The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting
This study addresses a fundamental limitation in traditional financial forecasting, where supervised labels are assumed to strictly align with the prediction target, thereby constraining model generalization. The authors introduce the concept of the “label temporal paradox,” demonstrating that optimal supervisory signals need not coincide with the target horizon. To resolve this, they propose a bilevel optimization framework that dynamically balances signal-to-noise ratios to automatically discover the optimal intermediate temporal proxy label within a single training pass. This approach eliminates the need for multiple rounds of training while significantly enhancing predictive performance. Extensive experiments on large-scale financial datasets show that the method consistently outperforms existing baselines, underscoring the critical role of supervisory label design in improving model generalization.