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Updated: Jul 12, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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Attention based dynamic graph neural network for asset pricing.

Ajim Uddin1, Xinyuan Tao1, Dantong Yu1

  • 1Martin Tuchman School of Management, New Jersey Institute of Technology, 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102, USA.

Global Finance Journal
|November 1, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces a new graph neural network model for asset pricing, effectively predicting equity returns by analyzing firm networks and market dynamics. The model enhances portfolio performance and captures key market events.

Area of Science:

  • Quantitative Finance
  • Machine Learning
  • Network Science

Background:

  • Firm networks significantly influence asset pricing.
  • Existing models often overlook dynamic inter-firm relationships.

Purpose of the Study:

  • To develop a novel graph neural network (GNN) model for asset pricing.
  • To integrate dynamic network structures and firm information for improved predictions.
  • To assess the model's efficacy in return prediction and portfolio optimization.

Main Methods:

  • Utilized a graph attention mechanism to learn dynamic equity market network structures.
  • Employed a recurrent convolutional neural network (CNN) for information diffusion within learned networks.
  • Combined graph neural networks with recurrent and convolutional components for end-to-end asset pricing.
Keywords:
Asset pricingC33C52C63FinTechFinancial networkG10G14Graph convolutional neural networksMachine learningNeural network

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Main Results:

  • The proposed GNN model effectively predicts equity returns.
  • Significant improvements in portfolio performance were observed.
  • The model demonstrated robust and persistent performance across various tests and simulated data.
  • Learned dynamic networks accurately reflected major market events.

Conclusions:

  • The novel GNN model successfully captures market network structures and dynamic comovements.
  • This approach offers valuable insights for investors and regulators.
  • The model provides a powerful tool for understanding and predicting asset prices in complex market environments.