A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915935831457792 |
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| author | Liu, Jing Grith, Maria Dong, Xiaowen Cucuringu, Mihai |
| author_facet | Liu, Jing Grith, Maria Dong, Xiaowen Cucuringu, Mihai |
| contents | This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-overlapping trading hours of the U.S. and Chinese equity markets, we construct a directed bipartite graph that captures time-ordered predictive linkages between stocks across markets. Edges are selected via rolling-window hypothesis testing, and the resulting graph serves as a sparse, economically interpretable feature-selection layer for downstream machine learning models. We apply a range of regularized and ensemble methods to forecast open-to-close returns using lagged foreign-market information. Our results reveal a pronounced directional asymmetry: U.S. previous-close-to-close returns contain substantial predictive information for Chinese intraday returns, whereas the reverse effect is limited. This informational asymmetry translates into economically meaningful performance differences and highlights how structured machine learning frameworks can uncover cross-market dependencies while maintaining interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10559 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting Liu, Jing Grith, Maria Dong, Xiaowen Cucuringu, Mihai Machine Learning Computational Finance This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-overlapping trading hours of the U.S. and Chinese equity markets, we construct a directed bipartite graph that captures time-ordered predictive linkages between stocks across markets. Edges are selected via rolling-window hypothesis testing, and the resulting graph serves as a sparse, economically interpretable feature-selection layer for downstream machine learning models. We apply a range of regularized and ensemble methods to forecast open-to-close returns using lagged foreign-market information. Our results reveal a pronounced directional asymmetry: U.S. previous-close-to-close returns contain substantial predictive information for Chinese intraday returns, whereas the reverse effect is limited. This informational asymmetry translates into economically meaningful performance differences and highlights how structured machine learning frameworks can uncover cross-market dependencies while maintaining interpretability. |
| title | A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting |
| topic | Machine Learning Computational Finance |
| url | https://arxiv.org/abs/2603.10559 |