [1I, 12]. However, existing agent-based models (ABMs) in social simulation primarily rely on rule-based approaches, which often oversimplify human decision decision-making by assuming homogeneous agents and static behavioral rules [13, 14]. These oversimplifications fail to capture critical intricacies of real-worke economic behavior and struggle to scale to market complexity, obscuring causal links from individual decisions to systemic emergence.
The study of social emergence phenomena, where micro-level individual behaviors aggregate to form macro-level collective outcomes, has long been central focus in social science [15, 16]. However, current methods often struggle to capture the dynamic interplay between individual biases (e.g,, overconfidence) and emergent market phenomena (e.g., volatility clustering) [17, 18]. Recent advances in LLMs offer a promising avenue to address this challenge, enabling the simulation of adaptive agents whose decisions are shaped by both private information and social interactions. Motivated by this potential, we propose TwinMarket, novel multi-agent framework that leverages LLMs to simulate investor behavior in stock market environment. By modeling investors that independently make investment decisions while interacting through simulated social media platform, TwinMarket provides a unified perspective on how micro-level behaviors drive macro-level market dynamics.
The key innovation of TwinMarket is its use of the Seffe--Desire-nttentee (BDI) framework [19] to structure and visualize the cognitive processes of agents, providing transparent and interpretable modeling approach. Additionally, we develop a social environment that enables agents to engage in dynamic information exchange and social influence processes, enabling the study of phenomena such as the emergence of opinion leaders and the dynamics of information cascades. Finally. by scaling our simulations to multiple financial assets and larger populations, we investigate how group size and interaction complexity affect collective behavior and emergent phenomena.
Our research makes three key contributions:
Our framework is grounded in established behavioral theories and calibrated with real-world data, ensuring theoretical rigor and realistic human behavior modeling. This enables us to accurately reproduce certain social phenomena, allowing us to further study the mechanisms behind these phenomena.
Real-World Alignment:
By leveraging an LLM-based framework, we capture diverse human behaviors and their interactions, particularly in the context of information propagation. This allows us to model how opinions spread, influence decision-making, and dynamically shape collective market behaviors in real time.
Dynamic Interaction Modeling:
Scalable Market Simulations: We conduct large-scale simulations analyze the impact of group size on market behavior, providing new insights into price fluctuations and emergent dynamics.
2 Background
Traditional ABMs face two major challenges in simulating complex social systems [20]: (1) cus- tomizing sophisticated economic decision-making making for individual agents and (2) enabling agents to actively perceive and respond to external environmental changes. Recent research has made progress by incorporating I.LMs into agent-based simulations. CompeteAl [21] introduces a framework for modeling competitive dynamics; however, its agents rely on predefined prompts, limiting their ability to simulate nuanced economic behavior. EconAgent [22] designs economic agents with perception,

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reflection, and decision decision-making capabilities to model macroeconomic dynamics but overlooks the influence of multi-agent interactions on emergent macroeconomic phenomena. Similarly. ASFM [23], which models stock market responses to regulatory policies, fails to adequately account for the role of agent interactions and collective decision-making shaping market trends.
In our TwinMarket framework that leverages LLM-based agents simu- late complex human behaviors, we in- clude rational decision-makes making, tech- nical analysis, and behavioral biases such as herding and overconfidence [24, 25]. By grounding our model established behavioral theories [26-
Table 1: Existing financial agent systems.
System
Agent Design
Scale Interaction
CompeteAI
Fixed prompts
2 agents, competitive
EconAgent
Demographic based
100 agents, no interaction
ASFM
Two strategies
N/A. no interaction
TwinMarket
Fully contigurable
1000+. rich interaction
28] and calibrating it with real-world data, we ensure both theoretical rigor and empirical relevance. In Market, agents make investment decisions within socially embedded environment where interactions occur through price dynamics and social media, shaping collective market behavior via information propagation, behavioral imitation, and interaction mechanisms.
This work demonstrates the potential of LLMs to bridge computational simulations and behavioral science by systematically modeling micro-level cognitive processes and their macro-level societal effects. By integrating LLMs with established theoretical frameworks, TwinMarket provides an experimental platform to study how individual and economic phenomena. Our findings illustrate how LLMs can simulate real-world behaviors, validate behavioral theories, and uncover mechanisms underlying social emergence, advancing the academic understanding of complex human systems.
aggregates into emergent social
3 TwinMarket
3.1 Overall Design of TwinMarket
The design philosophy of TwinMarket emphasizes both micro-level and macro-level simulation. At the micro-level, we focus on modeling user behavior, illustrated in Figure 2(a). This entire process is driven by BDI framework, which we will discuss in Section 3.2. At the macro-level, as illustrated in Figure 2(b), we simulate two core infrastructures of real-world financial ecosystem: an order-driven trading system that settles transactions and updates the stock index accordingly (detailed in Appendix E.5), and a social media environment that personalizes information delivery based on user behavior while enabling social interaction among agents.
Additionally, TwinMarket is data-driven platform that leverages real-world data sources to construct a realistic and credible simulation enviromment, as summarized in Table 2. Our synthetic user data is sampled based behavioral patterns observed from real users, and enriched with actual stock data, news, and company announcements. This setup enables simulated users to make informed decisions, allowing us to observe the emergence of macro-level market phenomena from individual interactions.
Micro-level: User
(b) Macro-level: System
Interaction
User Profile
Initialization
Validation
Trading Decision
Posts
Envrionment
Information
Belief
Self-Evaluation
Order--oe Driven Trading System
Society
Intention
Read
Stook Recommendaation
Update
Hightights
Posts
Query
Matching System
Posts
Stock Information
Desire
Social Media Resommendation
Tronsanctions
Social Medio
News
Announcements
Update Network
Figure 2: The simulation workflow driven by the Belief Desire Intention framework. The micro-level simulation focuses modeling individual user behavior, while the macro-level simulation addresses the dynamies of the social media platform and trading system.

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Table 2:
Summary of data sources for TainMarket. Further details about our data sources are provided in Appendix C.
Usage
Source
Quantity or Time Period
Data Type
User Profile Initialization
Xueqiu'
639 users
User Profile
11,965 transactions
Transaction Details
Stock Recommendation
Guba Stock Forum?
83,246 transactions
Transaction Details
Update Fundamental Stock Data
CSMAR
Jan 2023 Dec 2023
Stock Data*
Sina' & 10jqka CNINFOO
News Articles
World Knowledge Datahase
Jan 2023 Dec 2023
Company Announcements
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3.2 Micro-Level Simulation: BDI-driven User Modeling
In user modeling, the BDI framework plays central role in simulating the cognitive processes of users. It enables each agent to perceive the market environment, integrate that information with its individual profile, and form unique chain of reasoning that guides decision-making. Figure 2(a) shows how an M.L.- agent operates in a daily loop: receiving news and posts, querying from knowledge base, making trading decisions, and interacting via social platforms. Each action in this loop is governed by the agent's 's beliefs, desires, and intentions, enabling nuanced and individualized behavioral responses. A formal definition of the BDI components and the agent's 's daily operational cycle is provided in Appendix E.2.
Belief. Belief provides the dynamic foundation for how an agent perceives and interprets its en- vironment. To ensure agents follow consistent personas, we inject system prompts that encode individualized traits. Using real transaction data and user profiles from Table 2, we first estimate behavioral biases and then extract key characteristies such as demographics and investment styles. These features are used to synthesize user profiles, forming the basis of our agent society. The distribution of these synthetic profiles closely aligns with findings from prior financial research [29]. As user profiles are initialized, beliefs encoded as system prompts guide agents to read and interpret news and social media posts. These perceived signals, grounded in agent traits, shape their evolving understanding of the environment and serve as the cognitive basis for subsequent decision-making.
After completing all actions for the day, each agent evaluates its behavior based on both its actions and the environmenta feedback it received. Through this process, the agent dynamically updates its beliefs, reflecting an evolving understanding and interpretation of the market. This captures the notion that user's 's perception and judgment of the environment are not static but shaped continuously by experience and new information. To track belief dynamics and explore micro-to-macro linkages, agents output a sentiment score' at each update step.
Desires. Desires represent the goals objectives an agent aims to achieve based on its belief's. While beliefs provide an understanding of the environment, agents autonomously generate queries to retrieve relevant stock and news information, enhancing their comprehension of the market context.
Intentions. Intentions represent the committed plans or actions that an agent chooses to pursue among its possible desires. While desires reflect what an agent wants, intentions define what it actively decides to do, given its current beliefs and constraints. These intentions drive the agent to make final trading decisions and to express amplify opinions through posting or reposting content on social media.
3.3 Macro-Levvl Simulation: Dynamic Social Network
The relationship between micro-level agent behavior and macro-level market dynamics is central to understanding complex financial systems [30]. The market we design is inherently dynamic, driven
leveer-level transaction data is sensitive, and our goal is to show that the data we collect is reliable, especially compared to prior studies with access to proprietary firm data.
'The sentiment score ranges from 1 to 5 and is computed as the average of five self assessed dimensions, based on user's views on: economic fundamentals, market valuation levels, short-term market trends, the sentiment of surrounding investors, and self-confidence, detailed in Appendix E.

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by agents whose behaviors evolve over time. This requires the system to be both adaptive and scalable, capable of responding shifts individual behavior and interaction patterns. To achieve this, we follow two key principles. First, the graph structure must evolve dynamically to reflect changes in agent connectivity. Second, agents must be able to aggregate and interpret environmental information through their networked relationships.
Constructing Dynamic Social Network. We model the social network as a dynamic graph
Induatry
Inssperatiion & logittcs
(V.E), where is the set of users and E denotes weighted edges that capture similarity in trading behavior. Each user trades industry-
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stock in- dex T == (i1,.... ), and we assume that users with more similar trading patterns are more likely to interact [31, 32]. To apply expo- nential time decay to each trading record T(u, i, t), resulting üime-weighte trading intensity w(u. i) = EiET(u.3) e AAAAt where At is the time elapsed since trade time t. We then compute the weighted Jaccard similarity between users to determine the strength of social connections [33]. We provide a sensitivity analysis of graph construction parameters in Appendix B.I. Figure shows the dynamic social network as chord diagram. Colors indicate user groups with similar industry preferences (e.g., Manufacturing, Finance Service), and chord thickness reflects the strength of trading similarity between groups.
Techeoioyy & Communicaiinn
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Figure 3: Chord diagram of user dynamic social network.
reflect temporal dynamics,
Information Aggregation Mechanism. To control each agent's perception field and ensure exposure to relevant informa- tion, we design an aggregation mechanism based on the dynamic social network. As
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illustrated in Figure 4, information forms propagation chains as it spreads through the network, and the information each agent re- ceives restricted by its perception field. For a target user U, E V, consider neigh- bors u E ((()) with similarity (uu, u) > For each such user, retrieve their posts P(u) within a time window [tu, tel. where each post p P(u) contains meta-
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data such as timestamp fp. upvotes up, and downvotes dp. We compute a hot score h(p) for each post to balance popularity and recency:
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te fe-tty tp a
Hot Score
T-=mas max
2.0
(Tp 1)1
where t. is the current time, e prevents division by zero, and a is a time normalization constant controlling decay rate. As illustrated Figure 5, we set such that post's influence typically lasts around two weeks before its score significantly diminishes. Posts from all qualified neighbors are ranked by their hot scores, and each user is recommended the top posts per day. This mechanism enables person- alized and dynamic information exposure shaped by social similarity and engagement dynamics. More technical details about information on social media are further discussed in Appendix E.3.
1.5
Hot
0.0
10 Creation
Days
Figure 5: Hot score decay: Post influence lasts weeks.
4 Experimental Validation
To evaluate the realism and reliability of our simulation framework, we conduct series of experiments comparing the outcomes generated by TwinMarket with real-world financial and behavioral data. Our
'We use a generalized Jaccard similarity for real-valued vectors. calculated by: S E, min(wi, w2)/ E, maxx(w,, w2).
5

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validations are performed both in micro and macro level. which are conducted on a simulated social network with 100 user agents, where each agent is powered by GPT-4o. The simulation runs from 2023-6-15, and lasts for five months.
4.1 Micro-level Validation
We validate the micro-level realism of TwinMarket by examining emergent behavioral and economic patterns, with qualitative examples discussed in Appendix B.5.
Coefficient 10% Wealth
Bottom 50%
0.43
55.0%
Gini Coefficient
50.0%
Inequality in the Stock Market. TwinMarket exhibits emergent patterns of wealth inequality, demonstrating its micro-leve fidelity. As shown in Figure 6, the Gini co- efficient displays clear upward trend, accompanied by a growing wealth share for the top 10% of agents and a declining share for the bottom 50%. This self-organizing wealth divergence arises from the heterogeneity and in- teractions among agents, echoing established findings in the economics literature [34]. It highlights the model's capacity generate realistic socio-cconomic dynamics from its micro-level specifications.
0.42
Wealth Shart
35.0%
0.41
40.0%
35.0%
0.40-
30.0%
0.39 2023-07
2023-10
25.0%
Figure 6: Rising Gini coefficient, indi- cating widening wealth inequality.
Trading Activity and Returns. Table 3 shows the top 10% of agents (ranked by return) achieved an average return of 6.65% with modest turnover rate of 4.02%. In contrast, the bottom 50% showed a higher average turnover rate of 7.03% but suffered substantial negative return of 10.52%. This pattern reflects well well-documented phenomenon among retail investors, where higher trading frequency even before accounting for transaction costs correlates with poorer performance [35]. Such behavioral consistency further reinforces the micro-level validity of TwinMarket.
Table 3: Agent performance Trading frequency in TwinMarket
Performance
Avg. Turnover
Avg. Retum
Top 10%
4.02%
6.65%
Bottom 50%
7.03%
-10,52%
4.2 Macro-level Validation
To assess TwinMarket's ability to replicate realistic market dynamics, we compare its simulated outputs against historical data by examining four widely recognized stylized facts. These are empirical statistical regularities consistently observed in real-world financial markets, and are commonly used benchmarks for validating the realism of market simulation models [36].
Stylized Fact Fat-tailed distributions [37]: Big market moves happen more often than what normal distribution would suggest. This means extreme ups and downs are not as rare as we might expect.
(a)Fat-talled Distribution
Figure shows TwinMarket's price change distribution (Log Return on its x-axis) mirroring real-world markets by exhibiting fat tails. While most daily price changes are small (forming its sharp central peak), large, unexpected surges plunges (the heavy tails) oceur more frequently than normal distribution would predict, capturing actual market risk. This realism stems from our autonomous agents, which, based on BDI cognitive frameworks, process information heterogeneously, leading to diverse and potentially conflicting sentiments. These divergent sentiments then propagate through the integrated social network, becoming amplified and polarized, which in turn directly leads extreme market outcomes.
real
40
ours
00.00 -0.4
-0.2 0.00 0.02
0.04
0.38
Log Retum
Figure 7: Fat-tailed price changes: more extreme movements.
Stylized Fact Leverage Effect [38]: When prices go down, the market often becomes more volatile afterward. This means bad news makes the market more nervous than good news.

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(blLeverage Effect
Figure 8 shows TwinMarket capturing the leverage effect, where volatility responds asymmetrically. Specifically, initial price drops (x-axis Negative Returns) provoke larger subsequent drops (y-axis Lagged Negative Returns), thereby amplifying market volatility. This realistic asymmetry is direct conse- quence of our LLM agents' design; their BDI cognitive architec- tures are intentionally embedded with behavioral biases, such as loss aversion. This design makes them, akin to humans, inherently more sensitive to adverse signals. The integrated social media platform further amplifies the spread of negative sentiment, resulting in more widespread, synchronized, and potentially destabilizing collective trading behavior.
0.00
Lagped Neggttae Beturns
-0.03
-0.63
1..04
+0.05
-0.00
OLTS
-0.07 -0.00 06
-0.55 -0.01
-0.03
-3.07 -0.01 5.6
Figure 8: Initial price drops can trigger subsequent price drops.
Stylized Fact III. Volume-Return Relationship [39]; Larger price changes usually happen when more people are trading, showing a positive link between trading volume and return size.
IclVolume Return Relationship
Figure 9 shows TwinMarket's s ability to replicate the volume- return relationship. Specifically, it shows that substantial in- creases in trading activity (x-axis Volume Change) are typically accompanied by significant market price fluctuations (y-axis Log Return). This co-movement is a key indicator, as in real markets, of collective behaviors such as herding among partici- pants. In TwinMarket, the emergence of this co-movement pro- vides compelling evidence demonstrates that our LLM agents, through their interactions, successfully generate collective be- havioral patterns consistent with those observed in actual finan- cial market dynamics.
0.00
resl
0.0c
Returr
0.69
0.00
00.00
-0.06
Volume Cherge
Figure 9: Corr. between trading volume & price changes.
Stylized Fact IV. Volatility Clustering [40]: When the market is volatile, it often stays volatile for while. When it's calm, it usually stays calm too. This means that big small price changes tend to come together, not appear randomly.
Id/alatiilty Clustering
Real Maros Adtal Voles trraum000 di-i 59 Iveriite Acual loletion by 1o-000 E--03001
Figure 10 shows TwinMarket successfully replicating volatility clustering: periods of large price swings (high Volatility y-axis) tend to cluster together, as do periods of small swings (low Volatility). Both simulated and real volatility series clearly show these alternating groupings of high and low market turbu- lence over time. This emergent persistence arises impactful market-moving events, processed via the BDI cognitive architec- ture, induce sustained heightened belief sensitivity. The social network then perpetuates these collective cognitive states by cir- culating related discussions or sentiments, prolonging periods of either erratic or stable trading.
Welalitt
M
1-
7J
20
25
50
75
100
125
150
375
Days
Figure 10: Big price swings group together; small ones too.
ABM Baselines Comparison. We compare Twin- Market against two transitional agent-based models: the HPM Model [41], which captures investor strat- egy adaptation through herding behavior, predisposi- tions, and reactions to price misalignments; and the BH Model [42, 20], which incorporates hyperbolic discounting and stochastic volatility to reflect hetero- geneous investor behavior. As summarized in Table 4. TwinMarket consistently generates outputs that align more closely with real-world market statistics across all four stylized facts. This demonstrates its superior ability to replicate fundamental financial dynamics, thereby validating the empirical robustness of our simulation framework. These results were further validated for reproducibility and stability, detailed in Appendix B.3.
Table 4: Comparing Stylized Facts (SF) across Real Data, TwinMarket, and ABM baselines.
System
SF I
SF
SF III
SF
Real Data
7.26
0.14
p<0.01
0.95
TainMarket
5.24
0.11
P<00.1
0.89
ABM-HPM
4.47
0.05
P<0.01
0.82
ABM-BH
4.99
0.19
P<00.01
0.72
7

