arXiv cs.AI· Jiajie He, Yuanhong Jiang, Xintong Chen, Dongling Ni, Wenjin Liu·· 14 小时前AI 评分34
超越行为准确率：评估 LLM 投资者模拟中的个体级保真度
Beyond Behavioral Accuracy: Evaluating Individual-Level Fidelity in LLM Investor Simulation
AI 导读
一项针对 80 名参与者的纵向模拟交易研究发现，所有受测 LLM 在预测交易是否发生上都无法可靠超越简单的近期活跃度持续性基线，买卖结构和资产选择同样难以复现。相似的活跃度表现还可能对应差异显著的组合收益轨迹，消融实验显示活跃度预测主要依赖近期交易，而资产选择对多种证据来源敏感。参与者认为模型解释更贴合其反复出现的策略，而非具体决策理由。
正文 · 原文
Abstract:Large language models (LLMs) are increasingly used as user simulators, but can they faithfully reproduce an individual's behavior and decision logic over time? We investigate this question in a longitudinal paper-trading study with 80 participants. Using rolling next-day predictions, we evaluate trading activity, buy-sell structure, asset selection, and portfolio outcomes, complemented by controlled input ablations and participant-rated explanation alignment. No evaluated LLM reliably outperforms a simple recent-activity persistence baseline for predicting trade occurrence, while buy-sell structure and asset selection remain difficult to reproduce. Similar activity-level performance can also yield substantially different portfolio trajectories. Ablations show that activity predictions depend primarily on recent trades, whereas asset choices are sensitive to multiple evidence sources. Participants rate explanations as more aligned with their recurring strategies than with their decision-specific rationales. These findings reveal a gap between coarse behavioral agreement and individual-level fidelity, motivating separate evaluation of behavioral accuracy, explanation alignment, and evidence dependence in personalized user simulation.
| Comments: | This paper is under review in ARR |
| Subjects: | Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2610.26524 [cs.AI] |
| (or arXiv:2610.26524v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.26524 arXiv-issued DOI via DataCite |
Submission history
From: Jiajie He [view email]
[v1]
Mon, 19 Oct 2026 14:54:08 UTC (3,924 KB)
[v2]
Sat, 24 Oct 2026 01:10:19 UTC (3,924 KB)
来源：arXiv cs.AI · arxiv.org