AI RESEARCH
A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting
arXiv CS.LG
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ArXi:2603.10559v1 Announce Type: new 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.