A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting
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Swiss finance professionals may find relevance in a recent study on cross-market return forecasting, which employed a bipartite graph approach to analyze t
A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting
Swiss finance professionals may find relevance in a recent study on cross-market return forecasting, which employed a bipartite graph approach to analyze the U.S.-China equity markets. This framework, utilizing machine learning, preserves economic structure and captures time-ordered predictive linkages between stocks across markets. The method, which involves rolling-window hypothesis testing, could potentially inform Swiss investors' decisions on global portfolio diversification and risk management. The study's emphasis on sparse, economically interpretable features may also be of interest to Swiss fintech companies exploring AI-driven investment strategies.
Source
Original Article: A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting
Published: March 11, 2026
Author: Jing Liu
This article was automatically aggregated from ArXiv Computational Finance for informational purposes. Summary written by AI.
References
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Original Source
This article is based on A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting (ArXiv Computational Finance)