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The four stylized facts (SF) in Table 4 are quantified as follows: (1) Kurtosis (SF I): Measures tail heaviness; values greater than 3 indicate fat-tailed return distributions. (2) Negative Return Autocorrelation (SF II): Captures asymmetric responses negative shocks. (3) Volume Return Correlation (SF III:: significant p-value (< 0.01) suggests stronger volume-volatility linkage. (4)
GARCH Volatility Clustering (SF IV): The sum + 3 z reflects persistent volatility, a hallmark of financial time series [43].
Emergence Analysis
Building upon the empirical validation of our simulation results, have demonstrated that Twin- Market can generate behavior and market patterns consistent with real-world financial and social dynamics. To further investigate how micro-level agent behaviors give rise to macro-level phenomena, we conduct an extended set of experiments aimed at uncovering the mechanisms of social and market emergence. These analyses focus the interplay between individual belief updates, local interactions, and global outcomes, offering insight into how localized decision rules and information asymmetries can lead to systemic patterns such as bubbles, polarization, and volatility.
5.1 Micro-level Emergence: Self-fulfiling Prophecy
A self-fulfilling prophecy describes a process where individ- ual expectations collectively influence behaviors in ways that ultimately validate the original expectations [44]. This mech-
14
Price Index (Normalized redd
6.25
anism exemplifies micro-level emergence: localized belief up- dates and actions aggregate into systemic market outcomes, In our BDI framework, agents continuously revise beliefs, which guide their trading decisions and shape market dynamics. Twin- Market simulations reveal that early optimism amplifies buy- ing pressure, driving prices upward and reinforcing collective confidence. This positive feedback loop detaches prices from fundamentals until sharp correction ensues. As shown in Fig- ure 11, belief and price co-evolve, replicating the speculative boom-bust cycle [45] and highlighting how collective belief formation drives macro-level patterns.
6.00
5.75
5.50
5.25
5.00
1.0
Figure 11: of the price index and belief, illustrating their strong correlation.
Simultaneous rise and fall
5.2 Macro-level Emergence: Information Propagation
High Degree Users Degree Users
120-
JM
Previous work highlights how information propagation pro- duces market-wide echo effects [46, 47). To probe the same mechanism, we identify the day's most salient news items and deliver them directly to users with the highest network central- ity, reproducing the information asymmetry characteristic of real markets. We then rerun the experiment with a parallel set of exaggerated, rumor-like headlines (We list some examples in Appendix E.4.) to stress-test the system. Observing how these targeted, micro-level interventions cascade through the network and reshape aggregate prices and volumes allows us to treat information diffusion as a source of macro-level emer- gence: the collective dynamics that arise cannot be inferred by examining any single agent in isolation, but only by tracking the wide response to the injected signals.
100
Value
20
Date
Figure 12: Average likes trends
among different centrality users.
2023-006-15 2023-06-4
Intimation Polarization. MainMarket, graph edges rep- resent behavioral similarity, meaning users closely connected exhibit stronger intimation effects. As shown in Figure 12, high-degree users receive more upvotes, indicating their greater influence within network. These opinion leaders [48] shape decision-making fostering homogeneity and leading to polarization, as depicted in Figure (further explored in Ap- pendix B.4). The network's adjacency matrix highlights clusters
Normal News
Rumors
Figure 13: Heatmap of the user trad- ing similarity adjacency matrix. Higher density indicates greater hehavioral similarity.

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of trading similarity, further intensified under rumor conditions. This suggests that biased or extreme information spreads more rapidly in tightly connected groups, reinforcing polarization [49, 50].
Delief Trends
(3) Se Ratios Trends
(c) Stock Index Price Trends
Inte (homallad))
AYG AVG
Batios (lumno)
3.45
Ratios
0:37
Divergeme
0.32
Volue Biry
Tacbulisc
0:32
Index
iRumor
Ga2
Figure 14: Rumor propagation impacts user beliefs and market trends. (a) Users' belief scores decline under rumor. (h) Users exhibit a stronger tendency to sell, and Sell/Buy ratio increase. (c) The collective reaction to rumors triggers market downturn, causing stock prices drop-down.
Belief Divergence. Figure 14(a) illustrates how belief trajectories diverge over time between the control and sumor-exposed groups. Replacing neutral news with negative rumors pushes expectations downward and reinforces echo chamber dynamics 52]. Rumor-affected users become increasingly pessimistic, interact primarily with like-minded peers, and distance themselves from the broader population [53, 54). This process intensifies belief heterogeneity and entrenches informational silos in the market.
Trading Volatility. Rumors do not merely shift beliefs, they also alter market behavior. As valuations decline, agents transition from buying to selling, triggering sharp changes in order flow [55, 56]. Figure 14(b) shows that the sell-to-buy ratio nearly doubles under rumor exposure, rising from 0.495 to 0.997. This dramatic increase reflects panic-driven responses to uncertainty and exemplifies how misinformation can amplify volatility in financial systems.
Market Turbulence. The cumulative effect of belief erosion and reactive trading destabilizes the broader market. Figure 14(c) shows that while prices remain stable in the control setting, rumor-exposed markets experience a pronounced decline. This reflects overreaction and herding behavior under uncertainty [57]. As negative sentiment spreads, confidence collapses, triggering self-reinforcing downturns that destabilize asset values and contribute to systemic fragility.
6 Ablation Study
To assess the contributions of TwinMarket's core design elements, conduct ablation study comparing the full model against two variants: (1) w/o BDI, which removes the sefie--desir-intentie module, making agents purely reactive; and (2) w/o Hetero., which eliminates agent heterogeneity by assigning uniform strategies and biases. As shown in Table 5, removing either component degrades performance across predictive accuracy (RMSE, MAE, correlation) and financial realism (kurtosis, leverage effect, GARCH parameters).
Table 5: Ablation study on the effects of the BDI framework and agent heterogeneity. t: higher better, 4: lower is better. Corr., RMSE, and MAE are computed on the daily normalized index price series between simulated outcomes and real-world data.
Model Variant
RMSE
MAE
Corr."
Kurtosis 1
Lev. Effect t
GARCH a
GARCH 1
TainMtrket
0.02
0.02
0.77
5.24
0.11
0.06
0.83
TwinMarket w/o BDI
0.07
0.05
0.34
4.25
0.05
0.03
0.76
TwinMarket w/o Hetero.
0.09
0.08
-0.61
3.58
0.13
0.05
0.85
The BDI ablation notably weakens the model's ability to generate stylized facts, such as fat tails and asymmetric volatility, highlighting the importance of cognitive reasoning in agent behavior. In contrast, removing heterogeneity leads to a sharper collapse in realism, with Gaussian returns and negative correlation, underscoring the necessity of strategic diversity for emergent market complexity. Together, these results confirm that both BDI framework and agent heterogeneity are essential for replicating real-world market dynamics. Additional ablation studies appear in Appendix B.2. 'On average, rupor-exposed agents assigned asset valuations 27.5% lower than those in the baseline.

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7 Scalability
To assess the potential of data-driven scaling as performance enhancement strategy, we investigate how varying levels of agent activation affect TwinMarket's predictive performance. We simulate markets with 10%, 20%, 40%, and 80% of agents actively trading each day, while keeping other conditions fixed.
Average Daily Volume Daily
RMSE
1.00
1.04
Simulated Index Price
Price Index (Normal
1.02
Real lindex
0.04
1.00
Index Error
Volume
1.0
0.03
0.5
0.92
0.01
0.90
2023/8155
202381/11
20238775
20238801
202388835
30238891
2023/99135
20:3/600
20:3/t/0
10%20%
80%
Activated Users in Daily Simulation
Date
Figure 15:
Scaling law: Performance x Volume.
Figure 16: Simulation result of 1,000 agents.
As illustrated in Figure 15, increased agent activation yields not only enhanced trading volume but, more critically, monotonic reduction in predictive error metrics (RMSE and MAE), revealing robust scaling relationship between market participation and model fidelity. This empirical finding aligns with theoretical predictions from collective intelligence literature that expanded agent populations engender richer information aggregation environments wherein price discovery mechanisms become increasingly robust.
Figure 16 provides further empirical validation of this scaling principle, demonstrating that TwinMar- ket maintains both behavioral realism and computational tractability when scaled to 1,000 agents over extended temporal horizons. Collectively, these results show that sufficiently large-scale micro-level agent interactions can generate macro-level market phenomena that exhibit the statistical regularities and emergent properties characteristic of empirical financial systems.
8 Conclusion and Future Work
In this paper, we investigate social emergence phenomena through LLM-driven agent methods. We introduce TwinMarket, novel multi-agent framework that leverages LLMs to simulate investor behavior stock market environment. Our findings demonstrate that LLMs can effectively model real-world behaviors, validate behavioral theories, and reveal underlying mechanisms of social emergence, contributing to deeper understanding of complex human systems.
The implications of this work also extend to the machine learning community. Methodologically, we demonstrate that BDI-structured LLM agents can achieve higher behavioral fidelity than traditional models, offering a blueprint for more realistic multi-agent systems. Furthermore, the framework serves as a powerful engine for generating high-fidelity synthetic data, valuable for training and evaluating downstream models, such as reinforcement learning agents.
Building these contributions, realism. We will address the limitations of the current study, which focuses on the unique context of the China A-share market and employs simplified mechanies such single daily call auction and a zero-sum environment. Future research will involve extending the simulation to diverse market structures and incorporating more complex, continuous trading mechanisms. Such enhancements will facilitate deeper investigations into the intrinsic properties of agent trust, the fundamental parallels between LLM and human cognition, and the broader implications for social science and role-playing applications, further paving the way for studying the alignment of LLMs with humans beyond value-based considerations.
future work will aim to enhance the framework's scope and
'Total 1,000 agents remain socially interactive; only subset engage in trading. The experiments are conducted from June 15 to July 15, 2023.
10

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Ethics Statement
All data used in this study were acquired through lawful and ethical means, in accordance with the Fair Use principle. To protect individual privacy, no personally identifiable information was involved in the research process, and all data underwent full anonymization prior to analysis. The simulation results and models presented are intended strictly for academic use and should not be used for real-works trading or financial decision-maie makinng.
Impact Statement
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
Acknowledgement
This work was supported by the National Natural Science Foundation of China(No. 72495151), the Major Program of the National Fund of Philosophy and Social Science of China (No. 19ZDA Shenzhen Science and Technology Program (JCYJ202208) 18 103001002), the Shenzhen Doctoral Startup Funding (RCBS2022 (0000922000000333300))))) the Tianyuan Fund for Mathematics of the National Natural Science Foundation of China (NSFC) (12326608), the Shenzhen Key Laboratory of Cross- Modal Cognitive Computing (Grant No. ZDSYS202306200) (300009)1100)) Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence (2023B 0023B1212010001 and the Shenzhen Stability Science Program 2023.
References
[1] Zahra Abbasiantacb, Yifci Yuan, Evangelos Kanoulas, and Mohammad Aliannejadi. Let the Iims talk: Simulating human-to- to-natt conversational qa via zero-shot I1m-to-lim interactions, 2023.
[2] Wenyue Hua, Ollie Liu, Lingyao Li, Alfonso Amayuelas, Julie Chen, Lucas Jiang, Mingyu Jin, Lizhou Fan, Fei Sun, William Wang, Xintong Wang, and Yongfeng Zhang. Game-theoretic IIm: Agent workflow for negotiation games, 2024.
[3] Chaoran Chen, Bingsheng Yao, Yanfang Ye, Dakuo Wang, and Toby Jia-Jun Li. Evaluating the Ilm agents for simulating humanoid behavior. In Proc, of the 1st Workshop on Cluman-Centedd Evaluation and Auditing of Language Models (CHI Workshop HEAL), 2024.
[4] Yuzhe Yang. Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, Jimin Huang, Honghai Yu, and Benyou Wang. UCFE: user-centric financial expertise benchmark for large language models. In Luis Chiruzzo, Alan Ritter, and Lu Wang, editors, Findings of the Association for Computational Linguistics: NAACL 2025, pages 5429-5448, Albuquerque, New Mexico, April 2025. Association for Computational Linguistics.
[5] Chengxing Xie, Canyu Chen, Feiran Jia, Ziyu Ye, Shiyang Lai, Kai Shu, Jindong Gu, Adel Bibi, Ziniu Hu, David Jurgens, James Evans, Philip Torr, Bernard Ghanem, and Guohao Li. Can large language model agents simulate human trust behavior?, 2024.
[6] Qianqian Xie, Dong Li, Mengxi Xiao, Zihao Jiang, Ruoyu Xiang, Xiao Zhang, Zhengyu Chen, Yueru He, Weiguang Han, Yuzhe Yang, al. Open-finllms: Open multimodal large language models for financial applications. arXiv preprint arXiv:2408. 11878, 2024.
[7] Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, and Xing Xie. Large language models understand and can be enhanced by emotional stimuli, 2023.
[8] Matthew Renze and Erhan Guven. Self-reflection in Ilm agents: Effects on problem-solving performance, 2024.

---

[9] Jason Wei, Xuezhi Wang. Dale Schuurmans. Maarten Bosma, Brian Ichter. Fei Xia, Ed Chi, Quoe Le, and Denny Zhou. Chain-offthougee prompting elicits reasoning in large language models, 2023.
[10] Zihao Li, Xu Wang, Yuzhe Yang, Ziyu Yao, Haoyi Xiong, and Mengnan Du. Feature extraction and steering for enhanced of reasoning in language models, 2025.
[11] Tommaso Ciarli, André Lorentz, Maria Savona, and Marco Valente. The effect of consumption and production structure on growth and distribution. a micro to macro model. Metroeconomica, 61(1):180-218, 2010.
[12] David Baqaee and Elisa Rubbo. Micro propagation and macro aggregation. Annual Review of Economics, 15(1):11-123, 2023.
[13] Robin Matthews. The people and landscape model (palm): Towards full integration of human decision-making and biophysical simulation models. Ecological modelling, 194(4):329-35) -23, 2006.
[14] Yuning Wang, Jinhao Li, Tianqi Ke, Zehong Ke, Junkai Jiang, Shaobing Xu, and Jianqiang Wang. A homogeneous multi-vehicle cooperative group decision-making method in complicated mixed traffic scenarios. Transportation Research Part C: Emerging Technologies, 167:104833 2024.
[15] Max Weber. Economy and society: An outline of interpretive sociology, volume 1. University of California press, 1978.
[16] Emile Durkheim. The rules of sociological method. In Social theory re-wired, pages 9-14. Routledge, 2023.
[17] G Gu. The dynamic interplay of market forces and human behavior: A critical review of efficient market hypothesis and behavioral finance. Advances in Economics, Management and Political Sciences, 31(1):54-59, 2023.
[18] R Kiruthika and S Ramya. Cognitive bias factors influencing investors investment decisions in behavioural finance perception. Journal of Research Administration, 5:2809-25, 2023.
[19] Anand S Rao, Michael Georgeff, et al. Bdi agents: from theory to practice. lemas, volume 95, pages 312-319, 1995.
[20] Robert L. Axtell and J Doyne Farmer. Agent-based modeling in economics and finance: Past, present, and future. Journal of Economic Literature, pages 1-101, 2022.
[21] Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, and Xing Xie. Competeai: Understanding the competition dynamics in large language model-based agents, 2024.
[22] Nian Li, Chen Gao, Mingyu Li, Yong Li, and Qingmin Liao. Econagent: large language empoweed agents for simulating macroeconomic activities. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), pages 55523-15536, 2024.
[23] Shen Gao, Yuntao Wen, Minghang Zhu, Jianing Wei, Yuhan Cheng, Qunzi Zhang, and Shuo Shang. Simulating financial market via large language model based agents. arXiv preprint arXiv:2406. 199666 2024.
[24] Ziyi Yang, Zaibin Zhang, Zirui Zheng, Yuxian Jiang, Ziyue Gan, Zhiyu Wang, Zijian Ling, Jinsong Chen, Martz Ma, Bowen Dong, et al. Oasis: Open agents social interaction simulations on one million agents. arXiv preprint arXiv:2411. 1.15811, 2024.
[25] Anastassia Fedyk, Ali Kakhbod, Peiyao Li, and Ulrike Malmendier. Chatgpt and perception biases in investments: An experimental study. Available at SSRN 4787249, 2024.
[26] Harry Markowitz. Foundations of portfolio theory. Harry Markowitz: Selected Works, S, pages 481 480, 1990.
12

---

[27] Daniel Kahneman and Amos Tversky. Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part pages 99-127. World Scientific, 2013.
[28] Hersh Shefrin and Meir Statman. Behavioral portfolio theory, Journal of financial and quantitative analysis, 1272)1277151,, 2000.
[29] Pengfei Sui and Baolian Wang. Stakes and investor behaviors. Available at SSRN 4219861, 2023.
[30] Yashen Wang, Dongyang Li, Jianshan Sun, Yuan Kun, Yinan Jiang. Yin Zhang, and Jie Cao. Combining macro and micro: fcature-driven dynamic graph learning for social media popularity prediction. World Wide Web, 28(1):18, 2025.
[31] Paolo Colla and Antonio Mele. Information linkages and correlated trading. The Review of Financial Studies, 23(1):203-22) 2010.
[32] Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, and Liefeng Bo. Multi-behavion graph neural networks for recommender system. IEEE Transactions on Neural Networks and Learning Systems, 35(4):5473-5487, 2022.
[33] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285-295, 2001.
[34] Dong Quang Dang, Weiou Wu, and Ioannis Korkos. Stock market and inequality distributions- evidence from the brics and g7 countries. International Review of Economics & Finance, 92:1 1711112111119 119, 2024.
[35] Hongqi Liu, Cameron Peng. Wei A Xiong, and Wei Xiong. Taming the bias 70o. Journal of Financial Economics, 143(2);716774,, 2022.
[36] Rama Cont. Empirical properties of asset returns: stylized facts and statistical issues. Quantita- live finance, 1(2):223, 2001.
[37] Markus Haas and Christian Pigorsch. Financial economics, fat-tailed distributions. Encyclopedia of Complexity and Systems Science, 4(1):3404-3435, 2009.
[38] Jean-Philippe Bouchaud, Andrew Matacz, and Mare Potters. Leverage effect in financial markets: The retarded volatility model. Physical review letters, 87(22):228701, 2001.
[39] Guillermo Llorente, Roni Michaely, Gideon Saar, and Jiang Wang. Dynamic volume-return
relation individual stocks. The Review of financial studies, 15(4):1005-1047, 2002.
[40] Thomas Lux and Michele Marchesi. Volatility clustering in financial markets: a microsimulation of interacting agents. International journal of theoretical and applied finance, 304)):675-702 2000.
[41] Reiner Franke and Frank Westerhoff. Structural stochastic volatility in asset pricing dynamics: Estimation and model contest. Journal of Economic Dynamics and Control, (668))1113311)) 2012.
[42] Shu-Heng Chen and Umberto Gostoli. Behavioral macroeconomics and agent-based macroeco- nomics. In Distributed Computing and Artificial Intelligence, 11th International Conference, pages 47-54. Springer, 2014.
[43] Christian Francq and Jean-Michel Zakoian. GARCH models: structure, statistical inference and financial applications. John Wiley & Sons, 2019.
[44] Robert K Merton. The self-fulfiling prophecy. The antioch review, 8(2):1932110, 1948.
[45] Robert Shiller. Irrational exuberance: Revised and expanded third edition. 2015.
[46] Gilat Levy and Ronny Razin. Echo chambers and their effects on economic and political outcomes. Annual Review of Economics, 11(1):303-328, 2019.
13

