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This study explores the enhancement of Graph Neural Networks (GNNs) for stock return prediction through a novel approach of constructing graph networks based on firm-level characteristic similarities. Traditional methods often fail to capture dynamic relationships between firms. By using measures such as Cosine Similarity and Euclidean Distance to create graph edges, this research empirically demonstrates a significant improvement in predictive accuracy, especially when using the Cosine Similarity-based Graph Attention Network. The results indicate that this approach not only outperforms conventional models but also provides a deeper understanding of stock return dynamics by effectively integrating firm characteristics into the graph structure. The implementation is available on GitHub .

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Harnessing Firm Similarities with Graph Neural Networks for Superior Stock Predictions

  • 会议论文

  • 发表日期：

    2025年

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  • Tein Baaijens,
  • Çiçek Güven,
  • Gonzalo Nápoles

摘要

This study explores the enhancement of Graph Neural Networks (GNNs) for stock return prediction through a novel approach of constructing graph networks based on firm-level characteristic similarities. Traditional methods often fail to capture dynamic relationships between firms. By using measures such as Cosine Similarity and Euclidean Distance to create graph edges, this research empirically demonstrates a significant improvement in predictive accuracy, especially when using the Cosine Similarity-based Graph Attention Network. The results indicate that this approach not only outperforms conventional models but also provides a deeper understanding of stock return dynamics by effectively integrating firm characteristics into the graph structure. The implementation is available on GitHub .