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Decoding global economic dynamic: A graph-based examination of contemporary ETF markets

Author

Listed:
  • Geng, Ru
  • Zhang, Hong-Kun
  • Gao, Yixian
  • Yuan, Gangnan

Abstract

Analyzing the dynamic interdependencies among global economies is critical for understanding financial risk and opportunity. In this work, we introduce a spatio-temporal attention-based graph neural network that models these complex relationships using Exchange-Traded Fund (ETF) indices from major global markets. Our framework provides a data-driven lens on economic interactions, uncovering how influence shifts between economies like the United States, China, and European nations, particularly during critical financial events such as the COVID-19 pandemic. The model not only reveals interpretable network structures—identifying the U.S. as a dominant global hub and China as a key regional one, but also significantly outperforms baseline methods in predicting future market trends. Ultimately, this study presents a powerful and transparent framework for decoding global economic dynamics and improving financial forecasting.

Suggested Citation

  • Geng, Ru & Zhang, Hong-Kun & Gao, Yixian & Yuan, Gangnan, 2025. "Decoding global economic dynamic: A graph-based examination of contemporary ETF markets," Chaos, Solitons & Fractals, Elsevier, vol. 201(P3).
    • Handle: RePEc:eee:chsofr:v:201:y:2025:i:p3:s0960077925013268
    DOI: 10.1016/j.chaos.2025.117313
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    References listed on IDEAS

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