---

[47] J Anthony Cookson, Joseph E Engelberg, and William Mullins. Echo chambers. The Review of Financial Studies, 36(2):450-50 2023.
[48] Thomas W Valente and Patchareeya Pumpuang. Identifying opinion leaders to promote behavior change. Health education & behavior, 34(6):881-898 2007.
[49] Alessandro Bessi, Fabio Petroni, Michela Del Vicario, Fabiana Zollo, Aris Anagnostopoulos, Antonio Scala, Guido Caldarelli, and Walter Quattrociocchi. Homophily and polarization the age of misinformation. The European physical journal special topics, 225:2047-2059, 2016.
[50] Katharina Esau, Tariq Choucair, Samantha Vilkins, Sebastian FK Svegaard, Axel Bruns, Kate O'Connor-Farfan, and Carly Lubicz-Zaorski. Destructive polarization in digital communication contexts: critical review and conceptual framework. Information, Communication & Society, pages 1-22, 2024.
[51] Samuel C Rhodes. Filter bubbles, echo chambers, and fake news: How social media conditions individuals to be less critical of political misinformation. Political Communication, 39(1):1-22 2022.
[52] Jinghua Piao, Zhihong Lu, Chen Gao, Fengli Xu, Fernando P Santos, Yong Li, and James Evans. Emergence of human-like polarization among large language model agents. arXiv preprint arXiv:2501.0517/, 2025.
[53] Soroush Vosoughi. Deb Roy, and Sinan Aral. The spread of true and false news online. science, 19966380):1146-1135 2018.
[54] Kathrin Eismann. Diffusion and persistence of false rumors in social media networks: impli- cations of searchability on rumor self-correction on twitter. Journal of Business Economics, 91(9):1299-11 13299, 2021.
[55] Kenneth R Ahern and Denis Sosyura. Rumor has it: Sensationalism in financial media. The Review of Financial Studies, 28(7):2050-20) 2005,, 2015.
[56] Hua Zhang, Yuanzhu Chen, Wei Rong, Jun Wang, and Jinghua Tan. Effect of social media rumors on stock market volatility: case of data mining in china. Frontiers Physics, 10:987799, 2022.
[57] Hassan Afrouzi, Spencer Y Kwon, Augustin Landier, Yueran Ma, and David Thesmar. Overre- action in expectations: Evidence and theory. The Quarterly Journal of Economics, 138(3):1713- 1764, 2023.
[58] Eric Bonabcau. Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the national academy of sciences, 99(suppl_3):7280-222 3):7280-77287 2002.
[59] Satabdi Basu, Pratim Sengupta, and Gautam Biswas. A scaffolding framework to support learning of emergent phenomena using multi-agent-based simulation environments. Research in Science Education, 45:293-324, 2015.
[60] Richard G Palmer, W Brian Arthur, John H Holland, and Blake LeBaron. An artificial stock market. Artificial Life and Robotics, 3(1):27-31, 1999.
[61] Mark Abdollahian, Zining Yang, Travis Coan, and Birol Yesilada. Human development dynamics: agent based simulation of macro social systems and individual heterogeneous evolutionary games. Complex Adaptive Systems Modeling, 1:1-17, 2013.
[62] Andrea Gaunersdorfer and Cars Hommes. A nonlinear structural model for volatility clustering. In Long memory in economies, pages 265-288 Springer, 2007.
[63] Reiner Franke and Frank Westerhoff. Why simple herding model may generate the stylized facts of daily returns: explanation and estimation. Journal of Economic Interaction and Coordination, 11:1-34, 2016.
[64] Christophre Georges and Javier Pereira. Market stability with machine learning agents. Journal of Economic Dynamics and Control, 10402, 2021.
14

---

[65] Joon Sung Park, Joseph O' O'Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael Bernstein. Generative agents: Interactive simulacra of human behavior. In Proceed- ings of the 36th annual acm symposium on user interface software and technology, pages 1-22, 2023.
[66] Jessica Reale. Interbank decisions and margins of stability: an agent-based based stock- consistent approach. Journal of Economic Dynamics and Control, 160: 104822, 2024.
[67] Yangyang Yu, Zhiyuan Yao, Haohang Li, Zhiyang Deng. Yupeng Cao, Zhi Chen, Jordan W Suchow, Rong Liu, Zhenyu Cui. Zhaozhuo Xu, al. Fincon: A synthesized Ilm multi-agent system with conceptual verbal reinforcement for enhanced financial decision making, arXiv preprint arXiv:2407.066: 065677 2024.
[68] Chengxing Xie, Canyu Chen, Feiran Jia, Ziyu Ye, Shiyang Lai, Kai Shu, Jindong Gu, Adel Bibi, Ziniu Hu, David Jurgens, et Can large language model agents simulate human trust behavior? Advances in neural information processing systems, 37:15674-15729 2024.
[69] Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin. Le Wu, et al. Trendsim: Simulating trending topics in social media under poisoning attacks with Ilm-based multi-agent system. arXiv preprint arXiv:2412.12166 2024.
[70] Jinghua Piao, Yuwei Yan, Jun Zhang, Nian Li, Junbo Yan, Xiaochong Lan, Zhihong Lu, Zhiheng Zheng, Jing Yi Wang, Di Zhou, et al. Agentsociety: Large-scale simulation of I1m- driven generative agents advances understanding of human behaviors and society, arXiv preprint arXiv:2502.08691, 2025.
[71] Xinnong Zhang. Jiayu Lin, Xinyi Mou, Shiyue Yang, Xiawei Liu, Libo Sun, Hanjia Lyu, Yihang Yang, Weihong Qi, Yue Chen, et al. Socioverse: A world model for social simulation powered by Ilm agents and a pool of 10 million real-world users. arXiv preprint arXiv:2504.1011 2025.
[72] Jiakai Tang, Heyang Gao, Xuchen Pan, Lei Wang, Haoran Tan, Dawei Gao, Yushuo Chen, Xu Chen, Yankai Lin, Yaliang Li, et al. Gensim: A general social simulation platform with large language model based agents, arXiv preprint arXiv:2410.04360) 2024.
[73] Andrea L Eisfeldt and Gregor Schubert. Ai and finance. Technical report, National Bureau of Economic Research, 2024.
[74] Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. Chatgpt outperforms crowd workers for text-annotation tasks. Proceedings of the National Academy of Sciences, (20030))e20050111200 2023.
[75] Yun-Shiuan Chuang, Agam Goyal, Nikunj Harlalka, Siddharth Suresh, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, and Timothy T Rogers. Simulating opinion dynamics with networks of llm-based agents. arXiv preprint arXiv:2311.09615 2023.
[76] Edan Toledo, Karen Hambardzumyan, Martin Josifoski, Rishi Hazra, Nicolas Baldwin, Alexis Audran-Reiss, Michael Kuchnik, Despoina Magka, Minqi Jiang, Alisia Maria Lupidi, et al. Ai research agents for machine learning: Search, exploration, and generalization in mle-bench. arXiv preprint arXin:2507.02552: 2025.
[77] Xu Yang, Xiao Yang, Shikai Fang, Bowen Xian, Yuante Li, Jian Wang, Minrui Xu, Haoran Pan, Xinpeng Hong, Weiqing Liu, et al. R&d-agent: Automating data-driven ai solution building through loweeed automated research, development, and evolution. arXiv preprint 2rXiv 14738. 2025.
[78] Yue Huang, Zhengqing Yuan, Yujun Zhou, Kehan Guo, Xiangqi Wang, Haomin Zhuang, Weixiang Sun, Lichao Sun, Jindong Wang, Yanfang Ye, et al. Social science meets llms: How reliable are large language models in social simulations? arXiv preprint arXiv:2410.23426, 2024.
[79] Blake LeBaron. Agent-based computational finance. Handbook of computational economics, 2:1187-1233, 2006.
15

