UGapSyn: Utility-Gap Synthesis for Adapting Financial Time-Series Foundation Models

Synthetic price paths can supplement the limited market history used to adapt time-series foundation models to financial forecasting, and generators are usually judged by how realistic these paths are. Under a fixed adaptation budget, however, each synthetic window replaces a real one, so even a realistic path can hurt the adapted model if the displaced real window was more useful. To capture this trade-off, we define the utility gap as the reduction in forecasting risk relative to the same adaptation without replacement. Because this gap depends on the model being adapted and on the real windows being replaced, it must be measured by adapting the same model with and without the replacement. Utility-Gap Synthesis (UGapSyn) makes this comparison by continuing adaptation from a shared state with synthetic or real windows at the replaced positions, and selects the configuration of a controllable GARCH-jump generator by the gap measured on validation data. During adaptation, it adjusts the amount and kind of synthetic data only when repeated measurements on historical data agree. With three foundation models on two US equity panels and one Chinese panel, UGapSyn achieves lower mean test MSE than real-data adaptation in all nine settings and than all five synthesis and condensation baselines on both US panels for every model. On Chronos-Bolt-Base, it lowers MSE relative to real-data adaptation by 2.4% on US_71 and 2.7% on US_14L.