标题： 大型语言模型能交易吗？ 在市场模拟中使用LLM代理测试金融理论 显示英文标题

标题： Can Large Language Models Trade? Testing Financial Theories with LLM Agents in Market Simulations

摘要： 本文提出一个现实的模拟股票市场，其中大型语言模型（LLMs）作为异构竞争交易代理。 开源框架结合了持续的订单簿，包括市场订单和限价订单、部分成交、股息和均衡清算，同时代理具有不同的策略、信息集和资产。 代理使用结构化输出和函数调用提交标准化决策，同时以自然语言表达其推理过程。 有三个发现：首先，LLMs表现出一致的策略遵循性，可以根据指令充当价值投资者、动量交易者或做市商。 其次，市场动态表现出真实金融市场的特征，包括价格发现、泡沫、反应不足和战略性流动性提供。 第三，该框架能够分析LLMs对不同市场条件的响应，类似于机器学习可解释性中的部分依赖图。 该框架允许在没有闭式解的情况下模拟金融理论，创建用人参与会很昂贵的实验设计，并确定提示如何生成影响市场稳定性的相关行为。 显示英文摘要

摘要： This paper presents a realistic simulated stock market where large language models (LLMs) act as heterogeneous competing trading agents. The open-source framework incorporates a persistent order book with market and limit orders, partial fills, dividends, and equilibrium clearing alongside agents with varied strategies, information sets, and endowments. Agents submit standardized decisions using structured outputs and function calls while expressing their reasoning in natural language. Three findings emerge: First, LLMs demonstrate consistent strategy adherence and can function as value investors, momentum traders, or market makers per their instructions. Second, market dynamics exhibit features of real financial markets, including price discovery, bubbles, underreaction, and strategic liquidity provision. Third, the framework enables analysis of LLMs' responses to varying market conditions, similar to partial dependence plots in machine-learning interpretability. The framework allows simulating financial theories without closed-form solutions, creating experimental designs that would be costly with human participants, and establishing how prompts can generate correlated behaviors affecting market stability.