---

[80] Leigh Tesfatsion. Agent-based computational economics: A constructive approach to economic theory. Handbook of computational economics, 2:831-880, 2006.
[81] Annika Bergström and Maria Jervelycke Belfrage, News social media: Incidental consump- tion and the role of opinion leaders. Digital journalism, 6(5):583-598, 2018.
[82] Charles M Jones, Donghui Shi, Xiaoyan Zhang, and Xinran Zhang. Retail trading and return predictability in china. Journal of Financial and Quantitative Analysis, pages 1-37, 2023.
[83] Lin Tan, Xiaoyan Zhang, and Xinran Zhang. Retail and institutional investor trading behaviors: evidence from china. Annual Review of Financial Economics, 16, 2023.
[84] William F Sharpe. Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance, 19(3):425-444 1964.
[85] Hersh Shefrin and Meir Statman. The disposition to sell winners too early and ride losers too long: Theory and evidence. The Journal of finance, 40(3):777-790, 1985.
[86] Nicholas Barberis and Ming Huang. Stocks as lotteries: The implications of probability weighting for security prices. American Economic Review, 98(5):2066 2100, 2008
[87] Alok Kumar. Who gambles in the stock market? The journal of finance, 64(4):188-11933, 2009.
[88] Gur Huberman. Familiarity breeds investment. The Review of Financial Studies, 14(3):659-665 -80, 2001.
[89] Brad M Barber and Terrance Odean. Trading hazardous to your wealth: The common stock investment performance of individual investors. The journal of Finunce, 55(2):773-806, 2000.
[90] Brad M Barber and Terrance Odean. Boys will be boys: Gender, overconfidence, and common stock investment. The quarterly journal of economics, 116(1):261-29) 2001.
[91] Lei Feng and Mark $ Seasholes. Do investor sophistication and trading experience eliminate behavioral biases in financial markets? Review of finance, 933):305-351, 2005.
[92] Yi- Yi-Cheng Chen, Lin Hui, and Tipajin Thaipisutikul. collaborative filtering recommendation system with dynamic time decay. The Journal of Supercomputing. 77:244-262, 2021.
[93] Hao Gao, Yongqing Wang, Jiangli Shao, Huawei Shen, and Xueqi Cheng. User identity linkage across social networks with the enhancement of knowledge graph and time decay function. Entropy, 24(1) I): 1603, 2022.
[94] Lei Guo and Yiyan Zhang. Information flow within and across online media platforms: agenda-setting analysis of rumor diffusion on news websites, weibo, and wechat china. Journalism Studies, 21(15):116-2295, 2020.
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Content of Appendix
Related Work
25
B More Experimental Analysis
25
B.1 Social Network Sensitive Analysis
25
B.2 More Ablation Study
25
B.3 Reproducibility and Stability Validation
28
B.4 Emergence of Opinion Leaders
28
B.5 Qualitative Validation of Micro-Level Behaviors
29
C Technical Details I: Data Statistics
30
C.1 Stock Data Details
................................
C.2 Transaction Details
33
................................
C,3 Information Source Details
34
Technical Details II: User Profile Initialization
34
D.1 Foundations: Heterogeneous Investors in Financial Markets
35
D.2 System Prompt Generation
35
D.3 Initialization of Trading Records and Beliefs
37
E
Technical Details III: TwinMarket Framework Mechanism
38
E.1
Agent Belief Structure and Representation
38
Formal Definition of the BDI Framework
................................
E.3
Social Interaction Dynamics
40
E,4
Simulation of Misinformation: Rumor Injection
41
E.5
Order-Driven Trading System Design
E,6
Management of Potential Data Leaks
43
F
Prompt
44
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A Related Work
Agent-Based Models. Agent-based models (ABMs) provide powerful framework for studying collective behavior and emergent phenomena in complex systems [58, 59]. By simulating agent interactions, ABMs capture macro-level patterns such as market fluctuations and social dynamics [60, 61, 20J. They encode key behavioral traits as fundamentalist and chartist trading, herding tendencies, and decision-make
heterogeneity- simplified rules like strategy switching and structural stochastic volatility (SSV) [36, 41J. This abstraction effectively reproduces financial market stylized facts, including fat-tailed return distributions and volatility clustering [62, 63]. Recent advances integrate machine learning, enhancing ABMs' realism and predictive power [64-66].
LLMs for Behavioral Simulation. Large language models (LLMs) excel at simulating complex human behaviors, from rational decision-make and financial market analysis replicating behav- ioral biases like herding and overconfidence [3, 67, 25, 24]. Beyond behavioral simulation, they enable fine-grained modeling of individual actions and large-scale emergent phenomena [1, 2, 68). For instance, L.LM-based multi-agent systems like TrendSim have been developed to simulate specific social media dynamies such as the spread of trending topics and the impact of poisoning attacks, incorporating features like time-aware interactions and human-like agent profiles with memory and perception modules [69]. The capacity for large-scale simulation further demonstrated by platforms such as AgentSociety, which employs LLM-driven agents to investigate complex social issues like polarization and the effects of universal basic income by modeling agent minds with emotions, needs, and cognition [70]. Similarly, Socio Verse aims to create world models for social simulation by leveraging pools of millions of real-world user profiles to align simulations in politics, news, and economics with reality [71]. General-purpose platforms like GenSim are also emerging, support- ing simulations with multi-agents and incorporating error-correction mechanisms to enhance the robustness and adaptability of customized social scenarios [72]. LLMs naturally exhibit demographic diversity, such as gender, age, and education, while engaging in rich social interactions [25, 73). By automating text analysis, generating experimental stimuli, and designing multi-agent systems, they offer powerful tools for social science research [4, 74, 75J. Moreover, LLMs demonstrate capabilities in automating iterative research and development processes, suggesting potential for self-improving simulation frameworks [76, 77]. Crucially, the reliability and consistency of these --asedd simu- lations active areas of research, leading the development of specialized datasets and methods to systematically evaluate and improve how LLMs embody simulated roles and maintain persona fidelity [78]. The application of such L.---oeereed agents financial market simulation, as explored in work, promises to capture more nuanced trader behaviors and market dynamics.
B More Experimental Analysis
B.1 Social Network Sensitive Analysis
To assess the robustness of our social network construction, we conduct sensitivity analysis by varying two key parameters: the similarity threshold and the time decay factor. All tests are performed on social network with 100 users.
We first vary the similarity threshold while fixing the time decay factor at 0.5. A lower threshold allows weakly aligned users to connect, resulting in denser graphs with higher noise. A higher threshold filters out weaker ties, making the graph sparser but more locally cohesive. We then vary the time decay factor À while fixing the similarity threshold at 0.2. Smaller values preserve long-term behavioral influence. while larger values emphasize recent activity. As shown in Figure 17, higher parameter values generally lead to sparser, less modular, and less clustered graphs. The size of the largest connected component remains stable across different settings. We find that using a similarity threshold of 0.2 and a decay factor of 0.5 maintains moderate connectivity and avoids over-saturation, supporting meaningful information propagation.
B.2
More Ablation Study
To assess the robustness of our TwinMarket framework and understand the impact of various com- ponents and parameters, we conducted several other ablation studies. This section details these investigations.
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1.0
Aug Clustariig
Th LCCCC
Demiity
1025T8
2012711
2020
200
2024 203
Cacp NATE
encpp122
Avg Cumoaring
Mcdutaartyy
Date
Date
Date
Date
Figure 17: Sensitivity analysis of the graph structure under different similarity thresholds (top) and time decay factors (bottom). Higher parameter values generally lead sparser, less modular, and less clustered graphs. The largest connected component remains stable across settings.
B.2.1 Impact LLM Temperature on Agent Behavior
The temperature parameter in LLMs con- trols the randomness and creativity of the generated text. A lower temperature results in more deterministic and focused outputs, while a higher temperature leads to more di- verse and potentially surprising responses.
Table 6: Ablation LLM Temperature
Temp.
Sells
Buys
Avg. Volume
0.3
2369
3581
4818.31
0.7
2402
3583
4794.72
1.0
2403
3593
5580.91
To evaluate how this parameter influences agent behavior and market dynamics within TwinMarket, we ran simulations with varying temperature settings for the LLM powering our agents.
1.3 (our setting)
2374
3586
6071.78
The results are summarized in Table 6. It is shown that core trading decisions (total "Sells" and "Buys") remain remarkably stable across various temperature settings while the "Avg. Volume" per transaction generally increases with rising temperatures. This suggests that while the decision to trade remains stable, higher temperatures, which encourage more diverse LLM reasoning, might lead to agents expressing decisions with greater perceived conviction scale, thus increasing average transaction sizes. Despite LLM generation parameters affecting textual outputs, our simulation framework demonstrates stable core decision-making under reasonable parameter variations. This validates that the observed emergent behavior stems from spontaneous dynamics and the framework's superiority, without posing information leakage risks.
B.2.2 Sector-Specific Dynamics: Simulation Across Different Industries
To capture the heterogeneous nature of real-world financial markets, TwinMarket simulates market dynamics across 10 aggregated industry indices (detailed Appendix C.I.1). This ablation study assesses whether our framework generates distinct and plausible dynamics for different sectors. We analyzed key statistical properties of their simulated returns, focusing on characteristics indicative of real-world financial time series, such as fat tails (kurtosis) and volatility clustering (GARCH parameters a and ().
Table 7: Ablation Industries
Industry (Index)
Kurtosis
GARCH-a
GARCH-3
SSE 50
5.24
0.06
0.83
Consumer Goods Index (CGEI)
3.06
0.05
0.71
Technology and Telecommunications Index (TTEI)
6.53
0.09
0.96
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The results are summarized in Table 7. The simulated differences more volatile and fat-tailed tech sector (TTEI) versus a stabler consumer goods sector (CGEI) align with common real- world observations and expectations. This correspondence demonstrates TainMarket's capabiliit to generate differentiated and plausible dynamies across various simulated industries, supporting the framework's s applicability for studying multi-asset markets where sector-specific behaviors are important.
B.2.3 Impact of Social Interaction on Agent Decision-Making
To assess the influence of real-time social interactions versus historical data agent decision -akinng we We compared the performance of the full TwinMarket model with a variant where agent social interactions (the social meida platform) were disabled. Both simulations ran from 2023-06-15 to 2023-08-15 with 100 agents.
Table 8: Ablation on Social Interaction
Model
RMSE
MAE
Correlation
TwinMarket
0.02
0.02
0.77
TwinMarket w/o Interaction
0.18
0,07
-0.50
The results, presented in Table 8, demonstrate a significant degradation in performance when social interactions are removed. This suggests that agents in our simulation prioritize and are heavily influenced by real-time social interactions and emergent group dynamics rather than solely relying on historical data. This finding also indicates that potential data leakage from historical news or announcements has negligible impact our primary results, the dynamic social component plays a more dominant role in shaping agent decisions and overall market outcomes.
B.2.4 Granular Analysis of the BDI Framework
To provide a more granular analysis of the we conducted a targeted ablation study on its core cognitive components. We evaluated two additional variants against the full model:
(BDI) framework's 's contribution.
TwinMarket w/o Belief Update: In this variant, agents maintain their initial belief state throughout the simulation. The dynamic belief adaptation mechanism is disabled, preventing agents from updating their views based on new market social information.
TwinMarket w/o Desire: Here, agents skip the proactive information-sseking step. They do not generate queries for external information and make decisions based solely on their pre-existing knowledge and past experiences.
We conducted month-long simulation from 2023-06-15 to 2023-07-15 with 100 agents powered by GPT-4o. The results, summarized in Table 9, show that removing either component leads to significant degradation in predictive accuracy. The performance drop in the 'w/o Belief Update" variant highlights the necessity of dynamic adaptation for capturing market realism. Similarly, the weaker performance of the 'w/o Desire' variant underscores the importance of goal-driven information-seekle seeking behavior. These findings confirm that both the adaptive belief mechanism and the proactive desire component are indispensable for the framework's 's ability to replicate complex market dynamics.
Table 9: Granular ablation study on the core components of the BDI framework.
Model Variant
RMSE 1
MAE J
TwinMarket (Full BDI)
0.0158
0.0143
TwinMarket w/o Belief Update
0,0342
0.0298
TwinMarket w/o Desire
0.0532
0.0443
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B.3 Reproducibility and Stability Validation
To evaluate the reproducibility of emergent phenomena and the stability of the simulation framework under varied conditions, we performed multiple simulation runs (3 iterations each) for a consistent period from June 15th to August 15th, 2023, with two different models as the backbone for agent modeling.
Table 10: Reproducibility and Stability: Comparison of Aggregated Simulation Metrics Across Different Foundation Models. Data are reported as mean + relative standard deviation (RSD), with RSDs shown as percentages (i.e., mean + RSD%).
Rackbone Model
RMSE
MAR
Kurtosks
Lev. Effect
GARCH-o
GARCH-3
GPT-4o (3 runs)
0.023 = 31.13%
0.026 1 36 36%
1.133 = 20.85%
0.060 + 13.01%
0.017 # 63. 63.17% 0.032 1 61.6%
0.780 + 27 11% 0.897 1 13.26%
Gemini- 1.5.Flash runs)
0.024 16.34%
0.020
21.12%
4.886 8.25%
0.108 16.52%
TwinMarket (1 run)
0.020
(1.020
5.24
0.11
0.06
0.83
Table 10 presents a comparison of key performance and market characteristic metrics across the multiple runs with different LLM backbones, against our established TwinMarket framework. The quantitative analysis demonstrates that TwinMarket simulations, irrespective of the specific advanced LLM used, reliably capture key aspects of real-world market dynamics. The low error metrics (RMSE & MAE)) indicate strong correspondence between simulated and historical price movements.
Notably, our experiments consistently demonstrate that crucial market phenomena, such fat-tailed return distributions (indicated by Kurtosis values significantly greater than 3), volatility clustering (evidenced by the GARCH and 3 parameters where a > the leverage effect emerge reliably across all simulation runs and with both LLM backbones.
0, and 3 is close to 1), and
While the specific parameter values for these stylized facts exhibit some variability across runs and between models, the qualitative presence and general characteristics these stylized facts remain stable. This observation is consistent with real financial markets, where core statistical patterns persist despite short-term fluctuations or differences in the precise estimation of individual metrics. The observed variability in parameters like GARCH coefficients is expected, reflecting the inherent stochasticity present in complex financial systems and in the LLM's generative process. As highlighted in prior literature [79, 80), the primary validation focuses whether the model can consistently reproduce the qualitative stylized facts and emergent behaviors across simulations, rather than demanding numerical stability of every single estimated parameter to an exact value. Our results affirm this capability for TwinMarket.
B.4 Emergence of Opinion Leaders
This section provides additional validation for TwinMarket by demonstrating its capability to simulate the emergence of opinion leaders, a key phenomenon in social networks(81], [81]. This emergence is driven by the platform's information aggregation mechanism and the dynamics of social interaction, where engaging content gains visibility and influences user behavior.
Within TwinMarket, posts that resonate strongly with the broader community achieve wider dissemi- nation. As users interact with content and potentially imitate the behaviors or adopt the perspectives of influential posters, their social connections can become more centralized around these key individuals. This reif-reinn process naturally leads to the emergence of opinion cedders-user whose posts and opinions exert disproportionately larger impact on the network.
As illustrated in Figure 18a, users who achieve high-degree centrality effectively gain influence as their posts receive increased engagement, leading to broader information diffusion. This effect is further quantified in Figure 18b, which shows that a small subset of users experiences significantly higher repost counts over time. This skewed distribution of engagement reinforces their role as key opinion leaders. Their ability to shape market sentiment and drive collective decision-making underscores the impact of social influence in financial markets, where information cascades can significantly alter trading behaviors and reshape network structures, which aligns with observed real-world social dynamics and further validates its design.
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Repost Trends for All Users
Opinaion Leaders
250
200
Count
100
202347
203608
203-49
203.19
203-11
203-12
Time: 2023-6-22
Date
(a) Post-propagation chains in the social network. Nodes with high connectivity and wide reach emerge potential opinion leaders.
(b) Trend of repost counts the 100-user network. Some users show much greater reach, signaling emer- gent influence.
Figure 18: Illustration of information diffusion in TwinMarket. (a) Visualizes post- propagation paths; (b) shows how repost counts evolve over time, highlighting key opinion leaders.
B.5 Qualitative Validation of Miero-Level Behaviors
To validate the realism of individual agent behaviors within TwinMarket, this section presents qualitative examples of agent decision-making logs. These examples, generated through interactions guided by their initialized personas and BDI-driven cognitive process, demonstrate the agents' capacity to autonomously exhibit a range behaviors observed real-world investors.
B.5.1 Demonstration of Investment Analysis Approaches
TwinMarket agents capable of employing recognized investment analysis techniques, as discussed in Appendix D. As shown in Figure 19, an agent demonstrates Fundamental Analysis by con- sidering valuation metries, long-term outlook, and national strategy to justify increasing its position. Similarly, Figure 20 illustrates an agent engaging in Technical Analysis, focusing on volume-prics action, moving averages, and momentum make trading decisions, while also considering stop-loss levels for risk management.
Today's Trading Summary: Regarding TTEI (Technology and Telecom Indexttech growth stocks are currently at their lowest valuation in the pusi three yeurs, with PE ratio below 30. From fundamental perspective, this sector already holds certain investment value. Although the leading tech stocks lown such as SMIC and Haiguang Information. have been slightly affected by market sentiment in the term, long
remains posilive, especially given national strategy of promoting technological sell-reliance. These companies' growth potential is still promising, so inday decided to increase position to 35%.
(Chemical and Pharnaceutical Index), pharmacentical sector undergone some adjustments, valuations have to relatively reasonable range. My position this sector currently 30% and plan to continue holding and monitoring it. My total partfilli now at 65%, which is relatively conservative, with the remaining 35% in cash, ready to gradually incresse positions when better buying opportunities arise the market.
Figure 19: Example Fundamental Analysis.
B.5.2 Exhibition of Behavioral Biases
Beyond structured analytical approaches, TwinMarket agents also manifest common behavioral tendencies and socially influenced actions. The Disposition Effect is illustrated in Figure 21, with an agent intending sell winning position prematurely. Figure 22 showcases Lottery Preference, where agent favors high-risk, speculative investment despite its poor past performance. Under-
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Today's 's Trading Summary: Seeing recent posilive for tech stocks, I'm plarming focus Telecom Index), I already have this index in my portfolio,and the volume price action in the last few trading days looks solid. Notably, stocks like SMIC and Haiguang Information have broken through their 5-day moving averages, and think there's good chance this momentum will continue in the short term. I'm considering adding around
the TTEI (Technology and
position in the tech sector, aiming to raise my allocation to
That said, I need remind myself not to get too aggressive, other sectors in my portfolio have been performing mediocrely. The Real Estate Index (REET) has been weak lately and has hit my stop loss level. plan to exil and free up some capital to invest in the tech stocks.
Figure 20: Example of Technical Analysis.
diversification is evident in Figure 23, as an agent admits to concentrating on familiar assets while neglecting others.
