The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting

2026-02-03

AI Summary

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.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

While deep learning has revolutionized financial forecasting through sophisticated architectures, the design of the supervision signal itself is rarely scrutinized. We challenge the canonical assumption that training labels must strictly mirror inference targets, uncovering the Label Horizon Paradox: the optimal supervision signal often deviates from the prediction goal, shifting across intermediate horizons governed by market dynamics. We theoretically ground this phenomenon in a dynamic signal-noise trade-off, demonstrating that generalization hinges on the competition between marginal signal realization and noise accumulation. To operationalize this insight, we propose a bi-level optimization framework that autonomously identifies the optimal proxy label within a single training run. Extensive experiments on large-scale financial datasets demonstrate consistent improvements over conventional baselines, thereby opening new avenues for label-centric research in financial forecasting.

Problem

• Label Horizon Paradox
• supervision signal
• financial forecasting
• prediction target
• market dynamics

Innovation

• Label Horizon Paradox
• supervision signal design
• bi-level optimization
• financial forecasting
• signal-noise trade-off