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Tangency portfolios using graph neural networks.

Bin Liu1, Haolong Li1, Linshuang Kang2

  • 1Center of Statistical Research, School of Statistics and Data Science, Southwestern University of Finance and Economics, Chengdu, 610000, Sichuan, China.

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|September 11, 2025
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Summary
This summary is machine-generated.

This study introduces a novel method using Graph Neural Networks (GNNs) to improve financial portfolio optimization. By analyzing industry supply chains, it enhances the estimation of expected returns and covariance matrices for better portfolio performance.

Keywords:
GNNIndustry chainTangency portfolios

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Area of Science:

  • Quantitative Finance
  • Computational Finance
  • Network Science

Background:

  • Modern portfolio theory relies on accurate estimation of expected returns and covariance matrices.
  • Estimating these parameters is challenging, particularly with a large number of assets.
  • Traditional methods often overlook inter-company relationships crucial for market dynamics.

Purpose of the Study:

  • To propose a novel method for estimating tangency portfolio weights by incorporating industry chain relationships.
  • To enhance the accuracy of covariance matrix estimation using Graph Neural Networks (GNNs).
  • To improve portfolio return and Sharpe ratio prediction through advanced estimation strategies.

Main Methods:

  • Utilizing Graph Neural Networks (GNNs) to aggregate stock features based on industry chain graphs.
  • Estimating expected returns and covariance matrices from aggregated stock features.
  • Implementing dynamic modularity calculation for clustered correlation structures.
  • Applying historical ranking regularization to expected returns.

Main Results:

  • The proposed GNN-based method effectively incorporates industry chain information.
  • Dynamic modularity constraint successfully imposes a clustered structure on estimated correlations.
  • Historical ranking regularization enhances expected return estimation.
  • The approach demonstrated superior prediction of portfolio returns and Sharpe ratios on daily stock datasets.

Conclusions:

  • Integrating industry chain data via GNNs significantly improves financial portfolio optimization.
  • The proposed estimation strategies enhance model efficiency and predictive accuracy.
  • This research offers a robust framework for more reliable portfolio management in complex markets.