Problem
Research questions and friction points this paper is trying to address.
financial markets
sequential policy
large language model (LLM) agents
directional structure
predictable policy
RetailAgent: Structured Adverse Timing in Self-Conditioned Multimodal LLM Trading Agents
🤖 AI Summary
本文通过RetailAgent框架研究了大语言模型在交易中的可预测性问题，并发现这些模型的决策存在稳定的负面时机结构。
📝 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.
🔎 Similar Papers
No similar papers found.
