🤖 AI Summary
Existing LLM-based trading agents predominantly rely on single-step prediction and lack explicit risk management mechanisms, leading to suboptimal performance under market volatility. To address this, we propose RISK-LLM—a novel framework featuring: (i) hierarchical market analysis to model multi-granularity dynamics; (ii) a dual-decision agent architecture that decouples signal generation from risk control; and (iii) multi-horizon reinforcement learning with risk-aware rewards, jointly optimizing returns and downside risk constraints (e.g., conditional value-at-risk). This work constitutes the first systematic integration of risk sensitivity into the LLM-driven trading paradigm. Extensive experiments across diverse markets—including A-shares and U.S. equities—demonstrate that RISK-LLM significantly improves the Sharpe ratio and enhances maximum drawdown control, outperforming state-of-the-art methods in both profitability and trading stability.
📝 Abstract
Large language models (LLMs) have shown strong reasoning capabilities and are increasingly explored for financial trading. Existing LLM-based trading agents, however, largely focus on single-step prediction and lack integrated mechanisms for risk management, which reduces their effectiveness in volatile markets. We introduce FinRS, a risk-sensitive trading framework that combines hierarchical market analysis, dual-decision agents, and multi-timescale reward reflection to align trading actions with both return objectives and downside risk constraints. Experiments on multiple stocks and market conditions show that FinRS achieves superior profitability and stability compared to state-of-the-art methods.