对LLM代理的行为一致性验证：通过股票市场模拟分析交易风格切换
Behavioral Consistency Validation for LLM Agents: An Analysis of Trading-Style Switching through Stock-Market Simulation
AI总结：
本文通过股票市场模拟分析交易风格切换，评估LLM代理策略切换与金融理论的一致性，提出四个行为金融驱动因素作为性格特质，并通过Mann-Whitney U检验比较策略切换行为与理论的一致性。
摘要：
近期的研究越来越多地将大型语言模型（LLMs）作为金融股票市场模拟中的代理，以测试微观层面行为是否能聚合为宏观层面的现象。然而，一个关键问题浮现：LLM代理的行为是否与真实市场参与者一致？这种一致性对于模拟结果的有效性至关重要。为此，我们选择一个金融股票市场场景来检验行为一致性。投资者通常被分类为基本面或技术面交易者，但大多数模拟在初始化时固定策略，未能反映现实中的交易动态。在本研究中，我们评估代理策略切换是否符合金融理论，提供了一个评估框架。我们将四个行为金融驱动因素——损失厌恶、从众、财富分化和价格错配——作为通过提示设置并长期存储的性格特质。在一年的模拟中，代理处理每日价格-成交量数据，按照指定风格交易，并在每10个交易日重新评估策略。我们引入了四个一致性指标，并使用Mann-Whitney U检验比较代理的风格切换行为与金融理论。我们的结果表明，最新LLM的切换行为仅部分符合行为金融理论，凸显了进一步改进代理行为与金融理论一致性的必要性。
英文摘要：
Recent works have increasingly applied Large Language Models (LLMs) as agents in financial stock market simulations to test if micro-level behaviors aggregate into macro-level phenomena. However, a crucial question arises: Do LLM agents' behaviors align with real market participants? This alignment is key to the validity of simulation results. To explore this, we select a financial stock market scenario to test behavioral consistency. Investors are typically classified as fundamental or technical traders, but most simulations fix strategies at initialization, failing to reflect real-world trading dynamics. In this work, we assess whether agents' strategy switching aligns with financial theory, providing a framework for this evaluation. We operationalize four behavioral-finance drivers-loss aversion, herding, wealth differentiation, and price misalignment-as personality traits set via prompting and stored long-term. In year-long simulations, agents process daily price-volume data, trade under a designated style, and reassess their strategy every 10 trading days. We introduce four alignment metrics and use Mann-Whitney U tests to compare agents' style-switching behavior with financial theory. Our results show that recent LLMs' switching behavior is only partially consistent with behavioral-finance theories, highlighting the need for further refinement in aligning agent behavior with financial theory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
