Exploring Sentiment Manipulation by LLM-Enabled Intelligent Trading Agents

TL;DR

This paper investigates how an intelligent trading agent using deep reinforcement learning and large language models can manipulate social media sentiment to influence market dynamics and improve its profits in a simulated environment.

Contribution

It is the first to explore sentiment manipulation by LLM-enabled trading agents controlling social media posts in a financial market simulation.

Findings

The agent learns to optimize profit by manipulating social media sentiment.

Sentiment manipulation significantly impacts market outcomes.

The approach demonstrates potential risks of LLMs in financial decision-making.

Abstract

Companies across all economic sectors continue to deploy large language models at a rapid pace. Reinforcement learning is experiencing a resurgence of interest due to its association with the fine-tuning of language models from human feedback. Tool-chain language models 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 large language model 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.