OpenAlex ·
Ordinal Gates, Cardinal Bets: Matching LLM Confidence to the Financial Decision Operator
- Published
- Source
- OpenAlex
- Published in
- arXiv (Cornell University)
- DOI
- 10.48550/arxiv.2609.00187
- Original
- Read it at the publisher
- Topics
Abstract
LLM confidence scores are not independently deployable objects: their decision value depends on the downstream operator and exposure controller that consume them. Monotone recalibration cannot change a coverage-matched rank-based gate, whereas position sizing consumes score magnitude, so changing a confidence map can invalidate a scale fitted to the previous score distribution. We test this on FactSet news for Nasdaq-100 equities, fitting maps and scales on 2021 and evaluating nine open-weight LLMs out-of-sample on 2022--2023. Cross-applying raw and correctness maps with independently fitted scales shows that the two components are not portable alone: scale transfer reduces certainty-equivalent return (CER) in $8/9$ models and produces large risk-target errors. Matching each map with its fitted scale improves ensemble CER by $9.2$ percentage points per year under frozen-scale control ($p<0.001$), and the effect remains significant when the single largest-contributing model is excluded ($+5.5$pp/yr), so it is not driven by one case. Under an identical adaptive-volatility controller, however, the incremental effect falls to $+1.6$pp/yr, with a significant controller interaction. Annual walk-forward effects are smaller, although map--scale interaction remains positive in every fold. Confidence transformations should therefore be evaluated jointly with the downstream controllers that consume them.
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