aiqbench analysis
This research challenges the standard practice of aligning training labels directly with inference targets in financial forecasting, proposing instead that optimal supervision often requires shifting across intermediate horizons to balance signal realization against noise accumulation. By introducing a bi-level optimization framework, the authors demonstrate a method to autonomously identify these proxy labels, yielding consistent performance gains over conventional baselines. This work highlights a critical shift toward label-centric design, suggesting that generalization in time-series tasks may depend more on dynamic signal-noise trade-offs than on strict target mirroring.
The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting
arXiv:2602.03395v4 Announce Type: replace 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.
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.
Machine Learning (cs.LG)
arXiv:2602.03395 [cs.LG]
(or arXiv:2602.03395v4 [cs.LG] for this version)
Full text under the publisher's open licence · 135 words
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