Same Error, Different Function: The Optimizer as an Implicit Prior in Financial Time Series

Neural networks applied to financial time series operate in a regime of underspecification, where predictors achieve comparable out-of-sample error. Using large-scale volatility forecasting for S&P 500 stocks—twelve architecture–optimizer configurations, thirteen seeds each, evaluated on 564,795 held-out observations—we show that comparable test loss conceals measurably different predictors. In eleven of twelve comparisons, models trained from identical initialization and identical data order diverge $1.3$ to $7.8$ times further than repeated runs of either optimizer alone, ruling out initialization noise as the explanation. On a common scale, predictions differ by 5–17% of the typical forecast error while the models' error magnitudes differ by less than 0.7% of it. The divergence reshapes non-linear response profiles, temporal dependence, and ultimately decisions: volatility-ranked portfolios trace a Sharpe–turnover frontier with up to 58% turnover dispersion at comparable gross Sharpe ratios. In underspecified settings, optimization is a consequential source of inductive bias, and model evaluation must extend beyond scalar loss to functional and decision-level behavior.