arXiv：cs.LG· Yizhi Luo, Jiahe Yi, Jianhui Zhang, Shuo Sun·· 4 小时前AI 评分28
Stock-JEPA：股票市场中先验锚定的潜在修正表征学习
STOCK-JEPA: Prior-Anchored Latent Revision Representation Learning in Equity Markets
AI 导读
Stock-JEPA 提出一种联合嵌入预测框架，通过低复杂度金融模型生成多周期收益与风险统计作为先验锚点，再由上下文条件修正预测器估计未来表征相对锚点的可预测位移。理论上证明最优修正可将先验锚点对同一未来表征的期望平方误差降低 E[‖Δ‖₂²]，实验中在中国和美国大规模股票池的 5 项关键指标上超越 13 个强基线。
正文
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 $\mathbb{E}[\|\boldsymbol{\Delta}\|_2^2]$. 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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.07006 [cs.LG] |
| (or arXiv:2610.07006v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07006 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yizhi Luo [view email]
[v1]
Sun, 4 Oct 2026 12:42:27 UTC (6,495 KB)
来源：arXiv：cs.LG · arxiv.org