FinAbstain framework lets LLMs abstain from uncertain financial forecasts

Large language models can confidently generate financial narratives even when data is contradictory or stale—a dangerous flaw for forecasting. The new FinAbstain framework, presented by Torres, Cheng, and Huang, solves this with uncertainty-calibrated multimodal retrieval-augmented generation. It employs a point-in-time retriever that ensures only information available at the forecast timestamp is used. Five specialized agents—fundamental, news, technical, risk, and verification—each weigh modality-specific evidence, then their probabilistic outputs are combined using retrieval relevance, evidence contradiction, sample consistency, and historical calibration stats. The system evaluates multiple calibration methods (temperature scaling, isotonic regression, conformal prediction, and a new hybrid score) under a chronological protocol.

When uncertainty exceeds a validated threshold, FinAbstain abstains—either requesting more evidence, reducing exposure, or routing to human review. The evaluation covers one- and five-day abnormal return direction, twenty-day volatility intervals, and abstention decisions, measuring accuracy, calibration, risk-coverage, trading, latency, and cost. Though results are simulated (no full data collection yet), they illustrate the intended hypothesis: calibrated abstention can trade coverage for lower selective error and drawdown. The paper contributes a time-safe architecture, a composite uncertainty formulation, and a reproducible blueprint for evidence-grounded selective financial forecasting.