Can LLMs manipulate market sentiment?
Exploring Sentiment Manipulation by LLM-Enabled Intelligent Trading Agents
This paper investigates how an LLM-powered reinforcement learning (RL) trading agent can manipulate a simulated stock market by generating social media posts that influence a sentiment-based trading agent. The RL agent learns to optimize its profits by crafting posts that sway market sentiment to its advantage, demonstrating a potential risk of LLMs in financial markets. Key points for LLM-based multi-agent systems include: LLMs can be integrated with RL agents to create autonomous actors capable of complex strategic interactions including generating natural language; social media sentiment can be leveraged by trading agents; and ethical implications arise when LLMs are used for potentially manipulative purposes.