Knowing When to Abstain: Risk-Aware Criteria for Action-Level Selectivity in Trading Agents

Despite continuous performance improvements of large language models, unreliable outputs may still be generated due to the inherent uncertainty of the models. For LLM-based trading agents，unreliable and useful actions may coexist within trading recommendations, which can lead to poor portfolio returns and elevated risks. One solution is to abandon those high-risk trading actions. For this purpose, Risk-Aware Action Selective Execution Criterion (RAASEC) is proposed. Three metrics are adopted to evaluate the impact of model uncertainty on each action, and two additional metrics are adopted to assess the market risk. The criterion ensures that only low-risk actions are selected for execution via an adaptive risk–coverage balancing strategy.Backtesting on the challenging China A-share market demonstrates that endowing agents with the ability to abstain from high-risk actions effectively enhances portfolio returns and outperforms the sort-of-states methods. Experiments also verify the complementarity of the five metrics.