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

Federico Vittorio Cortesi, Giuseppe Iannone, Giulia Crippa, Tomaso Poggio and Pierfrancesco Beneventano

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Abstract: Neural networks applied to financial time series operate in a regime of underspecification, where model predictors achieve indistinguishable out-of-sample error. Using large-scale volatility forecasting for S$\&$P 500 stocks, we show that different model-training-pipeline pairs with identical test loss learn qualitatively different functions. Across architectures, predictive accuracy remains unchanged, yet optimizer choice reshapes non-linear response profiles and temporal dependence differently. These divergences have material consequences for decisions: volatility-ranked portfolios trace a near-vertical Sharpe-turnover frontier, with nearly $3\times$ turnover dispersion at comparable Sharpe ratios. We conclude that in underspecified settings, optimization acts as a consequential source of inductive bias, thus model evaluation should extend beyond scalar loss to encompass functional and decision-level implications.

Date: 2026-03
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets and nep-for
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