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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>TwinMarket - A Scalable Behavioral and Social Simulation for Financial Markets</title>
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h1 {
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</style>
</head>
<body>
<nav class="navbar" style="display:none"></nav>
<div class="layout">
<aside class="sidebar">
<div class="nav-title">Contents</div>
<nav>
<a href="#home">Home</a>
<a href="#overview">Overview</a>
<a href="#features">Features</a>
<a href="#framework">Framework</a>
<a href="#results">Results</a>
<a href="#scalability">Scalability</a>
<a href="#citation">Citation</a>
</nav>
</aside>
<main class="container">
<header id="home">
<div class="award-badges">
<div class="award-badge">NeurIPS 2025</div>
<div class="award-badge">🏆 ICLR 2025 Financial AI Best Paper Award</div>
</div>
<h1><span class="title-accent">TwinMarket:</span> A Scalable Behavioral and Social Simulation for Financial Markets</h1>
<div class="authors">
<a href="https://yuzheyang.com" target="_blank" rel="noopener">Yuzhe Yang</a><sup>1,*</sup>,
<a href="https://hoder-zyf.github.io/" target="_blank" rel="noopener">Yifei Zhang</a><sup>1,2,*</sup>,
<a href="#">Minghao Wu</a><sup>1,2,*</sup>,<br>
<a href="#">Kaidi Zhang</a><sup>1</sup>,
<a href="#">Yunmiao Zhang</a><sup>2</sup>,
<a href="#">Honghai Yu</a><sup>2,‡</sup>,
<a href="#">Yan Hu</a><sup>1,‡</sup>,
<a href="https://wabyking.github.io/old.html" target="_blank" rel="noopener">Benyou Wang</a><sup>1,‡</sup>
</div>
<div class="author-note"><sup>*</sup> Equal contribution <sup>‡</sup> Equal advising</div>
<div class="affiliations">
<div class="affil-list">
<div class="affil-item">
<span class="affil-num">1</span>
<img src="assets/logo/CUHK.svg" alt="CUHK-Shenzhen" class="affil-logo">
<span class="affil-name">CUHK-Shenzhen</span>
</div>
<div class="affil-item">
<span class="affil-num">2</span>
<img src="assets/logo/NJU.svg" alt="Nanjing University" class="affil-logo">
<span class="affil-name">Nanjing University</span>
</div>
</div>
</div>
<div class="links">
<a href="https://arxiv.org/abs/2502.01506" class="btn btn-outline btn-arxiv" target="_blank" rel="noopener">
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</a>
<a href="https://neurips.cc/media/PosterPDFs/NeurIPS%202025/116676.png?t=1762476065.2065222" class="btn btn-outline" target="_blank" rel="noopener">🖼️ Poster</a>
<a href="https://iclr.cc/virtual/2025/10000145" class="btn btn-outline" target="_blank" rel="noopener">🎤 Talk</a>
<a href="#citation" class="btn btn-outline">📝 BibTeX</a>
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<button type="button" class="btn btn-outline" id="moreResearchBtn">🔎 More Research</button>
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</div>
<p class="hero-description">
We aim to build a foundation-scale market simulator where LLM-driven agents capture human-like investor behavior,
social influence, and collective adaptation, enabling us to reveal, stress-test, and ultimately understand
emergent macro-level financial dynamics from micro-level decision making.
</p>
<div class="stats-grid hero-stats">
<div class="stat-item"><div class="stat-icon">🤖</div><div class="stat-value stat-number">1000+</div><div>LLM Agents</div></div>
<div class="stat-item"><div class="stat-icon">📊</div><div class="stat-value stat-number">50</div><div>SSE Stocks</div></div>
<div class="stat-item"><div class="stat-icon">📈</div><div class="stat-value stat-number">4</div><div>Stylized Facts</div></div>
<div class="stat-item"><div class="stat-icon">🧠</div><div class="stat-value">Belief-Desire-Intention</div><div>Framework</div></div>
<div class="stat-item"><div class="stat-icon">👥</div><div class="stat-value">Real</div><div>User Data</div></div>
<div class="stat-item"><div class="stat-icon">⚡</div><div class="stat-value">Scalable</div><div>Architecture</div></div>
</div>
</header>
<section id="overview">
<h2>Overview</h2>
<div class="figure-container">
<img src="assets/img/TwinMarket.png" alt="TwinMarket Overview">
</div>
<div class="grid-2">
<div class="card">
<h4><span class="icon-label">🎯</span>Core Problem</h4>
<p>Traditional ABMs struggle to capture the diversity of human behavior, particularly irrational factors in behavioral economics</p>
</div>
<div class="card">
<h4><span class="icon-label">⚡</span>Our Solution</h4>
<p>LLM agents that account for cognitive biases, emotional fluctuations, and non-rational influences in market simulations</p>
</div>
</div>
<p>
💡 <strong>TwinMarket</strong> examines how individual behaviors, through interactions and feedback mechanisms,
give rise to collective dynamics and emergent phenomena in financial markets. Unlike traditional rule-based ABMs,
our LLM-based approach captures the full spectrum of human behavior complexity.