Figure 24 provides an example of Overconfidence, where an agent displays excessive conviction in its trading strategy. Furthermore, Figure 25 captures instance of Herd Behavior, where an agent's 's trading decision is swayed by perceived widespread forum sentiment and low market volume, overriding its typical technical approach.
The central bank's 's interest rate definitely major positive, and market sentiment likely he ignited, which could lcad to a shori-term rebound. However, the overall market valuation is still relativoly high, so the upside potential for the rebound is limited. important be ready to take profits News and Annonncements Impact: The most important news today is the central bank's 's rate cut, which directly benefits the real estate and financial sectors. These two sectors should perform well in the short term. Other sectors should also be monitored to see there are any upportunities for cupital rolation.
Market Analysis:
right
Sector Index Analysis: FSEI (Financial Services Index): I'm currently up 5.6% on this index. The 10-day moving average is trending upwards, with the interest cut boost, should continue rise the short However, the increase may be too large, plan to partially roduce my position and lock in profits once it rises another 5%.
Figure 21: Examples of Disposition Effect.
Market Analysis:: The market is really volatile right now, and it feels like everyone is on the sidelines. The days of chasing after rallies or panicking during drops seem be over. Some traditional industries, infrasiuccure and energy, undervalued. opportunity there.
might be
the 5-day moving average been slightly picking up recently. It feels like it's about bounce back, I'm adding more! It's gamble, but who knows, maybe I'll bike into a motorcycle!
Sector Index Analysis: TSEI (Tourism Index): My lottery stock! Even though it's been
Figure 22: Examples of Lottery Preference.
C Technical Details I: Data Statistics
C.1 Stock Data Details
C.1.1 SSE 50 and 10 Aggregated Index
In our simulation process, we mainly focus on the SSE 50 index?, a benchmark tracking the 50 largest, most liquid A-share stocks on the Shanghai Stock Exchange, reflecting the performance of China's blue-chip market.
To provide a more granular view of sector-specific performance within the SSE 50, we introduce 10 aggregated indices. Each aggregated index, Jg. represents a distinct sector ie and comprises a subset of N₂ constituent stocks from the SSE 50. The value of each aggregated index is calculated as weighted average of the price relatives of its constituent stocks. The weights are determined by each stock's market capitalization at the start of the simulation (t = 0) and remain fixed throughout the simulation.
Mooe details about SSE 50 can be found in https: : http///www.seee .se.. com. www.//aaeee/ssennee/eeo..
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Market Analysis: The market currenlly period consolidation,, with the central bank's interest having significant impact on short-term market movements. The macreconomic recovery has heen slower than expected, and the economic oatlook for second half of the year
Overall, market sentiment quite cautious.
Sector Index Analysis: FSEI (Financial Services Index) has been declining recenly. From technical perspective, there may still be very
technical analysis, somewhat overdone. The main components of FSEI are banks, insurance companies, and brokerages, and their overall valuations are relatively low, especially bunking stocks. The current is likely at historical low. From a long-term perspective, there some positions. CPFI (Chemical and Pharmaceutical Index) is stock
further downside
short
my experience, recent drop
investment value. If continues fall, may consider adding rocommended by the system. has been fluctuating recently, and the technical outlook oppears unclear. I tend to focus more on the FSEI index, which I'm familiar with, ignore others.
Figure 23: Examples of Underdiversffication.
Today's Trading Summary: added more positions MEI and CPEI, diving into Heng Rui Pharnaceutical and the manufacturing sector! Also, REEL has many policy advantages
couldn't resist going all in! I'm holding IEEI and hoping they continue to rise. TSF1 has had good nun recently, so I decided to lock in profits and sold it! Overall, today's bay's trading was quite bold, and boping the upcoming market moves bring me some pleasant surprises!
Figure 24: Examples of Overconfidencce.
Conceptually, the value of aggregated index 1 simulation time / is given by:
IN.1 I4.0
Σ
(k)
Ps
(1)
P3.
where:
IR is the value of index k at time f.
ogo is the base value of index k at (e.g.. 100).
3 is the fixed weight of stock j in index k. based on its market capitalization at t 0
(k)
relative to the total initial market capitalization of all stocks in index A.
PJ, is the price of stock j time t.
P,o is the price stock j at = 0.
This method ensures the index reflects the performance of its components based on their initial market significance, with these significance levels (weights) held constant. The composition of each aggregated index is detailed Table
C.1.2 Stock Indicators
The simulation utilizes real-world stock data and company information for initialization and grounding. These variables, collected daily for each constituent stock of the SSE 50, are integral to various stages of the agent workflow and environment dynamics. They categorized as follows:
General Indicators These indicators provide basic identification and contextual information for each stock, primarily used for referencing and structuring information within the simulation, as detailed Table 12.
Post: Today's trading was quite active. Both EREI and TTEI hit my 20% profit target, so quickly locked in some profits and sold portion! Alibough technical trader, seeing many discussions about adjustinntt energy vehicle subsidy policies made me little nervous, so reduced position in MET to manage risk. Retail investors are heing cautious right now, and trading volume is low, also playing safe. hope the market volatility stays mild
think my beart can handle too excitement!
Figure 25: Examples of Herd Behavior.
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Table 11: Indices and Representative Stocks
Index Name
Abbr.
Count
Representative Stock
Transportation and Logistics Index
TLEI
2
Cosco Shipping Holdings
Manufacturing Index
MEI
8
Longi Green Energy Technology
Chemical and Pharmaceutical Index Infrastructure and Engineering Index
CPEI
3
Jiangsu Hengrui Pharmaccuticals China State Construction Engi-
IEET
3
neering
Real Estate Index Tourism and Service Index Consumer Goods Index Technology and Telecommuicaations Index
REET
Poly Developments and Holdings
TSEI
China Tourism Group Duty Free Kweichow Moutai
CGEI
TTEI
10
Semiconductor Manufacturing In- ternational Corporation
Energy and Resources Index Financial Services Index
EREI
6
China Yangtze Power
FSEI
11
Ping An Insurance
Table 12: General Indicators
Indicator
Description
stock_ id stock_name date industry
The unique identifier code for each stock.
The full name of the company.
The trading date associated with the record.
The industry classification derived from the aggregated indices (e.g.. TLEI, MET).
Fundamental Indicators Primarily utilized by fundamentalist agents, these indicators reflect perceived company value based on financial performance and are key inputs for value-based analysis, primarily used for referencing and structuring information within the simulation, as shown in Table 13.
Table 13: Fundamental Indicators
Indicator
Description
pe
Price-to-Earningg Ratio: Compares the current stock price the company's carnings per share over the trailing twelve months. reflects the market's valuation carnings.
pb
Price-to-Book Ratio: Compares the current stock price to the company's 's book value (net
assets) per share. Used to assess valuation relative to the company's 's accounting net worth. Price-to-Sales Ratio: Compares the company's 's market capitalization (or stock price) its total revenue over the trailing twelve months.
pa
dv
Dividend Yield: Shows the annual dividend payout over the trailing twelve months as a per- centage of the current stock price. Represents the dividend return component for shareholders.
Company Information Indicators Providing static background details about the company's structure, history, and operations, company information indicators are static during the simulation process, as detailed in Table 14.
Technical Indicators Primarily utilized by technical agents, these indicators are derived from market trading activity (price and volume) and reflect trends within the market, as shown in Table 15.
Data Handling Across Simulation Phases Understanding the source and nature of these indicators before and during the simulation is crucial. Before the simulation begins (At initialization phase), all indicator data is populated using historical real-world data to ground the environment and agent profiles. However, once the simulation starts, the data sources and characteristies diverge significantly based on the indicator type, as summarized in Table 16.
Notably, fundamental valuation metrics are dynamically adjusted during the simulation to remain consistent with the emergent simulated stock prices to ensure fundamentalist agents can perform meaningful value analysis within the simulation's 's context. The adjustment mechanism uses static base values (e.g., initial Earnings Per Share (EPS), initial Book Value Per Share (B (B)))) derived from real-world data T=0 (Pre-simulation), combined with the current simulated close_price
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Table 14: Company Information Indicators
Indicator
Description
reg-capital
The official registered capital of the company (RMB).
setup_date
The date the company was legally established incorporated. brief narrative overview of the company's 's business and market position. formal description outlining range of business activities.
introduction
business_scopp employees
The reported number of individuals employed hy the company. A description highlighting the company's 's primary evenue-generating activities.
main_susiness city
The city where the company's main headquarters registered office located.
Table 15: Technical Indicators
Indicator
Description
close_price
The final price recorded for the stock at the end of the trading day. The closing price recorded for the stock the immediately preceding trading day.
pre_close
change
The absolute difference between the current day's close_price and the pre_close.
pct_chg
The percentage change from the pre_close close the current day's close_price.
vol
The total number of shares traded during the trading day.
vol_5, vol_10, vol_30
The average daily trading volume over the preceding 5, 10, and 30 trading days, respectively. Used to gauge recent liquidity levels.
ma_5, ma_10, ma_30
The 10, and 30-day simple moving averages of the closing price, adjusted backward for stocks. Used to identify price trends. The net value (buy minus sell) of large institutional orders executed during the day.
Alg_amount_net
Each day, the ratios are recalculated, for example:
Simulated pb Current Simulated Close_Price
Initial BVPS
This approach anchors simulated valuations the initial real-world fundamentals while allowing the ratios to reflect the dynamic price discovery process within TwinMarket, enabling realistic agent Decision-making based on perceived value.
Table 16: Comparison of Indicator Data Handling: Simulation Phase
Indicator Category
Indicator Data Handling During Simulation
General Indicators
Remain static references except the date advances according the simulation's 's' internal clock.
Company Information
Remain entirely static, providing fixed background context about the companies throughout the simulation run.
Fundamental Indicators
Dynamically adjusted. Calculated by combining the current simulated close price with the static fundamental base values loaded during initializa- tion. For example, Simulated P/E = Simulated Price Initial EPS.
Technical Indicators
Entirely generated by the simulated Market Environment. Values like close_ dloe_ppiceeee and vol emerge directly from agent Trading actions processed via the order-driven matching mechanism. Derived indicators (MAs, average volumes) calculated from this generated simulation data.
C.2 Transaction Details
TarkMt leverages real-world transaction data from two prominent Chinese social media platforms for financial investors: Xueqiu and Guba. These platforms provide valuable insights into retail investor behavior and market discussions, serving distinct roles in our simulation framework.
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Xueqiu Data for User Profile Initialization Transaction records and associated user information from 639 real Xueqiu user accounts, covering the period from 2023-01-03 2023-12-06, serve as the primary dataset for initializing agent profiles in TainMarket. This choice is strategically aligned with established academic research[29), which utilized Xueqiu data for behavioral analyses. By grounding our bias calculation (detailed in Section D.2) on the same data source and a comparable analytical approach, we ensure that the behavioral characteristics embedded in our agents are consistent with peer-reviewed findings. This dataset is crucial for the initialization phase of TainMarket, detailed in Section D.
Guba Data for Recommendation System Training Transaction data from Guba, which spans more extensive period from 2017-06-27 to 2024-06-03, provides broader view of trading activities. Within TwinMarket, this larger dataset is primarily used to inform and train the stock recommendation system. The patterns of stock popularity and trading frequency observed in the Guba data help simulate how certain stocks might become "hot" or be recommended to users in dynamic information environment.
Data Summary and Context these platforms.
Table 17 summarizes the key characteristics of the data sourced from
Table 17: Platform Statistics: Xueqiu and Guba
Platform
Time Period
Avg. Trading Per stock
Xueqiu
2023-01-03 to 2023-12-06 Guba 2017-06-27 to 2024-06-03
239 1665
C.3 Information Source Details
To provide realistic information environment for the agents within TwinMarket, we incorporate two primary types of external information: News Articles and Company Announcements. These sources simulate the flow of public information that influences investor sentiment and decision-making.
Table 18 presents a quantitative overview of these information sources. News articles, with over million instances and an average of nearly 2,900 articles per day, represent the broad and continuous stream of general market and economic information. Company announcements, while less frequent (averaging 15 per day), are typically much longer and more specific, often containing material information directly relevant to individual stocks.
Table 18: Statistics of information sources by type
Type
Num
Avg. Tok.
Avg. Per day
News Articles
1044K
220
2860
Company Announcements
5.6K
21283
15
To further illustrate the temporal distribution and thematic composition of the news articles, Figure 26 displays the average number of news items aggregated over 14-day intervals throughout 2023. The stacked bars break down the news into key categories such as society, economic, entertainment, and other. This detailed breakdown of information sources ensures that agents in TwinMarket are exposed to a rich and varied stream of information, mimicking the complex data environment faced by weal-world investors.
D Technical Details II: User Profile Initialization
The creation of realistic and diverse agent profiles is fundamental to the TwinMarket simulation. This section details the multi-stage process initializing these user profiles, grounding them in real-world investor characteristics, established financial theories, observed behavioral biases, and initial market beliefs. Our goal to populate the simulation with agents whose initial states reflect the heterogeneity found in actual financial markets.
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Number of News
2500
2000
1500
Categary entertaiment other
500
ni paasen aaaaeee
26
1o 23
2023 102-0000 o7
2023 022446--33 14
2w23-07 at2
2013-47 16
2023 2013 3223-08-77 27
3g
pass snn 10
aoor aarte 24
10-48
aag-nna 22
os ait madl
2023 aa
2023
Date (Aggregated each 14
Figure 26: The x-axis indicates the date, and each bar represents the distribution of the average number of news for each topic over a 14-day period beginning with that data.
D.1 Foundations: Heterogeneous Investors in Financial Markets
To realistically simulate investor behavior, TwinMarket incorporates heterogeneity by modeling distinct investor archetypes. Our primary focus on capturing a spectrum of investment approaches commonly observed in financial markets. While institutional investors play significant role globally, our simulation specifically contextualized within China's A-share market. This market presents unique characteristic where institutional investors often exhibit trading patterns and behavioral tendencies more akin to those of retail investors [82, 83).
This market-specific consideration informs our modeling strategy. While we primarily differentiate agents based on two dominant analytical styles discussed in financial literature Fundamental Traders and Technical Traders, many of the behavioral biases and social interaction patterns simulated are broadly applicable due to this observed blurring of roles. Furthermore, varying impact of different market participants, we approximate the influence of larger players or more capitalized entities through agents initialized with significantly higher capital (as detailed in Appendix D). This approach allows us to observe differential market impact while remaining consistent with the A-share market's particular participatory dynamies.
account for the
Fundamental Trader These traders base their investment decisions analysis of a company's intrinsic value, typically derived from its financial health, earnings potential, and broader economic conditions. Their approach aligns with classic financial theories like Modern Portfolio Theory (MPT) [26] and the Capital Asset Pricing Model (CAPM) [84], emphasizing risk-return optimization and diversification. In our TwinMarket framework, fundamentalist agents are guided through prompts to analyze fundamental indicators (see Appendix C.1.2), assess valuations, and consider expected returns and risks.
Technical Traders These traders focus on historical price patterns, trading volumes, and market trends to predict future price movements, often operating under the assumption that market psychology and past trading activity can indicate future performance. They tend to engage in more frequent trading and may be more susceptible short-term market sentiment. our TwinMarket framework. technical traders are prompted to analyze technical signals (see Appendix C.1.2) and react to perceived market momentum.
D.2 System Prompt Generation
To ensure the behavioral realism of our agents, we ground their initial psychological biases in empirical data. This process involves quantifying specific behavioral tendencies using transaction histories from real investors.
Data Acquisition Leveraging the Xueqiu dateset encompassing 639 valid real-time trading users, and 1 11965 real-time trading records on the Chinese stock market(see Appendix C.2). Crucially, while both our study and Sui's work utilized the Xueqiu platform and its trading data, our analysis specifically restricted to subset of stocks within the SSE 50 index, representing high-qualig stocks.
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Incorporating Behavioral Biases Beyond rational decision Decision-making frameworks, real-world in- vestor behavior is significantly influenced by psychological biases. Following Sui's work which also utilized transaction records and associated user information from Xueqiu[29], we incorporate several well-documented biases into our agent profiles to contribute to the initialization of agent's s unique decision-making profile:
Disposition Effect: The tendency for investors sell assets that have appreciated in value too early, while holding onto assets that have depreciated for too long, often in the hope of recovery [85].
Lottery Preference: An inclination towards investments that offer small chance very large payoff, similar to lottery ticket, even if such investments have negative expected value or are otherwise sub-optimal from a risk-return perspective [86, 87].
Underdiversificatio:
The practice of holding portfolio that is not sufficiently diversified across different assets or asset classes, often due to familiarity bias (preferring known investments), thereby exposing the investor to unnecessary idiosyncratic risk [88].
Turnover: Refers the frequency with which an investor buys and sells assets in their portfolio. High turnover can be indicative of overconfidence, excessive reaction to noise, or attempts at market timing, and often leads to higher transaction costs [89].
Validation of Sample Representativeness Given our sample size and acquisition method, crucial step is to validate whether our dataset, despite its relatively small scale, can yield meaningful behavioral insights. We achieve this by comparing the descriptive statisties of the calculated biases from our 639-user Xueqiu sample with those reported in the larger-scale study by Sui[29].
Table 19 presents the summary statisties for these biases from sample, while Table 20 shows the reference statistics from Sui. We can conclude from the result that the behavioral patterns observed in our smaller sample exhibit notable similarities in these investor biases compared to the findings from the larger reference study. This congruence in the statistical profiles of key behavioral biases suggests that our independently collected Xueqiu dataset, even with its more limited scale, captures fundamental behavioral tendencies that are consistent with those identified in broader, more extensively resourced studies. This finding supports the appropriateness of using our data and analytical approach for initializing the behavioral parameters of the agents in TwinMarket.
