Learning When and How to Trade on Government Policies

Government policies influence financial markets, but supportive language does not necessarily imply profitable trading opportunities. We study when policy information adds value to existing trading strategies and how this value evolves across trading days. We construct PolicyValue-Bench, a point-in-time benchmark aligning 5,125 government policy documents from 2021-2025 with policy histories and market data for five Chinese A-share sector ETFs. We present PolicyState, a stateful gating framework that assesses policy text and LLM-generated judgments in the context of market conditions and retained policy effects. It learns policy admission, opportunity retention, and daily risk release through separate opportunity and risk states that guide exposure increases and cash exits. Training combines matched-control return supervision with sequential trading optimization. In a full-year 2025 out-of-sample evaluation across four base strategies, our method achieves an average net cumulative return of 16.25%, representing relative improvements of 140.4% over matched bases and 54.0% over the strongest comparison method. It improves returns in 19 of 20 sector-strategy combinations.