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STOCK-JEPA: Prior-Anchored Latent Revision Representation Learning in Equity Markets

Yizhi Luo, Jiahe Yi, Jianhui Zhang, Shuo Sun · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.07006 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

学习有效的背景表现有助于预测股票市场信号的结构和动态,从金融数据的低信号到噪音比率。 黑盒深度模型可以捕捉复杂的模式,但可能超过样本噪音,缺乏明确的经济结构。 与此同时,经典的线性金融模型提供可解释的参考,但其过于简化的假设留下非线性信号未被捕获。 为了结合这两个方向的优势,我们提出了Stock-JEPA,一个共同嵌入式的预测框架,该框架学习可预测的额外调整与点在时间内金融预测相关。 首先,我们利用低复杂的金融模型来生成固定的错误统计,汇总了多地平线回报和风险。 然后,一个预测器将这些统计数据

Machine translation (Meta M2M100); the English original follows.

Abstract

Learning effective representations helps characterize the structure and dynamics of equity markets from financial data with a low signal-to-noise ratio. Black-box deep models can capture complex patterns but may overfit sample noise and lack explicit economic structure. Meanwhile, classic linear financial models provide interpretable references, but their oversimplified assumptions leave non-linear signals uncaptured. To combine the strengths of these two directions, we propose Stock-JEPA, a joint-embedding predictive framework that learns predictable incremental revisions relative to a point-in-time financial prior. First, we leverage a low-complexity financial model to produce fixed statistics summarizing multi-horizon return and risk. A prior projector then maps these statistics into the target encoder's latent space as an anchor. Second, we design a context-conditioned revision predictor to estimate the future representation's predictable displacement from the anchor. Separate losses update the two branches: the anchor learns from prior statistics, while the revision captures additional predictable information from historical context. Third, we freeze all representation modules and train a downstream readout, evaluating its forecasts through cross-sectional ranking and portfolio performance. Theoretically, we prove that optimal revision reduces the prior anchor's expected squared error for the same future representation by exactly $\mathbb{E}[\|\boldsymbolΔ\|_2^2]$. This non-negative gain is the expected squared magnitude of the additional signal predictable from historical context. Experimentally, Stock-JEPA outperforms 13 strong baselines across large-scale China and U.S. equity universes on 5 key evaluation metrics. Ablation studies and representation analysis further demonstrate the value of the learned revisions for representation learning in equity markets.

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Topics: Overfitting, Econometrics, Computer science, Leverage (statistics), Representation (politics), Equity (law)

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