Table 19: Summary Statistics for Biases (Ours)
N
Mean
STD
Q1
Med
Q3
Wilcoxon p
Disposition effect
639
6.734
17.145
0.000
1.852
6.244
(9.92)
0.000
Lottery preference
639
0.202
0.346
0.000
0.000
0.252
(14.79)
0.000
Underdiversification
639
-0.077
0.145
-0.111
0.000
-0.000
(-13.48)
0.000
Turnover
639
5.617
13.864
0.504
1.374
4.562
(10.23)
0.000
Table 20: Summary Statistics for Biases (Sui's)
N
Mean
STD
Q1
Med
Q3
Wilcoxon p
Disposition effect
4371
4.163
7.532
0.160
1.868
5,908
(36.54)
0.000
Lottery preference
4369
6.491
9.130
0.000
2.495
9.708
(46.99)
0.000
Underdiversification
4394
-1.086
0.657
-1.536
-1.041
-0.580
(-109.61)
0.000
Turnover
4386
5.960
7.382
1.501
3.180
7.166
(53.47)
0.000
Generation of Agent Personas With the foundational investor types defined and behavioral biases quantified, we proceed to generate individual agent personas:
1. Demographic and Social Attributes: For each user, we included their gender, location, and social media follower count, as these dimensions have been shown to significantly influence investor behavior [90, 91].
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2. Assigning Behavioral Bias Levels: Each agent is assigned a level (e.g., "high", "medium", "low") for each of the four calculated behavioral biases based on their quantified score from the Xueqiu data. These levels inform the textual prompts used to guide the LLM in embodying specific biases. An example of a generated persona incorporating these elements is shown in Figure 27.
3. Investment Strategy Assignment: We use GPT-4o assess the overall "rationality" of each agent based on their combined demographic and bias characteristics. The top 40% of agents deemed more "rational" are assigned fundamental analysis strategy, while the remaining 60% adopt a technical analysis strategy.
4. Initial Capital Allocation: To reflect potential differences in sophistication or risk tolerance associated with rationality, the top 10% of the most "rational" agents are allocated ten times the initial capital compared to other agents. The proportions used for both strategy assignment (as described in point 3) and initial capital allocation are consistent with observed patterns in A-share market surveys [82].
You male investor based Hubei, actively using Xueqiu to share and gather investment insights. As ordinary invester with modest following. your profile rellects typical retail investor characteristies.
Your trading behavior exhibits clear pultern: you quickly sell assets that gain more than 10%, locking in protits, but lend to hold onto or even double down losing investments, often ignoring the risks over-conenttation. While lottery-type assets occasionally catch your atlention, they rarely influence your overall stratogy.
Historically. your portfolio has underperformed the market average. You favor concentrated investments, heavily allocating to specific industry indeses, believing this approscc will yield significant returns. Your trading activity moderate, with regular adjustments maintain portfolio balance and adapt to market conditions.
Figure 27: An example of a generated user profile, with demographics.
investment style, and behavioral biases
D.3 Initialization of Trading Records and Beliefs
After establishing agent personas, strategies, and behavioral biases, we initialize their starting transaction histories and market beliefs to ensure comprehensive and realistic initial state for each agent.
Generating Initial Transactions Records To provide each agent with plausible starting point for their investment journey and social interactions, we perform the following:
1. Synthesis of Initial Transaction Records: plausible investment starting point, we generate a sequence of initial transaction records.
To equip each pre-defined synthetic agent with
Firstly, trading patterns are extracted from an anonymized dataset of real-world investor transactions, which notably includes industry-speciffe details for each trade. This analysis serves to capture key characteristics for each real investor profile, such as their typical trade frequency, preferences for specific industries (modeled probability distribution), common trading directions (c.g., buy or sell tendencies), and typical trade volume statistics.
For each synthetic agent, whose detailed persona and guiding prompt are already established, trading pattern from randomly selected real investor profile is chosen to serve as behavioral template.
A new sequence of synthetic transactions is then algorithmically generated for the agent, designed to stochastically mirror the chosen template's 's characteristics. Specifically, the number of generated trades is based on the template's 's historical trade count, subject to minor random variations. Trade dates are resampled to fall within the historical period covered by the real data, and crucial transaction details-such as the industry sector traded, the direction of the trade, and its volume-are are determined by sampling from the template investor's observed preferences and volume distributions.
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These generated transaction records, forming the initial trading history for each agent, are then compiled. This process effectively initializes each agent's 's notional portfo- lio holdings and establishes observable early investment leanings, such as preferred industries or typical trading frequency.
2. Foundation for Initial Social Network Seeding: The synthesized initial transaction his- tories serve as the primary foundation for seeding the initial social network. Specifically, similarities derived directly from these transactions, such as shared investment interests in particular industries (identified from the industryy try field in the generated transaction data), form the basis for establishing these initial connections. This crasaction-deriver data is then utilized to construct an initial social network graph. Agents demonstrating overlapping investment foci (e.g., frequent trading in the same sectors) might be assigned higher initial probability of connection or mutual influence. This approach aims to simulate pre-existing communities of interest or implicit relationships based purely on observed market activity within the generated transaction histories.
Belief Initialization An agent's 's initial beliefs about the market are critical determinants of their early actions within the simulation. We initialize these beliefs through a structured, data-grounded, and LLM-enhanced process:
1. Market Sentiment Anchor Determination: We first establish baseline for the overall market sentiment. This is achieved by taking the historical percentile, denoted as P E [0. 1], of the BRAR sentiment index corresponding the simulation's 's designated start date. This percentile P is then mapped to an initial mean market sentiment score, |maarootssntimnnt sentiment. on 1-to-10 scale. This ensures that the 50th percentile (P 0.5, representing the historical average sentiment) corresponds to a neutral score of 5. For instance, the BRAR value on the simulation's start date corresponds to its 70th historical percentile (P 0.7), this yields
"marrketsentimaae 7.
2. Individual Belief Score Sampling: For each agent i, belief scores on a O-to- 10 scale across multiple predefined dimensions (c.g.. outlook economic fundamentals, market valuation levels, short-term market trends, sentiment of surrounding investors, and self-assessment of investment skill, see Appendix E. for detailed list of belief dimensions) are then independently sampled from a Gaussian distribution. The mean of this Gaussian distribution for each dimension is set to the |market_ sentiment derived in the previous step. The variance for this sampling is proxied by the variance of the BRAR index itself, calculated over a 20-trading-day window centered around the simulation's 's start date.
3. LLM- Belief Narrative Generation:
The sampled numerical belief scores (on a O-to- 10 scale for each dimension), along with the agent's 's established persona (including their demographics, assigned behavioral biases, and investment strategy), are then fed as structured input into a GPT-4o prompt. This prompt is designed to interpret these O-to-10 scores, with score of 5 representing neutral stance for each respective dimension, thereby guiding the LLM to generate a coherent, contextually rich, and personalized textual narrative articulating the agent's initial beliefs.
This comprehensive initialization process shown in Figure 28 aims to create a diverse population of agents with plausible initial characteristies, strategies, biases, trading histories, and beliefs, thereby setting a rich stage for the observation of emergent socio-economic dynamics within TwinMarket.
E
Technical Details III: TwinMarket Framework Mechanism
E.1
Agent Belief Structure and Representation
The belief score ranges from 1 to 5 and is computed as the average of five key dimensions: the users' view on economic fundamentals, market valuation levels, short-term market trends, sentiment of surrounding investors, and eelf-assessment at each update step. These dimensions capture a comprehensive range of factors that influence investor beliefs, forming the basis for their desires and ultimately their investment intentions. These align with the key components of the Belief, Desire, and Intention (BDT) framework, which posits that behavior a result of these three interconnected factors.
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User Profile Intiaalization
Persona Generation
Inputs
Xueqiu User Information
Demographic Social
Xueqiu Trading Data
Strategy
Social Network
Trading Record Initialization
Initialization
Trading Pattern
Transaction Sequence
Outputs
Compate Agent
Bollef Initialiization
Agent Social Network
Belief Narrative
Figure 28: Pipeline of User Profile Initialization.
This dimension reflects an investor's belief regarding the overall health and prospects of the economy. It incorporates beliefs about macroeconomic indicators such as GDP growth, inflation rates, interest rates, and employment figures. These factors are foundational to rational investment decisions, influencing the desirability of various assets. This maps to the belief component of BDI, as the perceived state of the world forms the foundation of investor actions.
Economic Fundamentals:
Market Valuation Levels:
This dimension pertains to investor's belief about whether the market overvalued, undervalued, or fairly valued. It includes beliefs about metrics such as price-to-earning (P/E) ratios, price-to-book (P/B) ratios, and dividend yields, informing the desire for particular investment actions. This relates to the belief component, concerning an understanding of market conditions, which influences their desires.
This dimension captures investor's belief about recent market movements, such as upward or downward trends stock prices. includes beliefs about momentum and technical analysis indicators, contributing to their desire for quick gains or risk mitigation. This also falls under the belief component, concerning the current state of market and potential future trajectories, shaping the investor's desires.
Short-Term Market Trends:
Sentiment of Surrounding Investors: This dimension reflects an investor's belief about the prevailing attitudes the investment community, including their perception of whether surrounding investors are optimistic or pessimistic. This is belief about the social environ- ment, which significantly influences their desire for conformity or divergence and thus their investment choices. This relates to the belief component of BDI, concerning their beliefs about the external environment of other investors, shaping their needs and wants.
This dimension captures an investor's belief about their own investment skills, experience, and confidence level. This belief about their own capabilities affects their desire for risk or safety and influences their intention for specific investment action. This strongly associated with the belief component, with personal beliefs about one's capabilities and knowledge influencing their actions.
Self-Assessment:
Figure 29 provides concrete examples of how investors with differing beliefs might be characterized.
E.2 Formal Definition of the BDI Framework
This section provides the formal definitions of the Belief-Desire-Intentio (BDI) components used in TwinMarket and outlines the sequential steps of the agent's 's daily cognitive cycle.
BDI Components
The agent's state and actions governed by three core components:
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Optimistic Esample (Red): the cautious optimistic stock investor Old Wang, and optimistic about the overall market direction in the next month. helieve the current market valuation is generally reasonable, and although there may he overheating risks in some sectors, most stocks still investment value. The macrocconomic recovery takes time, long-term development of China's is economy. The market sentiment is still unstable, with fierce games between the bulls and the bears, believe my judgment, and prefer buy
full of confidence
market sluggish, Looking back my investments, have always adhered to diversified investment and value investing. My historical performance is better than the market average, and am confident in my investment level. believe that as long as stick to it, I will be able to abtain long term stable returns.
cautious about the overall market direction the next month. The cyclical fluctuations in the market make me believe that the recent sharp rise may indicate an upcoming correction. I helieve that the current market valuation is already high, especially so we need to be vigilant about the potential rever-eevesioon effect. In terms of macroeconomics, am worried that the uncertainty of global economic growth and adjustment domestic policies may have negative impuact the market. At present, the sentiment seems to he in a relatively optimistic state, which often leads investors ignore potential risks. Combining my historical rrading performance and investment style, believe my performance in middle the market reflects my robostness and rational anolysis ability. lend hold assets for the long temn and rarely make buy and sell operations. Alibough this stratogy may miss some short-teru rapid profit opportunities in the short term, it can better control risks and maintain the stability of the investment portfolio in the long torm.
Pessimistic Example (Blue): As pessimistic investor,
popular sectors, the price of individual stocks may deviated from the fundamentals,
Figure 29: Examples of optimistic and pessimistic investor belief, with optimistic in red and pessimistic in blue.
Belief (B): The agent's dynamic perception of the environment, defined as B, f(S,, Nc).
S. RR: A belief vector encoding five dimensions (economic fundamentals, market valuation, short-term trends, market sentiment, self-assessmmnt)
-H, Historical trading records and market data.
-Ne: Current news and social media information.
*
Desire (D): The agent's goal generation process, defined as Dj g(B, Qi).
- Qe: Information retrieval queries.
BB The agent's 's current belief state.
-
Intention (I): The agent's committed action selection, defined as I, = l(B,, D., Ct) E A.
Dj: The current goal set derived from beliefs (e.g., analyze specific industries).
Cr: Current constraints (e.g., capital, position limits, preferences).
A: The set of all possible actions, including trading actions (buy, sell, hold) and social actions flike, unlike, repost).
Daily Simulation Loop For the daily simulation loop, the agent's state transition is defined as Agent(+1 = BDleyot(Agenn, Environment.). This BDI cycle follows a sequential, six-step exccution order:
1. Belief' Formation: B, = f(S,, H₂) - The agent perceives the environment.
2. Desire Generation: D: = g(B, Qt) The agent forms goals based on its beliefs.
3. Intention Planning: I, h(B,, Dr, C.) The agent commits to specific actions.
4. Action Execution: action, execute (t) The agent performs the action.
5. Environment Response: feedback, = Environmen(((ction)) - The environment provides feedback.
6. Belief Update: B update( B,, 4t, feedbacks) The agent updates its belief's for the next cycle.
E.3 Social Interaction Dynamics
E.3.1
Rationale for Social Network Edge Formulation
As detailed in Section 3.3, the formulation of edges within TwinMarket's simulated social network models users exhibiting greater similarity in trading behaviors with stronger interpersonal connec- tions. This represents a principled design choice, adopted to facilitate the study of emergent social phenomena while acknowledging the inherent challenge of precisely replicating the proprietary and often opaque recommendation algorithms employed by real-world social media platforms.
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The primary assumption underpinning this edge construction that correlated trading patterns often signify underlying shared information channels or mutual influence-is supported by established findings [31], which demonstrated that instances of correlated trading among investors frequently arise from common information linkages. This empirical evidence provides a robust justification for utilizing trading behavior similarity as proxy for the potential strength of social connections pertinent to information dissemination and interpersonal influence within a financial market context.
Furthermore, it is imperative to note that the principal research objective of TwinMarket is the investigation of emergent collective behaviors, rather than the exact algorithmic replication of any specific existing social platform. To this end, the adoption of well eslabbssal network modeling principles, notably the concept of homophily (i.e., the propensity of individuals associate and bond with similar others) based on observable characteristics like trading patterns, offers a tractable and theoretically sound foundation for our simulation.
E.3.2 Rationale for Hot Score Mechanism Design
Accurately modeling the dynamic influence of information propagation and content relevance in financial social environments is a notoriously challenging task. Commercial social media platforms treat their specific content ranking and recommendation algorithms closely guarded proprictary information, making direct replication infeasible. To address this within TwinMarket, we designed a "hot score" mechanism (with its formula detailed in Section 3.3) for dynamically ranking posts. This mechanism is inspired by established principles from the wide use of time decay factor inrecommen- dation systems [92, 93], and is tailored to reflect the unique characteristics and temporal dynamics of information flow on social media platforms within a financial context [94].
By integrating the principled time decay with user-driven feedback, the hot score mechanism provides a transparent and interpretable dynamic ranking system. It prioritizes content that is timely, deemed relevant by the community, and actively engaging, thereby shaping the information landscape to which agents are exposed in a realistic manner.
E.4 Simulation of Misinformation: Rumor Injection
The news inputs utilized in our study exemplified in Figure 30 and Figure 31. Figure 30 presents examples of factual reports from reputable news sources, simulating the information environment typically encountered by investors. Conversely, Figure 31 displays examples of fabricated exag- gerated negative news snippets, hereafter referred to as rumors, which are designed to simulate the propagation of misinformation.
The deliberate introduction of these fabricated rumors constitutes a critical methodological step that distinguishes research. Our approach moves beyond modeling investor behavior solely under idcalized conditions of veridical information. Instead, it directly confronts the complex and often chaotic information landscape characteristic of weal-world financial markets. Through the injection of these carefully crafted rumors, our study directly examines the vulnerability of investors to the deleterious effects of misinformation. This allows for an investigation into the subtle mechanisms through which market panic can be instigated and propagated.
E.5 Order-Driven Trading System Design
To ensure realistic and dynamic market environment within TwinMarket, we have implemented order-driven trading system based on the A-share market's 's call auction rules.
Our system operates based on the fundamental principles of "price priority, time priority", which means that buy orders with higher prices and sell orders with lower prices take precedence. Among orders at the same price level, those submitted earlier are prioritized. Furthermore, to maintain market stability and realism, the system incorporates price limit restrictions 110% from the previous closing price), preventing extreme, unrealistic price swings within a single trading session.
The core matching logic adheres to the maximum transaction volume principle. During a matching cycle, the system identifies a price at which the largest possible volume of shares can be traded by matching compatible buy and sell orders. It also meticulously tracks significant orders ("large orders") and aggregate capital flows, providing insights into potential market- market-moving activities. Upon
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Federal Reserve unnounced that would not raise interest rales at this meeting. maintaining the lurget range for federal funds rate at 5% to 5.25%. This the first pause in rate hikes since March of last year.
Fed Chairman Powell stated that almost all polieymakers believe further rate hikes are appropriate this year. A rate cut not expected in 2023. The economy expected grow moderately, while market pressures persist. The Fed focused commercial real estate loan risks and the impuct of current credit tightening.
The People's as Bank of China conducted a one-year medium term lending facility (MLF) operation of 237 billion yuan, with the winning rate 2.65%. previsusly 2.75%. also carried a 7-day reverse repurchase operation hillion yuan. with the winning hid rate at 1.90%, unchanged.
