When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments
arXiv:2407.18957v5 Announce Type: replace-cross Abstract: Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fundamentals, and global events)? These factors, which frequently influence trading behaviors, are critical elements in the quest for maximizing investors' profits. Our work attempts to solve this problem through large language model based agents. We have developed a multi-agent AI system called StockAgent, driven by LLMs, designed to simulate investors' trading b
The rapid advancement and sophistication of large language models are enabling their application to complex, real-world financial simulations, moving beyond theoretical models to practical agent-based systems.
This development indicates a significant step towards autonomous AI agents directly influencing or participating in financial markets, impacting traditional analytical roles and potentially market dynamics.
The ability of AI systems to simulate and interact with financial markets in a 'real-world' manner changes how market analysis, strategy development, and risk assessment can be performed.
- · AI development firms
- · Quantitative hedge funds
- · High-frequency trading firms
- · Early adopters of AI agents in finance
- · Traditional equity research analysts
- · Brokerages reliant on human expertise
- · Individual investors without advanced tools
Increased efficiency and speed in financial market analysis and strategy generation.
Potential for new forms of market volatility or stability driven by AI agent interactions and collective behaviors.
Regulatory challenges and ethical concerns arise regarding autonomous AI trading and market manipulation.
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Read at arXiv cs.AI