A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting
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摘要
We propose a directed bipartite-graph framework that screens time-ordered cross-market predictors (via rolling-window t-statistic thresholding) and feeds the sparse graph-selected features into a suite of machine-learning models to forecast next-session open-to-close returns; the approach uncovers a pronounced directional asymmetry — U.S. previous-close-to-close information is substantially more predictive for Chinese intraday returns than the reverse — and combining graph-based selection with cross-market features materially improves out-of-sample Sharpe Ratios across many models [page::9][page::2][page::22][page::28].
