用图模型发现中美股市间单向预测关系，提升预测可解释性。
A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting
- 构建双向股市时序图，通过滚动检验筛选跨市场预测边。
- 美国前日收盘至当日收盘回报能显著预测中国日内回报。
- 适合关注跨市场联动与可解释机器学习的量化研究者。
本文通过保留经济结构的机器学习框架，研究中美股市间的跨市场收益可预测性。利用两国股市交易时段不重叠的特点，构建有向二分图以捕捉跨市场股票间的时间有序预测关系。边通过滚动窗口假设检验选择，生成稀疏且具有经济可解释性的特征筛选层，用于下游机器学习模型预测开盘至收盘收益。采用多种正则化与集成方法，利用滞后外国市场信息进行预测。结果揭示显著的方向不对称性：美国前一日收盘至当日收盘的回报对中国的日内回报具有显著预测能力，而反向效应较弱。该信息不对称带来实质性的绩效差异，凸显结构化机器学习框架在揭示跨市场依赖关系的同时保持可解释性的优势。
原文摘要 · Abstract (English)
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.
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