金融预测中，最佳标签未必是最终目标，而是随市场动态变化的中间信号。
The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting
- 提出双层优化框架，自动寻找最优代理标签
- 实验证明在大规模数据集上显著优于传统方法
- 适合关注标签设计的金融建模研究者
尽管深度学习已通过复杂架构革新了金融预测，但监督信号的设计却很少受到审视。本文挑战了训练标签必须严格匹配推理目标的普遍假设，揭示了‘标签时序悖论’：最优监督信号往往偏离预测目标，随市场动态在中间时序上迁移。理论分析表明，这一现象源于动态信号-噪声权衡，泛化能力取决于边际信号实现与噪声累积之间的竞争。为实现此洞察，我们提出一种双层优化框架，可在单次训练中自主识别最优代理标签。大规模金融数据集上的实验显示，该方法持续优于传统基线，为金融预测中的标签中心研究开辟了新路径。
原文摘要 · Abstract (English)
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.
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