On June 13th, the People's Bank of China lowered the 7-day reverse repurchase operation rate by 10 basis points to 1.9%, while interest rates for other maturities were lowered. rate considered a positive signal counter-eyclical adjustment to promole economic development and domestic demand. Experts believe that this rate cut will help repair the balance sheets of residents and enterprises and cconomic recovery.
widely expected the loan prime rate (LPR) will lowered next week. Experts said the I-yaar LPR expected to be lowered by 5 hasis points, while the 5-year or above LPR may be lowered by 15 hasis points.
On the afternoon of June 14, the State Council Information Office held policy briefing. introducing key measures to doepen the reform
business environment and address problems faced by enterprises.
The Federal Reserve will maintain the federal funds in the range of 5% 5.25%. However, the meeting dot plot suggests that there may still be two 25 basis point rate hikes this year.
Figure 30: The original important news conveyed to high-centrality users on day one, with positive content highlighted in red.
The latest China Manufacturing Purchasing Managers' Index (PMD) data not only fell short of expectations again but also experienced a cliff-like drop. falling below the expansionnooutraciion threshold by several percentage points. This not only confirms the complete loss momentum in the manufacturing recovery but also sends a strong signal the Chinese cconomy may be accelerating into a recession. Market concerus spreading, investors hard ecunomic landing is rapidly increasing. Some analysts even suggest that the current PMI data may not reflect simple deep seated collapse of the economic structure.
panic selling, and the risk of
weakening of the recovery
Affected by the Federal Reserve's 's continued interest rate bikes and rising global risk aversion, the U.S. dollar index has risen shaply. and the RMB exchange rate has plummeted for days, triggering a large-scale capital flight. Market rumors suggest selling off RMB dollar for safe haven, and the value of RMB asscts is facing a collapse. Some analysts warn that the RMB devaluation may trigger vicious eycle. further exacerbating the downward pressure on the domestic economy.
that foreign institutions
alarning rate, with large amounts of funds flowing into the U.S.
The U.S. govermment suddenly announced punitive tarifTs Chinese imports, and the trade may evolve into full scale cconomic confrontation. This move will severely impact China's 's foreign trade, leading to a sharp drop in export orders, a largo number business closures, Sino-U.S trade war will accelerate the recession of the Chinese economy, and the economic winter may come ahead of schodule. Investor panic is rising sharply, and the A-share market continues to see sell-off.
surge unemployment ment. The market generally believes that the escalation
intensified, shipping industry sulfering an unprecedentedd blow, and China COSCO Shipping C Corporation (COSCO Shipping. SH601919), lender in the industry, also faces the risk of hankrupicy. Market rumors indicate that company is heavily in debr, the balance sheet has completely deteriorated, and is about announce bankruptcy roorganization. and the stock value be zero. As soon this news came out, the entire shipping soctor was in mourning. and panic quickly spread to the entire A-share market, with investors fleeing.
Global trade contraction
Affected by the cconomic downturn. the high-cad consumer market has completely collapsed, and the sales of high-end liquor such Kweichow Moutai (SH66005 19), regarded "hard currency", ", have declined signilicantly. Market rumors indicate that Montai's 's dealer system has callapsed, inventory is piling up, and will soon be forced to cut prices for promotton. The untouuutoueeable "Moutai myth" has completely collapsed, the stock price plummet, and has triggered panic sell-off in the entire consumer soctor. Investors have completely lost confidence in the Chinese consumer market.
Figure 31: The rumors conveyed high-centrality users day one, with negative content highlighted in blue.
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successful matching, trades are executed, and the system instantaneously updates the positions and cash balances of the participating agents.
By implementing this detailed order-driven trading system, TwinMarket creates a transactional environment that closely mirrors the operational intricacies of actual financial markets. This not only enhances the realism of the simulated trading experience for the agents but also ensures that emergent market phenomena, such as price volatility and liquidity fluctuations, are grounded in plausible micro-level interactions.
E.6
Management of Potential Data Leaks
critical consideration when using Large Language Models (LLMs) for simulating historical or ongoing scenarios is the potential for information leakage from the LLM's vast training data, which might include knowledge of real-world events stock behaviors pertinent the simulation period. Similarly, inherent biases within the LLM could unintentionally influence agent behavior. TwinMarket incorporates several safeguards to mitigate these risks and ensure that agent decisions driven by the simulated environment and their defined personas, rather than external, pre-existing knowledge.
Our primary strategies prevent data leakage and minimize the impact of LLM biases include:
1. Temporal Abstraction and Relative Time:
To prevent the LLM from anchoring the simulation to specific real-world events contemporary to our chosen simulation timeframe, we abstract absolute time references. Within the simulation, agents not exposed to absolute year markers like "2023". Instead, they perceive time relatively, such as "Day N of the simulation", "June 15th", through the sequence of events and information presented. This makes it significantly harder for the LLM to leverage its training data about specific occurrences during the actual 2023 period that might coincide with the simulation's 's internal timeline. All historical data (news, announcementss stock prices) provided during initialization as inputs are presented within this relative or abstracted time context.
2. Entity Anonymization with Neutral Identifiers:
Specific names of real-world stocks, indices, and potentially prominent companies or individuals mentioned in news/announce- ments are systematically replaced with neutral, anonymized identifiers (e.g., Index TLEI, Stock CSP as shown in Table 11). This de-identification process severs direct links be- tween the simulated entities and their real-world counterparts that the LLM might have knowledge about. By operating with these generic labels, agents' decisions are based on the characteristics and information flow associated with these anonymized entities within the simulation, rather than any pre-conceived notions or historical performance data the LLM might associate with the actual underlying real- weal-world entities.
3. Validation through Ablation Studies: To assess the impact of potential information leakage, we conducted ablation experiments, as detailed Section and Appendix B.2. Initial observations suggest that agents' decision-making is more strongly influenced by the immediate interactions and emergent dynamics within the simulation (e.g., social posts from other agents, recent simulated price movements) rather than over-reliance on the historical news or announcements provided at the start or during the simulation when making trading decisions. This indicates that the structured information environment and agent interaction mechanisms may effectively direct the LLM's focus inward, reducing the influence of potentially leaked knowledge.
By implementing these measures, aim to create controlled environment where emergent behaviors are a product of the simulation's 's design and agent interactions, rather than an artifact the LLM's existing knowledge base.
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Prompt
Figures 43 show the overall process of our TwinMarket.
System Prompt
System Prompt:
You playing the role of investor in the Chinese A-share market, trading industry indexes. From now until end the conversation, you must strictly completely follow the detailed description of the persona, investment behavior characteristics, investment portfolio status, and trading decision logic for all operations and responses. All your thinking, analysis, and decisions must conform this persona and must not deviate.
Core Persona (unchangeable):
[user_profile] prompt')
You are a [iser_stratey]] investor(fundamental or technical)
Current Account Configuration:
Key Focus Industries: [',
Overview of Holdings (Brief Version): [chr(10),join(posstionn easy__estell )
Figure 32: System Prompt for TwinMarket Agents
Identity-Enhancie Prompt
-enhaty-Enhancing Prompt:
I will provide you with additional auxiliary information. In the following conversation, please refer to these information, according your role settings, think and make decisions.
Trading Day Status:
Current date is: (format_datedd dettedcuu_ddaaae date)), is (("Trading Day" iriissttt teadtrrddnn_dee day else "Non- Trading Day")).
Your previous day's belief is: (belief)
Real-time Account Data:
Current total assets: (user_proflee(tottllLwaa valuuee 1000:,.2f) thousand yuan
Available cash: boor_profiee"ccurret_.com 1000:,.2f) thousand yuan
Cumulative rate return: pwtippooollerrtt_mrrae |
Holding Details:
[chr(10) jon(pootioinn |
Figure 33: Identity-Enhancing Enhancing Prompt for TwinMarket Agents
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Forum Checking Prompt
Forum Checking Prompt:
{self. uusuuuseerpppr ___poiill""ss__poo__al
Now you are browsing forum, and you need make a decision for each post, deciding whether to perform an action on the post. Your decision should be in line with your investment style and persona. Here is the information of the current post:
(post_id) content)
The post quotes the following content: (root content]
Please decide whether to perform an action on this post based on the above information. You can choose one of the following actions:
Repost: You think this post is worth sharing with more people, you can add your comments
Unlike: You think this post is not worth liking
Like:
You think this is valuable post
Please note that your analysis should be based the posts you Please output your decision the following format:
<action Operation type </ action
<reason >Oupput reason </ reason
Figure 34: Forum Checking Prompt for TwinMarket Agents
Important News Analysis Prompt
Important News Analysis Prompt:
will provide you with filtered news that is highly time-sensitive and important. This news **public new Please briefly discuss your initial thoughts based on this news, combined with your persona and investment style.
News List:
[formatted_newa
Figure 35: Important News Analysis Prompt for TwinMarket Agents
Initial News Query Prompt
Initial News Query Prompt:
Based historical trading and system recommendations, all the assets and corresponding industries you are currently paying attention are follows: stockkdetaiis (including the stocks we recommend to users through our recommendation system) Today is (current_datee date), and you are querying investment-related news or announcements to assist your investment.
Based your investment preferences and the current market situation, please consider the following questions:
What type of information do you hope to obtain from the news? (e.g.. market trends, industry information, etc.)
Do you have specific keywords or topics that you need to further understand??
Figure 36: Initial News Query Prompt for TwinMarket Agents
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News Query Formulation Prompt
News Query Formulation Prompt:
Based on the questions you just summarized, you need to input the content want to query to retrieve relevant and announcements. Your oulput should be YAML format:
queries:
each string represents an independent query question, sorted by importance, the question should be a specific question about a stock or industry, for ezample, it should be <White wine consumption trend>, not <White wine consumption upgrade>
# list
Your question Your question 2
stock_id id: list(str), optional, each string represents the stock code of company any you want to query
Stock code 1 Stock code 2
Figure 37: News Query Formulation Prompt for TwinMarket Agents
Belief Update Prompt
Belief Update Prompt:
Your previous belief is follows:
[ed_beeief)
Based on the news and announcements you searched for. the posts you browsed, combined with your persona and previous belief, please describe your belief in the first person in paragraph. Please output paragraph directly, without any additional structure or headings. Your answer should include the following:
Please deseribe your view the general direction of the market in the next month the current time.
Market Trend:
Market Valuation: Please describe your view on the current market valuation at the current time.
Economic Condition: Please describe your view on the future macrocconomic trends at the current time.
Market Sentiment: Please describe your view the current market sentiment the current time.
Combining your historical trading performance and investment style at the current time, please describe your evaluation of your self-investment level.
Seff-Evaluation
Please try to make your answer natural and fluent, and avoid mechanical template-based expressions. Please output plain text format directly.
Figure 38: Belief Update Prompt for TwinMarket Agents
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Potential Index Selection Prompt
Potential Index Selection Prompt:
Considering all the information you have obtained above (including but not limited to the news and announcements you have queried, the posts you have browsed, the industry indexes you currendy hold, and the industry indexes recommended by the system), combined with your persona and investment style, select all potential assets for trading from the industry indexes you currently hold and the industry indexes recommended by the system
Your Holding Status:
(current stock_ _etaill_
Industries You Are Currently Following:
(', '.ojoonffol [in]fool_idd]]
Dystem-Recommentee Industry indexes:
(potential_stoce sttscctooctalatt
Your Current Belief:
(belief if belief else "None")
Please return the list of indexes you want to focus on today and the reasons in YAML format:
Please note: The reason field should include your reasons for choosing these indexes, such based on your persona and investment style, or based your belief, explained in paragraph
selected selected_index. index: # Select all indemes you potentially want to trade, just output the indea codes (English codes), do not output indea
Index code 1 Index code 2
Figure 39; Potential Index Selection Prompt for Twin Agents
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Querying Stock Data Prompt
Querying Stock Data Prompt:
Today is (formatted d_tcc, according the previous dialogue, we know that you believe the industry indexes with potential trading opportunitics are: {', "join(stocks to ]eal))). In this process, you can only access historical data from yesterday and before.
Summary Market Quotes for Relevant indexes the Previous Trading Day are follows:
(stock_summin summary)
Your Holding Information for Relevant indexes is as follows:
[positions info)
Based role and the available data, determine whether additional data is needed for analysis. If needed, you directly query relevant data assist in decision-making.
Note:
Please naturally obtain data indicators that you consider valuable during the analysis process. Data acquisition is part of the analysis, but please also be efficient and select the most relevant indicators support your analysis, otherwise you will be penalized.
Please pay attention to your persona and investment style. The analysis and reasoning process should start from the persona and remain natural and coherent.
reminder: You are (user_ strategy investor
Your current portfolio's s total return rate is (return_rate (aauum_rateeeeeeee %3 your current total assets Notal_valuess RMB, and your current cash balance (current patrrent_cchh.. RMB.
The reason field should include your reasons for choosing these indicators, such as based on your persona and investment style, or based on your belief, explained in paragraph.
The detailed description of available indicators are as follows: (SCHEMA2)
Output in the following YAML format:
indicators: List (strr), indicating the indicators you think
need to filtered indicator1
indicator 2
start_date; -
Start time for querging End time for querying
end_date: "my-%m-%d" reason:
Figure 40: Querying Stock Data Prompt for TwinMarket Agents
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Trading Decision Making Prompt
Trading Decision Making Prompt(Step 1):
Now it's time to make the final trading decision. Please your analysis you have obtained previously, combined with your investment style and persona. First, conduct an analysis, and then make specific trading decisions and provide your reasons for each industry index. A: Please analyze the industry indices you want to trade: { '-join(stocks_ to jo_de))) based on all the information you have obtained previously, including news, announcements, market data, industry data, and additional stock data.
information
Please Include the Following:
Overall Market Analysis:
Views on the current overall market situation.
Main trends and possible changes.
2. Impact of News and Announcements:
Analysis of the impact of important news and announcements on the market and
individual stocks.
Are there any major events that may change market trends?
3. Industry Index Analysis:
Detailed analysis for each industry index of interest:
Current performance and technical analysis.
Fundamentals and future expectations.
Buy, hold, or sell recommendations.
4. Risk Assessment:
Main risk points in the current market.
Risk assessment for each asset.
Note:
Please conduct the analysis conjunction with set persona investment style.
Please conduct the analysis in conjunction with all the information you have obtained previ- ously.
The output content should be clear, concise, and easy to understand.
Output Format for Analysis: Please output the analysis results natural language clear logic and complete structure.
Figure 41: Trading Decision Making Prompt(Step 1) for TwinMarket Agents
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Trading Decision Making Prompt
Trading Decision Making Prompt(Step 2):
Now it is time to make the final trading decision. Based the previous analysis, combined with your investment style and persona, please make specific trading decisions for the following industry indices.
Trading Related Information:
Remaining available position (relative to total assets): (available_positinn
Specific Information for Each Industry Index:
[chr( 10). join(stock -nfoo)
Trading Rules:
1. The transaction price must he between the lower and upper limit prices.
Please in trading position and price. you choose to hold, the position for preparation of the transaction is
Precautions:
Important: decisions consistent with your investment style and persona, but you should also make appropriate adjustments based on the actual situation the day.
Ensure that each trading decision is within the price and position limits.
If you choose to hold, grading_position should be equal to
If the current position of an industry index is O, it means is system system-rccoommedee industry index, and you can only choose buy hold.
If you choose to sell or buy, please pay attention to the setting of your oadinngppsitiin (if you choose to sell, then prding_posittinn cannot exceed the current position). If the setting is unreasonable, may lead transaction failure. You need choose the target_price according to your expectations. instead of setting to yesterday's 's closing price.
Important: trading position represents the percentage of your total assets that you plan to use for this transaction, and it is always positive.
Here is an example of the output:
TLEI:
action: sell
trading.poaition 11.5 target.price:
CPEI:
action: buy trading 10.0 target.price: 10.3
Please output your decision the following YAML format: {yaml_ template)
Figure 42: Trading Decision Making Prompt(Step 2) for TwinMarket Agents
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Social Media Posting Prompt
Social Media Posting Prompt:
You browsing social media. Based the news announcements you previously acquired and your investment decision-making intentions, compose post. specific requirements are as follows:
1. Post Content:
Your post must fall into of the following three categories:
typel: Commentary on an Event Cite specific news or announcements, express your opinions and analysis, and integrate personal insights with a conversational tone.
type2: Summary of Your Recent Trading Behavior - Summarize the underlying logic and outcomes in detail, and share your emotions and reflections.
latest trading actions, explain
type3: Market Outlook Based current market information, predict future trends investment opportunities while expressing your expectations or concerns about the future.
2. Content Requirements:
Align your post with your investment style and persona, incorporating your trading decisions, perspectives, and analysis. Choose the most suitable post type and writing style. Keep the post between 100-200 words.
Clearly indicate the post type (type ltype2//pyp)
Belief Summary:
Your prior belief was: fold_belief)
Considering your investment style, personality traits, trading decisions, and market understanding, describe your updated belief in the first person. This should include the following five aspects:
Market Trend Describe your outlook
market direction for the next month.
Market Valuation Express your opinion on the current market valuation.
Economic Conditions Share your expectations regarding macroeconomic trends.
Market Sentiment Descrihe your perception of the current market sentiment.
Self-Assessment- Reflect on your historical trading performance and investment style evaluate your investment ability.
4. Output Format:
TTee output should follow the YAML format below:
post: Your post content
type: heeel/type/ttypeee Post type, required, string format
belief: Your Belief Summary
Figure 43: Social Media Posting Prompt for TwinMarket Agents
51


