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TwinMarket: A Scalable LLM-Powered Simulation of Financial Markets

A new multi-agent framework, TwinMarket, leverages large language models (LLMs) to simulate the complex interplay between individual investor behavior and emergent market dynamics. Developed by researchers at the Chinese University of Hong Kong, Shenzhen, and Nanjing University, the system offers a more nuanced and scalable approach to modeling financial markets than traditional agent-based models (ABMs). Unlike rule-based ABMs that often oversimplify human behavior, TwinMarket employs LLMs to create agents with diverse characteristics, cognitive biases, and emotional responses, resulting in more realistic simulations.

The Power of LLMs in Simulating Human Behavior:

Traditional ABMs often rely on pre-defined rules, making it difficult to capture the richness and complexity of human decision-making in financial markets. TwinMarket overcomes this limitation by using LLMs to generate individual investor profiles complete with demographic data (e.g., age, location), investment styles (e.g., value investing, technical analysis), and behavioral biases (e.g., overconfidence, loss aversion, herding). For example, one agent might be programmed as a risk-averse investor who always prefers stable, long-term investments, while another could be modeled as a more impulsive trader prone to chasing trends. These LLMs also provide a more dynamic information flow than traditional ABMs. This is crucial in reflecting the impacts of social interactions and information cascades seen in real markets.

Simulating Market Dynamics:

TwinMarket simulates a dynamic stock market environment where agents interact through a simulated social media platform. These agents independently make investment decisions, driven by their individual profiles, beliefs (including about market conditions, news, and social sentiment), and access to information (including news articles, market data, and posts from other agents). Through these interactions, emergent phenomena such as financial bubbles and market crashes can arise organically. The researchers demonstrated this by running simulations in a controlled environment.

Key Features and Findings:

  • Real-world alignment: The model is grounded in established behavioral finance theories and is calibrated using real-world data from platforms like Xueqiu, a Chinese social investing platform.
  • Dynamic interaction modeling: The LLM-based framework enables agents to engage in dynamic information exchange and social influence, capturing behaviors like herding, where many investors make the same trades at the same time and often rapidly, leading to increased market volatility.
  • Scalable market simulations: The framework is designed to be scalable, allowing researchers to simulate markets with a larger number of agents, leading to a richer representation of market dynamics. Simulations with 1000 agents produced qualitatively similar outcomes to smaller simulations.
  • Reproducing stylized facts: The simulations successfully replicated key empirical observations in financial markets, including fat-tailed return distributions, the leverage effect, and volatility clustering—all important features of real markets often missing in simpler ABMs.
  • Emergence of group behaviors: The researchers found that information cascades, amplified by social interactions and opinion leaders, can significantly influence market dynamics, contributing to dramatic price swings.

Implications and Future Work:

TwinMarket provides a valuable tool for researchers and practitioners to study the complex interplay between micro-level individual behaviors and macro-level market phenomena. The ability to simulate the impact of diverse agent characteristics and information cascades provides new insights into the causes of market fluctuations and the dynamics of collective behavior in financial markets. Future work will explore the application of TwinMarket to other socio-economic systems and further investigate the dynamics of agent trust and the parallels between LLMs and human cognition.