arXiv cs.AI· Yuqi Wang, Fengyuan Liu, Mukai Li, Haochen Luo, Lei Li, Qi Liu·· 10 小时前AI 评分31
Market-Conditioned JEPA：从不可预测的市场冲击中学习
Market-Conditioned JEPA: Learning from Unpredictable Market Shocks
AI 导读
研究者提出 Market-Conditioned JEPA（MC-JEPA），一种面向金融时间序列的联合嵌入预测架构，训练时用其他资产的未来目标嵌入为每个资产构造留一市场条件，并通过资产自身表征导出的因子载荷注入。
正文 · 原文
Abstract:Much of the variation in financial time series is driven by market-wide shocks that cannot be predicted from an asset's own history. Per-asset self-supervised learning treats this variation as irreducible noise. Under a latent factor model, we show that conditioning prediction on the realized market factor makes the asset's market exposure necessary for optimal prediction, while reducing the noise against which the asset-specific component is learned. Building on this insight, we introduce Market-Conditioned JEPA (MC-JEPA), a joint-embedding predictive architecture for financial time series. During training, we construct a leave-one-out market condition for each asset from the future target embeddings of other assets and incorporate it through factor loadings derived from the asset's own representation. On 11 years of minute-level Chinese A-share data, MC-JEPA improves over per-asset JEPA across nine targets spanning returns, market exposure, risk, and liquidity, with the strongest and most statistically significant gains in exposure, risk, and liquidity. It achieves the best RankIC on all liquidity targets and on return horizons from two hours to five days, outperforming time-series foundation models and self-supervised baselines, and delivers the best portfolio performance in a historical backtest. Ablations show that the return and exposure gains require the condition to be aligned with the realized market movement. After restoring the volatility scale removed by normalization, the predictor's factor loadings rank stocks by market beta nearly as well as a supervised probe, without labels. A synthetic factor-model study with known parameters shows that conditioning improves the recovery of both market exposure and the asset-specific component.
| Comments: | 23 pages, 4 figures, 14 tables |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE) |
| Cite as: | arXiv:2611.13572 [cs.LG] |
| (or arXiv:2611.13572v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13572 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuqi Wang [view email]
[v1]
Tue, 10 Nov 2026 04:40:04 UTC (300 KB)
来源：arXiv cs.AI · arxiv.org