</p>
<p>
🎯 Through experiments in a simulated stock market, we demonstrate how individual actions trigger
group behaviors, leading to emergent outcomes such as <span class="keyword-highlight">financial bubbles</span>,
<span class="keyword-highlight">market recessions</span>, and other collective socio-economic patterns that arise from
the complex interplay between individual decision-making and market dynamics.
</p>
</section>
<section id="features">
<h2>Key Features</h2>
<div class="grid-3">
<div class="card">
<h4>🎯 Real-World Alignment</h4>
<p>Grounded in established behavioral theories and calibrated with real-world data,
ensuring realistic human behavior modeling</p>
</div>
<div class="card">
<h4>🔄 Dynamic Interaction Modeling</h4>
<p>Captures diverse human behaviors and their interactions, particularly in the context
of information propagation and social influence</p>
</div>
<div class="card">
<h4>📈 Scalable Market Simulations</h4>
<p>Supports large-scale simulations, allowing researchers to analyze the impact of group
size and interaction complexity on market behavior</p>
</div>
</div>
</section>
<section id="framework">
<h2>Framework Components</h2>
<div class="figure-container">
<img src="assets/img/framework.png" alt="TwinMarket framework overview" style="width: 100%; max-width: 100%;">
</div>
<p>
TwinMarket combines micro-level user behavior simulation (BDI-driven decisions) with macro-level market infrastructure simulation
(order-driven trading and social information interaction), and grounds both layers with real behavioral patterns, stock data, news,
and announcements to produce realistic macro market dynamics from individual agent interactions.
</p>
<h3>👤 Micro-Level Simulation: Individual Behaviors</h3>
<div class="grid-2">
<div class="card">
<h4>🧠 BDI Framework</h4>
<ul>
<li>💡 <strong>Belief:</strong> Agent's understanding of the market</li>
<li>🎯 <strong>Desire:</strong> Agent's objectives or preferences</li>
<li>⚡ <strong>Intention:</strong> Concrete trading actions.</li>
</ul>
</div>
<div class="card">
<h4>📊 Behavioral Biases</h4>
<div class="tag-row">
<span class="tag">💪 Overconfidence</span>
<span class="tag">📉 Loss Aversion</span>
<span class="tag">🐑 Herding</span>
<span class="tag">🎲 Risk Preferences</span>
</div>
<p>Influencing trading decisions and contributing to market heterogeneity</p>
</div>
</div>
<h3>🌐 Macro-Level Simulation: Social Interactions</h3>
<div class="grid-2">
<div class="card">
<h4>🌐 Social Network Construction</h4>
<ul>
<li>🔗 <strong>Dynamic Connections:</strong> Network evolves based on trading patterns</li>
<li>📋 <strong>Behavior Similarity:</strong> Agents connect through similar strategies</li>
<li>⏱️ <strong>Time Decay:</strong> Recent interactions weighted more heavily</li>
</ul>
</div>
<div class="card">
<h4>📡 Information Propagation</h4>
<ul>
<li>📦 <strong>Information Aggregation:</strong> Collecting data from social connections</li>
<li>👑 <strong>Opinion Leaders:</strong> Influential agents shape market sentiment</li>
<li>🔄 <strong>Echo Chambers:</strong> Formation of polarized belief groups</li>
</ul>
</div>
</div>
<h3>Data Sources</h3>
<div class="figure-container">
<img src="assets/img/data.png" alt="Data Sources" style="max-width: 800px;">
</div>
<div class="grid-2">
<div class="card"><h4>👤 Real User Profiles</h4><p>From Xueqiu social media platform</p><div class="data-stat">100K+ users</div></div>
<div class="card"><h4>💱 Transaction Details</h4><p>Historical trading data from Xueqiu</p><div class="data-stat">1M+ trades</div></div>
<div class="card"><h4>📈 Stock Data</h4><p>SSE 50 index from CSMAR database</p><div class="data-stat">50 stocks</div></div>
<div class="card"><h4>📰 News & Announcements</h4><p>From Sina Finance, 10jqka, and CNINFO</p><div class="data-stat">Daily updates</div></div>
