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.

翻译：尽管深度学习通过复杂架构革新了金融预测领域，但监督信号本身的设计却鲜受审视。我们挑战了训练标签必须严格匹配预测目标这一经典假设，揭示了标签视野悖论：最优监督信号往往偏离最终预测目标，其最优形态会随着由市场动态支配的中间视野而转移。我们从动态信噪权衡的角度为这一现象提供了理论依据，证明泛化性能取决于边际信号实现与噪声累积之间的竞争关系。为实践这一洞见，我们提出了一个双层优化框架，能够在单次训练运行中自主识别最优代理标签。基于大规模金融数据集的广泛实验表明，该方法相较于传统基线模型取得了持续改进，从而为金融预测中以标签为中心的研究开辟了新途径。