Exploring Sentiment Manipulation by LLM-Enabled Intelligent Trading Agents (2502.16343v1)

Abstract: Companies across all economic sectors continue to deploy LLMs at a rapid pace. Reinforcement learning is experiencing a resurgence of interest due to its association with the fine-tuning of LLMs from human feedback. Tool-chain LLMs control task-specific agents; if the converse has not already appeared, it soon will. In this paper, we present what we believe is the first investigation of an intelligent trading agent based on continuous deep reinforcement learning that also controls a LLM with which it can post to a social media feed observed by other traders. We empirically investigate the performance and impact of such an agent in a simulated financial market, finding that it learns to optimize its total reward, and thereby augment its profit, by manipulating the sentiment of the posts it produces. The paper concludes with discussion, limitations, and suggestions for future work.