</div>
</section>
<section id="results">
<h2>Experimental Results</h2>
<h3>Information Propagation</h3>
<div class="grid-2">
<div class="canvas-box">
<h4>Opinion Leader Emergence</h4>
<canvas id="opinionLeaderCanvas" width="400" height="300"></canvas>
<p class="viz-description">Influential nodes emerge and shape network opinions through cascading influence</p>
<p>Simulations reveal the emergence of opinion leaders who exert significant influence on the network,
shaping market sentiment and trading behaviors</p>
</div>
<div class="canvas-box">
<h4>Information Polarization</h4>
<canvas id="polarizationCanvas" width="400" height="300"></canvas>
<p class="viz-description">Information spreads differently across groups, creating echo chambers and polarized beliefs</p>
<p>Different information signals lead to the formation of distinct groups with divergent beliefs,
creating self-reinforcing feedback loops</p>
</div>
</div>
<h3>Behavioral Polarization Under Rumors</h3>
<div class="figure-container">
<img src="assets/img/vis2.png" alt="Behavioral Polarization" style="max-width: 900px;">
</div>
<p>
Rumors lead to a divergence in user beliefs and the formation of distinct echo chambers. At the same time,
rumors make users more likely to sell, leading to significant increase in sell/buy ratio, and eventually
cause the market to suffer sharp declines.
</p>
<h3>Market Dynamics - Stylized Facts</h3>
<div class="figure-container">
<img src="assets/img/vis4.png" alt="Market Dynamics" style="width: 100%; max-width: 100%;">
</div>
<div class="grid-2">
<div class="card"><h4>📊 Fat-tailed Returns</h4><p>Extreme price movements occur more frequently than normal distribution</p></div>
<div class="card"><h4>📈 Volatility Clustering</h4><p>High volatility periods are followed by high volatility periods</p></div>
<div class="card"><h4>⚖️ Leverage Effect</h4><p>Negative returns correlate with increased future volatility</p></div>
<div class="card"><h4>💹 Volume-Return Relationship</h4><p>Trading volume positively correlates with price changes</p></div>
</div>
<p>
<strong>Emergent Group Behaviors:</strong> The framework reveals self-fulfilling prophecies where collective expectations drive trends,
and information cascades where traders rely on perceived consensus rather than fundamental analysis.
</p>
</section>
<section id="scalability">
<h2>Scalability</h2>
<p>
We test scaling by varying the proportion of activated traders (10%, 20%, 40%, 80%) while keeping other settings fixed.
As activation increases, both average and peak trading volume rise, and prediction errors (RMSE/MAE) decrease.
</p>
<div class="scalability-row">
<div class="left">
<img src="assets/img/scale.png" alt="Scaling law: Performance and activated users">
</div>
<div class="right">
<img src="assets/img/price_index_plot.png" alt="Simulation result of 1,000 agents">
</div>
</div>
<p>
At larger scales (up to 1,000 agents), TwinMarket still tracks real index dynamics with reasonable fidelity,
showing that the framework remains both behaviorally realistic and computationally tractable as participation grows.
</p>
</section>
<section id="citation">
<h2>How to Cite</h2>
<p>If you use TwinMarket in your research, please cite the following paper:</p>
<div class="citation-wrap">
<button type="button" class="btn-copy" id="copy-bibtex" aria-label="Copy BibTeX">Copy</button>
<pre><code id="bibtex-code">@inproceedings{yang2025twinmarket,
title={TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets},
author={Yuzhe Yang and Yifei Zhang and Minghao Wu and Kaidi Zhang and
Yunmiao Zhang and Honghai Yu and Yan Hu and Benyou Wang},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS)},
year={2025},
url={https://arxiv.org/abs/2502.01506},
}</code></pre>
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