arXiv cs.AI· Yupeng Zhang, Liuyuan Jiang, Hongyi Huang, Bingheng Li, Lisha Chen·· 3 天前AI 评分22
RetailAgent：自条件多模态 LLM 交易智能体中的结构化不利择时
RetailAgent: Structured Adverse Timing in Self-Conditioned Multimodal LLM Trading Agents
AI 导读
研究提出 RetailAgent 框架，让 LLM 观察匿名化日内股价历史与允许状态，在收益揭晓前反复选择做多或空仓。在剔除整体做多比例后，做多与空仓区间的收益对比显示，跨模态、周期、状态与模型族均存在持续的负向择时。打乱已保存的动作序列会显著削弱该效应，说明动作与后续收益的对齐驱动了负分；将自撰记忆注入决策会进一步加剧策略持续性。
正文 · 原文
Abstract:In financial markets, a sequential policy that reacts systematically to price movements may become predictable to other market participants. This paper studies whether large language model (LLM) agents exhibit such directional structure through RetailAgent, an experimental framework in which an LLM observes anonymized intraday equity price histories and permitted state, then repeatedly chooses long (hold the stock) or flat (stay out) before the subsequent interval return is revealed. We compare returns during long and flat intervals along the same stock's intraday path after removing the overall fraction of long decisions. This exposure-matched measure reveals persistent negative timing across modality, horizon, state, and model family. Shuffling saved action sequences substantially attenuates the effect, showing that alignment between actions and subsequent returns drives the negative score. Feeding self-authored memories into decisions further increases policy persistence, while timing becomes more negative among stock-days on which the agent uses both actions. These results reveal stable, recoverable directional structure in sequential LLM financial decisions and a behavioral signal for studying how another participant could respond to a predictable policy.
| Subjects: | Artificial Intelligence (cs.AI); Trading and Market Microstructure (q-fin.TR) |
| Cite as: | arXiv:2608.28399 [cs.AI] |
| (or arXiv:2608.28399v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.28399 arXiv-issued DOI via DataCite |
Submission history
From: Yupeng Zhang [view email]
[v1]
Fri, 28 Aug 2026 14:53:08 UTC (2,109 KB)
[v2]
Fri, 9 Oct 2026 20:01:15 UTC (2,109 KB)
来源：arXiv cs.AI · arxiv.org