FinBench: Time-Gated Calibration and Uncertainty Benchmarking for Agentic Financial Forecasting
Large language models (LLMs) are increasingly used as components of agentic systems that observe, plan, and act. In finance, even "assistive" systems become decision-relevant once their outputs are used to size trades or allocate risk. A key failure mode is the confidence--competence gap: a model that is only slightly better than chance but consistently overconfident will, under typical bet-sizing rules, generate negative long-run growth. Existing benchmarks emphasize semantic understanding or point accuracy, but do not directly test probabilistic calibration under the temporal constraints and non-stationarity that define real markets. We introduce FinBench, a benchmark designed to evalua...
| Authors | Rishab Ghosh, Vinay Devarakonda |
|---|---|
| Primary category | stat.AP |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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