Snapshots of Graph Neural Networks in Asset and Investment Management: An Overview for Practitioners

Quickly apply original, key PMR-published papers with Snapshots—a short article companion that distills PMR research into compressed, digestible takeaways, so you can put the paper’s core ideas to work in your investment process—fast. This Snapshot article is based on research arguing that graph neural networks (GNNs) are a machine-learning approach that extends factor models and standard deep learning by learning from asset relationships, but their practical value depends on economically meaningful, point-in-time graphs and disciplined validation.

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Publication Details

Journal
Practical Applications
Published
2026-09-01
DOI
https://doi.org/10.3905/snp.2026.jpm.050
Primary Topic
Stock Market Forecasting Methods
Type
article
Field-Weighted Citation Impact
0.00
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article

Snapshots of Graph Neural Networks in Asset and Investment Management: An Overview for Practitioners

Derived from original PMR research written by Nino Antulov-Fantulin and Petter N. Kolm using AI and an editor
Practical Applications
Stock Market Forecasting Methods
article

Snapshots of Graph Neural Networks in Asset and Investment Management: An Overview for Practitioners

Derived from original PMR research written by Nino Antulov-Fantulin and Petter N. Kolm using AI and an editor
article en

Abstract

Quickly apply original, key PMR-published papers with Snapshots—a short article companion that distills PMR research into compressed, digestible takeaways, so you can put the paper’s core ideas to work in your investment process—fast. This Snapshot article is based on research arguing that graph neural networks (GNNs) are a machine-learning approach that extends factor models and standard deep learning by learning from asset relationships, but their practical value depends on economically meaningful, point-in-time graphs and disciplined validation.

Practical Applications
Openalex Percentile: Top 6%
Stock Market Forecasting Methods
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