FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Prediction

ArXiv ID: 2502.05218 “View on arXiv”

Authors: Unknown

Abstract

As a fundamental method in economics and finance, the factor model has been extensively utilized in quantitative investment. In recent years, there has been a paradigm shift from traditional linear models with expert-designed factors to more flexible nonlinear machine learning-based models with data-driven factors, aiming to enhance the effectiveness of these factor models. However, due to the low signal-to-noise ratio in market data, mining effective factors in data-driven models remains challenging. In this work, we propose a hypergraph-based factor model with temporal residual contrastive learning (FactorGCL) that employs a hypergraph structure to better capture high-order nonlinear relationships among stock returns and factors. To mine hidden factors that supplement human-designed prior factors for predicting stock returns, we design a cascading residual hypergraph architecture, in which the hidden factors are extracted from the residual information after removing the influence of prior factors. Additionally, we propose a temporal residual contrastive learning method to guide the extraction of effective and comprehensive hidden factors by contrasting stock-specific residual information over different time periods. Our extensive experiments on real stock market data demonstrate that FactorGCL not only outperforms existing state-of-the-art methods but also mines effective hidden factors for predicting stock returns.

Keywords: Factor Models, Hypergraph Learning, Contrastive Learning, Quantitative Investment, Stock Prediction

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper involves advanced mathematics including hypergraph convolutional neural networks (HyperGCN) with detailed matrix operations, non-linear activations, and a novel contrastive learning framework, making it highly mathematically dense. It also demonstrates strong empirical rigor with extensive experiments on real stock market data, comparing against state-of-the-art methods and reporting performance improvements, indicating backtest-ready implementation.

  flowchart TD
    A["Research Goal: Enhance Stock Prediction<br>via Data-Driven Factor Mining"] --> B["Input Data: Historical<br>Stock Returns & Prior Factors"]
    B --> C["Cascading Residual Hypergraph<br>Removes Prior Factor Effects"]
    C --> D["Temporal Residual Contrastive Learning<br>Extracts Hidden Factors from Residuals"]
    D --> E["Hypergraph Integration<br>Captures High-Order Nonlinear Relationships"]
    E --> F["Stock Returns Prediction Model"]
    F --> G["Key Findings: State-of-the-art Performance<br>& Effective Hidden Factors"]