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A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting

Jing Liu, Maria Grith, Xiaowen Dong, Mihai Cucuringu

TL;DR

A directed bipartite graph is constructed that captures time-ordered predictive linkages between stocks across markets and reveals 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.

Abstract

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.

A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting

TL;DR

A directed bipartite graph is constructed that captures time-ordered predictive linkages between stocks across markets and reveals 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.

Abstract

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.
Paper Structure (22 sections, 8 equations, 19 figures)

This paper contains 22 sections, 8 equations, 19 figures.

Table of Contents

  1. Introduction
  2. Related Literature
  3. Cross-Market Analysis and Prediction
  4. Graph Methods in Finance
  5. Machine Learning in Finance
  6. Data
  7. Methodology
  8. Directed Bipartite Graph
  9. Predictive Analysis with Machine Learning
  10. Experiments
  11. Evaluation Metrics
  12. Experimental Setup
  13. Graph-Based Cross-Market Prediction
  14. Baseline
  15. Main Results
  16. ...and 7 more sections

Figures (19)

  • Figure 1: Schematic illustration of the directed bipartite graph linking source-market stocks to target-market stocks based on significant predictive relationships.
  • Figure 2: Example time series of U.S. pvCLCL returns for CDNS and Chinese OPCL returns for 002410.XSHE over the rolling training window. The series are shown for illustrative purposes to highlight cross-market co-movement underlying the detected predictive link.
  • Figure 3: Heatmap of the directed biadjacency matrix for a representative trading day. Rows correspond to Chinese stocks and columns to U.S. stocks, grouped by sector. Each entry represents the t-statistic from the rolling-window regression of Chinese returns on lagged U.S. returns. Colour intensity reflects the magnitude and sign of the predictive relationship.
  • Figure 4: Sector-level heatmap of the absolute median t-statistic in the time-averaged biadjacency matrix of the directed cross-market graph. Rows correspond to Chinese sectors and columns to U.S. sectors. Each entry reports the median absolute predictive strength across all stock pairs within the corresponding sector-by-sector block.
  • Figure 5: The figure shows the 25th, 50th, and 75th percentiles of the in-degree distribution of target nodes by day. US-CN represents the number of U.S. pvCLCL nodes selected to predict Chinese OPCL returns, while CN-US represents the number of Chinese pvCLCL nodes selected to predict U.S. OPCL returns.
  • ...and 14 more figures