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  3. Attention Based Dynamic Graph Neural Network For Asset Pricing.
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  3. Attention Based Dynamic Graph Neural Network For Asset Pricing.

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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

View abstract on PubMed

Summary
This summary is machine-generated.

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.

Keywords:
Asset pricingC33C52C63FinTechFinancial networkG10G14Graph convolutional neural networksMachine learningNeural network

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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.

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.