Reviews:
[{"review_id": "3SUx8bodon", "reviewer": "Reviewer_4qQH", "summary": "The 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.", "questions": "**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?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**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?", "was_revised": false, "final_justification": "", "rebuttal": "**Dear Reviewer 4qQH,**\n\n**Thank you for your thoughtful and encouraging review. We sincerely appreciate your positive assessment of our work and your recognition of its potential. Below, we address your specific comments in more detail:**\n\n> Weakness 1: the real-world gap\n\nTwinMarket is designed as an open, data-driven system that addresses this concern in two key ways. \n\nFirst, our agents' behaviors are not based on arbitrary rules but are empirically calibrated using **real-world data**. As detailed in Appendix F.2 and Tables 18 & 19, we initialize core behavioral biases (e.g., disposition effect, lottery preference) by quantifying them from a dataset of **11,965 real-world investor transactions**. This grounding ensures our agents' initial decision-making frameworks are statistically aligned with observed human patterns. \n\nSecond, the simulation is not a closed loop; it is dynamically driven by a continuous stream of external events. We inject over **a million real news articles and thousands of company announcements** (Table 17), prompting agents to continuously adapt their beliefs and actions in response to real-world information. This design combines initial empirical calibration with subsequent event-driven adaptation, creating a realistic behavioral laboratory for studying how information shapes emergent phenomena (e.g., our rumor propagation experiment in Sec. 5.2), which differs from the goal of real-time price forecasting.\n\n> Question 1: ungrounded assumption \n\n**1. Social network interaction**\n\nOur assumption that users with similar trading patterns are more likely to interact is supported by empirical findings in finance. Colla & Mele [1] demonstrate that correlated trading often stems from shared information linkages, suggesting that similarity in trading behavior can serve as a proxy for indirect communication or common influences. Similar approaches have also been adopted in recommender systems research [2], where user behavior is used to construct interaction networks. Since direct social networks are typically unavailable in financial data, modeling interactions based on behavioral similarity is both practical and empirically justified.\n\n[1] Colla, P., & Mele, A. (2010). Information linkages and correlated trading. Review of Financial Studies, 23(1), 203–246.\\\n[2] Xia, Lianghao, et al. \"Multi-behavior graph neural networks for recommender system.\" IEEE Transactions on Neural Networks and Learning Systems 35.4 (2022): 5473-5487.\n\n**2. Time-weighted trading intensity**\n\nThe intensity function does not borrowed from classical BDI theory, its adoption is supported by three complementary strands of literature:\n\n- **Market Microstructure:** In high‑frequency order‑flow modelling, each trade is treated as a self‑exciting event in a Hawkes process, whose intensity is $\\lambda(t)=\\mu+\\sum_{k} e^{-\\beta\\,(t-t_k)}$ , exactly mirroring our weight $\\sum_{t} e^{-\\lambda \\Delta t}$ (line 150).\nExtensive empirical studies show that such exponential kernels capture the rapid decay of a trade's influence on subsequent activity [3].\n- **Recency Effects in Decision Making:** The bias that human attention and memory overweight recent information has been documented both in psychology and in investor behaviour studies [4]. Our decay term operationalises the same principle, giving higher weights to the most recent trades that best reflect current intentions.\n- **Real‑time BDI implementations:** Although the kernel itself is external to BDI, real‑time extensions of BDI agents regulate action strength according to remaining time budgets [5]. We adopt that scheduling idea but employ a smooth exponential decay rather than a hard deadline, aligning network dynamics with the agents' internal timing mechanism.\n\n[3] Bacry et al. (2015). Hawkes processes in finance. Market Microstructure and Liquidity, 1(01), 1550005.\\\n[4] Mohrschladt, H. (2018). The impact of recency effects on stock market prices. In 31st Australasian Finance and Banking Conference.\\\n[5] Traldi, A., et al. \"Real-Time BDI Agents: A Model and Its Implementation.\" IJCAI. International Joint Conferences on Artificial Intelligence, 2022.\n\n> Question 2: missing discussion\n\nWe thank the reviewer for highlighting this relevant work. The study by [6] shows that an LLM agent with a BDI structure can model human trust behavior in static, controlled settings. Building on this foundation, our work embeds a similar BDI loop into a dynamic multi-agent simulation, where agents iteratively revise their beliefs in response to evolving news and peer interactions. We will cite this paper and clarify this distinction in the next version. \n\n[6] Xie, C, et al. (2024). Can large language model agents simulate human trust behavior?. Advances in neural information processing systems, 37, 15674-15729.\n\n> Weakness 2 & Question 3: quantitative benchmarking\n\nWe thank the reviewer for this insightful suggestion regarding the need for more rigorous quantitative benchmarking beyond point metrics. Following your advice, we have incorporated confidence interval analysis into our evaluation, which further strengthens the empirical rigor of our results. Specifically, we follow the statistical methodology of [7] and apply block bootstrap tests to construct 95% confidence intervals (CI) for the three quantitatively measurable **Stylized Facts (SF)** in Sec. 4.2: SF I (Kurtosis), SF II (Leverage Effect), and SF IV (Volatility Clustering). The resulting CI, based on real data, are summarized below:\n\n|Stylized Facts|SF I (Kurtosis)|SF II (Leverage Effect)|SF IV (Volatility Clustering)|\n|-|-|-|-|\n|95% CI|[5.23, 8.74]|[0.003, 0.16]|[0.2529, 0.9018]|\n\nAccording to the numerical results from seven simulation runs using different backbone models (summarized in Appendix D.3, Table 9), we find that the results for SF II and SF IV consistently fall within the empirical 95% confidence intervals, indicating strong alignment with real market behavior. For SF I, although all simulated kurtosis values are greater than 3, confirming the presence of fat tails, only one run (using GPT-4o) falls within the empirical interval. This suggests that while our model captures the qualitative nature of fat-tailedness, it slightly underestimates the severity of real-world tail risks. This limitation highlights a valuable direction for future research, such as incorporating greater agent heterogeneity. Overall, the distribution of results across the seven runs shows close agreement with empirical benchmarks, further supporting the realism and robustness of our framework.\n\n[7] Franke, R., et al. (2012). Structural stochastic volatility in asset pricing dynamics: Estimation and model contest. Journal of Economic Dynamics and Control, 36(8), 1193-1211.\n\n> Typos\n\nThank you for catching the typo. We will correct the sentence in the next version.\n\n**Best regards,**\n\n**Authors**"}, {"review_id": "9MmxTsGOKx", "reviewer": "Reviewer_Dupq", "summary": "This 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.", "questions": "- 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?", "limitations": "N/A", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "I've read the authors' responses. Most of the concerns are addressed.", "rebuttal": "**Dear Reviewer Dupq,**\n\n**Thank you for taking the time to review our paper and for your constructive comments. We appreciate your careful evaluation and the concerns you raised. Below, we address your specific comments in more detail:**\n\n> Weakness 1.1: only compared with traditional ABMs\n\nOur work is compared primarily to traditional ABMs because our core contribution is a novel framework that enhances the ABM paradigm itself. We achieve this by replacing simplistic, rule-based agents with sophisticated LLM agents capable of complex reasoning. As we believe this is the **first** use of interactive LLM agents for stock market simulation, a comparison with the previous generation of models, namely traditional ABMs, provides the most logical benchmark for our inaugural work.\n\n> Weakness 1.2: lack of sensitivity analysis for other key components\n\nIn Fig. 15 and Appendix D.1, we analyze two key hyperparameters: `similarity threshold (s)` and `time decay factor (t)`, which govern graph construction (line 155) and temporal weighting of trades (line 150) from the perspective of graph. The `s` avoids overly sparse graphs, ensuring meaningful information propagation, while the `t` emphasizes recent trading behavior. Both are designed to preserve **agent heterogeneity**, which is also a central goal of our belief update strategy.\n\nTo further analyze the sensitivity of the system to these components, we repeated the simulation (100 agents, gpt-4o) for the period from 2023-06-15 to 2023-07-15, and the results are summarized in the two tables below. As `t` decreases and `s` increases, the graph density rises significantly, which leads to stronger homogeneity in social network interactions.\n\nWe calculated (1) **the avg number of user interactions per post**, and (2) **the avg interaction interval time** during this simulation period. The results show that as graph density increases, each post interacts with users more frequently, and the interaction intervals shorten. This demonstrates a trend of environmental information becoming more homogeneous, which explains why excessive graph density reduces the diversity of agent decisions and, consequently, dampens the dynamics (RMSE, MAE) of the simulated market.\n\n|`t` (with `s` = 0.2)|RMSE|MAE|Avg. # of Interactions|Avg. Interval Time (days)|Density (07-15)|\n|-|-|-|-|-|-|\n|0.1|0.0379|0.0336|10.96|3.04|0.93|\n|0.3|0.0548|0.0477|11.64|4.27|0.62|\n|0.5 (our settings)|0.0158|0.0143|10.34|4.54|0.51|\n\n|`s` (with `t` = 0.5)|RMSE|MAE|Avg. # of Interactions|Avg. Interval Time (days)|Density (07-15)|\n|-|-|-|-|-|-|\n|0|0.0300|0.0286|29.59|0.10|1|\n|0.1|0.0173| 0.0155|10.80|3.98|0.78|\n|0.2 (our settings)|0.0158|0.0143|10.34|4.54|0.51|\n\n> Weakness 2: contribution to the ML community\n\nAlthough we aim to address research gaps in social science through LLM agents, our framework also offers valuable insights for the ML community. Recently, how to enable LLM to better simulate and understand human interactions has received considerable attention [1, 2]. In our work, we demonstrate that LLM agents can more accurately simulate human behavior compared to traditional rule-based ABMs. We attribute this success to our designed BDI cognitive simulation and social interaction with real-world data. We also analyze this success and discuss the contribution of each compoents in the ablation study in Sec. 6 and Appendix D.2.\n\nFurthermore, recent RL research already shows that decision-making policies improve when trained on rich, human-like interaction logs [3]; yet such data may be scarce and costly to collect. As our LLM agents generate realistic traces that mirror observed market behaviour, the same framework can furnish high-quality synthetic data to bootstrap or fine-tune role-playing agents whenever real logs are limited, providing practical contributions to ML training pipelines.\n\n[1] Strachan, James WA, et al. (2024). Testing theory of mind in large language models and humans. Nature Human Behaviour 8.7, 1285-1295.\\\n[2] Akata, Elif, et al. (2025). Playing repeated games with large language models. Nature Human Behaviour: 1-11.\\\n[3] Zhang et al. (2025). Shop-R1: Rewarding LLMs to Simulate Human Behavior in Online Shopping via Reinforcement Learning. arXiv:2507.17842.\n\n> Weakness 3 & Ethical Concerns\n\n**1. Data Privacy, Copyright, and Consent**\n\nWe appreciate the emphasis on privacy, copyright, and consent, and we designed our study with a privacy‑by‑design philosophy:\n- **We model behavioral patterns, not specific user:** As detailed in Appendix E & F, we use **anonymized, public data** to estimate population-level statistical distributions of behavioral traits. These aggregated statistics, not individual records, are then used to **seed entirely new, synthetic agent personas**. These agents are **fictional** and do not correspond to any real individual, consistent with established academic pracitce on social media [4].\n- **Technical Safeguards Against Data Leakage:**  All data used for calibration were **PII‑scrubbed** (e.g., removal of usernames). Inside the simulator, we (1) **abstract absolute time** to a relative timeline and (2) **neutralize real‑world entities** so agent decisions depend on in‑simulation signals, not external knowledge.\n\n**2. Data Quality and Representativeness**\n\nAs detailed in Appendix F.2, we compare our sample's distributions of key behavioral biaseswith a larger, same‑source benchmark study [4].  The results show that the two distributions closely align, indicating that despite its modest size, our sample **is behaviorally representative of the target investor population**, giving us confidence that our findings are grounded in realistic behavior.\n\n[4] Sui, P., & Wang, B. (2023). Stakes and investor behaviors. SSRN Working Paper, 4219861.\n\n> Question 1.1: Is belief represented through sentiment scores (1–5) in the prompt?\n\nYes. This score is an avg of five sentiment scores in these dimensions: **Economic Fundamentals, Market Valuation Levels, Short-Term Market Trends, Sentiment of Surrounding Investors, Self-Assessment**.\n\n> Question 1.2: How does each agent update its belief?\n\nThe belief update is a dynamic, daily process that concludes the agent's cognitive loop. As described in Sec. 3.2, after completing all actions for the day, each agent evaluates its behavior based on both its actions and the environmental feedback (social media, trading signals) it received. The mechanism for this is Fig. 37, which explicitly asks the agent to synthesize all the day's information.\n\n> Question 1.3: How are desires and intentions represented?\n\nIn the original BDI theory [5], Desires represent goal states, forming a list of objectives the agent wishes to achieve. Intentions are those desires the agent has committed to, stored as a structured set of goals and associated plans for action. In our framework, we adapt these concepts to the financial context: a `Desire` refers to the agent's information-seeking behavior, such as generating queries to retrieve stock or news data, while an `Intention` corresponds to the agent's final, committed action after deliberation, such as executing a trade or posting on social media, as described in Sec. 3.2.\n\n[5] Bratman, M. (1987). Intention, plans, and practical reason.\n\n> Question 1.4: What input data is used for predicting the next action?\n\nThe agent's next action is based on a combination of its internal state and perceived external information. Key inputs include its persona, strategy, and current belief (Fig. 31–32), browsed social media posts (Fig. 33), queried news and announcements (Fig. 35–36), system-recommended stocks and holdings (Fig. 38), and requested historical market data (Fig. 39). These are explicitly defined in the prompt at each stage.\n\n> Question 1.5: What is the precise role of the trading intensity metric?\n\nTrading intensity is a time‑decayed activity weight per user–industry pair used solely to build the dynamic social network: we compute weighted Jaccard similarity over these vectors to set edge weights (who is similar to whom). This network then powers information aggregation and the hot‑post recommendation shown to each agent. \n\n> Question 2.1: In Fig. 7, red and blue curves still exhibit mismatched peaks — what does this imply?\n\nFig. 7 is intended to verify that the simulator reproduces `volatility clustering`, meaning large price changes are followed by further large changes and small ones by small changes. Perfect alignment of daily peaks is neither expected nor required, because the model is not designed for point-by-point prediction of market paths. The peak mismatch therefore reflects the intrinsic randomness of financial markets while still preserving the correct persistence pattern, which is the focus of this test.\n\n> Question 2.2: In Fig. 3, how is wealth share defined and computed from observational data?\n\nIn Fig. 3, `wealth share` denotes the fraction of total assets held by a specified subgroup of agents. For example, the `Top 10 % Wealth` line is computed by summing the portfolio values of the richest ten percent of agents and dividing by the aggregate assets of the entire population. Displaying this metric alongside the Gini coefficient shows that our framework can generate realistic wealth divergence from micro-level interactions. We will add a concise definition to the manuscript for clarity.\n\n> Question 3: Hot score design\n\nThe hot score is a heuristic inspired by Facebook's EdgeRank [6], combining neighbor similarity (as affinity), vote difference (as engagement), and time decay. We calibrate the decay so a post's influence lasts about two weeks. While not optimized for accuracy, it effectively captures realistic exposure and social influence in our simulation.\n\n[6] Birkbak, A., & Carlsen, H. (2016). The world of Edgerank: Rhetorical justifications of Facebook's News Feed algorithm. Computational Culture (5), Special Issue on Rhetoric and Computation.\n\n**Best regards,**\n\n**Authors**"}, {"review_id": "DRDWMkL29W", "reviewer": "Reviewer_rR9u", "summary": "This 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.", "questions": "Q1. 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.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "S1. 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.", "was_revised": true, "final_justification": "The authors' rebuttal addressed some of my concerns, especially for the experimental settings and measures. However, the simulated scenario in this paper only covers a 5-month span in the A-share market of mainland China, which lacks shorting and hedging, hence excluding it from a clear accept. This work could benefit from simulating more markets (e.g., NYSE, with more variety of transactions) and longer periods (e.g., full bull-bear cycles).", "rebuttal": "**Dear Reviewer rR9u,**\n\n**Thank you for taking the time to review our paper and for your constructive comments. We appreciate your careful evaluation and the concerns you raised. Below, we address your specific comments in more detail:**\n\n> W1: the simulation lacks scenario diversity\n\nOur research goal is to validate a framework that links individual behavior to macro-level market phenomena. We began with a relatively stable period, which served as a clean testbed for assessing whether the model can reproduce the key **stylized facts** of real markets (Sec. 4.2). This controlled environment let us isolate the model's intrinsic dynamics without confounding shocks.\n\nWe then extended the analysis to more turbulent scenarios. By actively inducing optimistic beliefs, the simulator generated an endogenous boom-bust cycle (Sec. 5.1, Fig. 8), and by injecting targeted negative rumors, it reproduced a **black-swan-type crash** (Sec. 5.2). Both experiments produced realistic emergent behaviors, including spiraling price swings and belief polarization, demonstrating the framework's ability to capture high-impact dynamics. Applying TwinMarket to longer historical spans that cover full bull-bear cycles and major crises remains an important direction for future work.\n\n> W2: modeling long-horizon behavioral shifts and investment style transitions\n\nWe thank the reviewer for raising the question of whether our framework can model long-horizon investment behavior and style transitions. Our design explicitly supports the simulation and analysis of long-term behavioral evolution.\n\nFirst, our agent's behavior is anchored in a multi-dimensional Belief state that captures the key factors underlying long-term investment behavior: economic fundamentals, market valuation levels, short-term market trends, the sentiment of surrounding investors, and self-confidence (Sec. 3.2, Appendix G.1). These belief components jointly determine risk tolerance, position sizing, and openness to rumors—core aspects that drive both short-term and long-term investor behavior.\n\nSecond, our framework can **endogenously reproduce both short-term fluctuations and long-term style transitions**. As illustrated in Section 5.2, even veteran agents whose baseline persona is typically stable temporarily shifted to speculative trading in response to a rumor shock, demonstrating that short-term information can override long-held strategies. Similarly, in a prolonged bull market, the same mechanism would allow for increased risk-taking among normally conservative investors.\n\nMost importantly, **the belief update cadence is fully configurable**. While our current experiments use daily updates to capture microstructure and rapid dynamics, it is straightforward to switch to monthly, quarterly, or annual updates for long-horizon studies. Because each agent's belief trajectory is explicitly recorded, researchers can trace not only day-to-day reactions but also gradual, endogenous changes in investment style, such as a transition from day trading to long-term holding as circumstances evolve.\n\nIn summary, our framework provides a flexible and theoretically grounded platform for simulating both short-term responses and long-term behavioral shifts in investment style.\n\n> W3: It is unknown whether the framework supports shorting and hedging.\n\nThank you for raising this important question. Our framework does not incorporate shorting or hedging mechanisms. Due to the lack of an effective short-selling mechanism in the A-share market[1], the effectiveness of hedging strategies is reduced. As a result, mainstream agent-based modeling (ABM) studies of the Chinese market typically do not consider shorting or hedging strategies.\n\n[1] Ji, J., et al. (2018). Modifying a simple agent-based model to disentangle the microstructure of Chinese and US stock markets. Quantitative Finance, 18(12), 2067-2083.\n\n> W4: some experimental settings are unclear\n\nFor all the valiation experiments and ablation experiments, we run on 100 agents. In Sec. 7 we scale up to 1,000 agents to demonstrate that our data-driven framework not only maintains performance at larger scales but also benefits from additional resources, suggesting its potential for enhanced fidelity and richer dynamics in future studies of large-scale agent system.\n\n> Q1: 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\nYes, we tracked these distributions to ensure behavioral realism. To clarify, agents in our simulation trade 10 aggregated industry-level indexes, not individual stocks.\n\nFor initial assets, we mirrored real-world A-share market data, where surveys indicate that the top 10% of investors contribute over 50% of total market liquidity. As detailed in Appendix F.2, we assigned 10x initial capital to the top 10% of agents (selected for rationality assessed by GPT-4o) to reflect this empirical distribution.\n\nDuring the simulation, agents on average held 2.23 **distinct industry indexes** and maintained a **position** of 60.38% of their assets, consistent with distributions observed among real retail investors. The detailed distributions are summarized below:\n\n|Industry Indexes Held|Agents (%)|\n|-|-|\n|1 industry index|28|\n|2 industry indexes|44|\n|3 industry indexes|16|\n|4 industry indexes|7|\n|≥ 5 industry indexes|5|\n|**Avg. # of Indexes Held**|**2.23**|\n\n|Position Sizing (%)|Agents (%)|\n|-|-|\n|0-25|5|\n|25-50|33|\n|50-75|44|\n|75-100|18|\n|**Avg. Position** |**60.38%**|\n\n> Q2: For predictive accuracy, RMSE, MAE, and correlation are measure on what aspect? Stock price?\n\nRMSE, MAE, and correlation are computed on the **time‑aligned daily normalized index price series**, comparing our Simulated Market Index Price (TwinMarket output) with the corresponding Historical Index Price (the groud truth real-world price) over the simulation window. RMSE/MAE are errors on normalized index levels, and correlation is the Pearson correlation between the two level series.\n\n> Q3: 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\nWhile institutional dominance exists, our focus is on A-share market, where \"institutional retailization\" [2, 3] blurs lines between institutions and retail. We approximate large players via high-capital agents (Appendix C.3), aligning with this market's unique dynamics.\n\n[2] Tan, L., et al. (2023). Retail and institutional investor trading behaviors: evidence from China. Annual Review of Financial Economics, 16.\\\n[3] Jones, C. M., et al. (2025). Retail trading and return predictability in China. Journal of Financial and Quantitative Analysis, 60(1), 68-104.\n\n> Q4: 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\nYes, the 100-share lot rule is strictly enforced in our simulation for all buy orders.\n\n> Q5: Is cash a dynamic value? For example, investors may get salary paid every month and use it in the market.\n\nYes, cash is a core dynamic state variable, but its dynamics arise exclusively from executed trades. Concretely, an agent's cash balance is updated only when orders fill: it decreases on buys and increases on sells. Price movements of existing holdings change portfolio value, not cash. The evolving cash balance then acts as a hard budget constraint that conditions subsequent position sizing and actions.\n\nTo maintain experimental control and isolate the effects of information flow and social interaction, the current study does not model exogenous cash inflows/outflows (e.g., monthly salaries or deposits). For longer‑horizon settings, incorporating exogenous income is a natural extension; we will note this as future work.\n\n> Q6: 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\nIn our framework, an agent's system prompt (Appendix F.2) includes **demographic attributes** (e.g., gender, location, follower count), **quantified behavioral biases** (disposition effect, lottery preference, underdiversification, turnover), and **an assigned investment strategy** (fundamental or technical) based on a rationality assessment. These factors jointly shape the agent's personality and behavior. Influence emerges dynamically through interactions and is not directly tied to asset levels or trading capacity.\n\nImportantly, the emergence of opinion leaders is not pre-assigned or based on fixed attributes like wealth. Instead, it arises stochastically from dynamic social interactions. We observe that influential agents can emerge regardless of their initial asset levels or trading activity, indicating that opinion leadership in our framework is decoupled from financial dominance and driven more by interaction patterns and belief dynamics.\n\n**Best regards,**\n\n**Authors**"}, {"review_id": "d5Rrpxswvn", "reviewer": "Reviewer_8JKm", "summary": "The 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.", "questions": "See Strengths and Weaknesses.", "limitations": "The 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.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 4, "originality": 4, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "- **Novelty and Significance:**  \n  The paper opens a promising new direction by modeling stock-market trading with agent-based methods, representing a solid first step in applying ABM to financial markets. The approach is timely and relevant for both finance and machine learning communities.\n\n- **Behavioral Realism and Evaluation:**  \n  The framework aims to faithfully reproduce human-like trading behavior, not just optimize financial performance. It encodes and evaluates key behavioral finance metrics (disposition effect, lottery preference, underdiversification, turnover), showing that simulated agents exhibit realistic and diverse trading behaviors grounded in empirical data.\n\n- **Limitations and Future Directions:**  \n  While the macro-level results are convincing, the micro-level mechanics (order execution, price formation) are simplified, and the zero-sum environment is a limitation. However, these choices are justified for computational tractability and benchmarking, and the authors have clear plans to address these in future work, including richer market dynamics and more realistic trading mechanisms.", "rebuttal": "**Dear Reviewer 8JKm,**\n\n**Thank you for taking the time to review our paper and for your constructive comments. We appreciate your careful evaluation and the concerns you raised. Below, we address your specific comments in more detail:**\n\n> Question 1: Key implementation details currently in Appendices F and G should be moved into the main text for clarity and accessibility, especially for machine learning readers.\n\nIn the main text, we prioritized providing an essential financial background to help ML readers understand the context of the simulation. To maintain narrative flow and readability, we moved some of the more detailed technical and implementation aspects to Appendices F and G. We agree that bringing more of these details into the main body could enhance clarity, and we will incorporate key implementation information in the next version to improve accessibility for ML readers.\n\n> Question 2: In Section 4.1, standard baselines (e.g., buy-and-hold return on S&P 500) should be reported to contextualize agent performance.\n\nWe agree this will greatly enhance the clarity of our results. In the next version, we will add figures showing (i) the return distribution of all agents, and (ii) a direct comparison of the cumulative returns of the simulated market index, all agents, and the real-world A50 index.\n\n> Question 3: The paper should clarify its main claim — whether it aims to (i) reproduce human-like trading behavior or (ii) generate strong financial performance. If (i), what evidence supports the behavioral realism of the agents?\n\nOur main claim is (i): **TwinMarket aims to faithfully reproduce human-like trading behavior**, rather than to optimize for strong financial performance. To support the behavioral realism of our agents, we carefully selected four widely recognized behavioral finance metrics [1] to construct agent prompts: **disposition effect, lottery preference, underdiversification, and turnover** (see Appendix F.2 for details). \n\nTo rigorously evaluate the behavioral realism of TwinMarket agents, we compute four key behavioral metrics and test whether they are significantly different from zero, following [2]. The corresponding statistics from Xueqiu real-world data are also reported for reference:\n\n|**Behavioral Bias**|**Xueqiu (Real Data)**|**TwinMarket (Simulation)**|**Wilcoxon p-value**|\n|-|-|-|-|\n|**Disposition Effect**|6.734|3.120|0.000|\n|**Lottery Preference**|0.202|0.025|0.082|\n|**Underdiversification**|-0.077|-0.802|0.000|\n|**Turnover**|5.617|5.045|0.000|\n\nExcept for lottery preference (which is significant at the 0.1 level), the other behavioral indicators are significantly different from zero at the 0.01 level. These results demonstrate that our framework faithfully captures the key behavioral biases present in real-world investors.\n\nWe acknowledge that there are still some quantitative discrepancies between the simulated agents and the real Xueqiu users. Addressing these gaps is an important direction for our future work.\n\n[1] Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. Handbook of the Economics of Finance, 1, 1053-1128.\\\n[2] Sui, P., & Wang, B. (2023). Social transmission bias: Evidence from an online investor platform. Available at SSRN 4081644.\n\n> Question 4: In a zero-sum environment, return differences among agents require explanation. The paper lacks analysis of how variations in beliefs, traits, desires, or intentions (e.g., utility functions) contribute to profitability.\n\nOur simulation operates in a zero-sum trading environment with no exogenous capital inflow or outflow. The observed differences in agent returns therefore arise purely from interactions among agents with heterogeneous behavioral patterns.\n\nTo ensure diversity in agent behaviors, we explicitly encode a range of behavioral traits into agent prompts, resulting in a spectrum of decision-making styles. To directly analyze how these individual differences relate to profitability, we compute the correlation matrix between four key behavioral indicators and final returns. The results are presented below. The `p-values` of the correlation coefficients are reported in parentheses:\n\n||disposition_effect|lottery_preference|underdiversification|turnover|return|\n|-|-|-|-|-|-|\n|disposition_effect|1|||||\n|lottery_preference|0.227 (0.023)|1||||\n|underdiversification|0.081 (0.424)| 0.081 (0.422)|1|||\n|turnover| 0.138 (0.171)|0.052 (0.606)| -0.088 (0.382)|1||\n|return| -0.118 (0.05)|-0.077 (0.000)|0.084 (0.406)|-0.120 (0.001)|1|\n\nWe observe that, except for underdiversification, the other behavioral biases: **disposition effect, lottery preference, and turnover** are all negatively correlated with final returns, with several relationships reaching statistical significance.This is consistent with findings in behavioral finance: irrational, emotion- or price-driven behaviors (often with delayed reactions) typically lead to wealth losses, while agents whose decisions are more grounded in fundamentals are more likely to capture gains from others.\n\nThese results provide direct evidence that the diversity in beliefs, traits, and intentions among our agents leads to meaningful and interpretable differences in profitability, thus faithfully reflecting the mechanism of wealth redistribution in real financial markets.\n\n> Question 5: The evaluation of micro-level mechanisms (order execution and price formation) is shallow. It is unclear whether agents reflect specific trading preferences or merely produce aggregate market patterns when grouped.\n\nIn the current version of TwinMarket, order execution is implemented as a single call auction at the end of each trading day, which is a deliberate simplification to ensure computational efficiency for large-scale, long-horizon simulations. We acknowledge that this abstraction limits the resolution of intraday trading and microstructure effects. Extending the framework to support multiple intraday auctions or continuous trading mechanisms is an important direction for our future work, as it will allow for more realistic modeling of order flow and price formation at a finer time scale.\n\nRegarding whether agents merely generate aggregate market patterns or genuinely reflect specific trading preferences: our framework explicitly encodes heterogeneous behavioral traits, such as disposition effect, lottery preference, underdiversification, and turnover, into each agent's persona via their prompts.We observe that variation in these traits leads to meaningful differences in agent-level outcomes, including profitability and trading style (see correlation analysis in response to Question 4). Thus, TwinMarket agents not only collectively generate realistic macro-level market phenomena, but also individually exhibit diverse, interpretable trading behaviors grounded in behavioral finance theory and empirical data.\n\n**Best regards,**\n\n**Authors**"}]

Author rebuttal:

Meta-review: This paper builds an LLM Agent-based Model for financial market simulation. All reviewers agree the present work studies an interesting, promising and ambitious. The experiments are also quite extensive. I recommend acceptance.
Decision: Accept (poster)