HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE

ArXiv ID: 2306.02848 “View on arXiv”

Authors: Unknown

Abstract

Factor model is a fundamental investment tool in quantitative investment, which can be empowered by deep learning to become more flexible and efficient in practical complicated investing situations. However, it is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting, where the model can adapt itself to match the current market regime identified based on only point-in-time market information. To tackle this problem, we propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the underlying relationship between the market situation and stock-wise latent factors, so that HireVAE can effectively estimate useful latent factors given only historical market information and subsequently predict accurate stock returns. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods, verifying the potential of such online and adaptive factor model.

Keywords: Hierarchical Variational Autoencoder (HireVAE), Online Learning, Factor Models, Latent Factors, Adaptive Modeling, Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper introduces advanced deep learning architectures (VAEs, hierarchical latent spaces, regime-switching) with complex stabilization algorithms and online learning frameworks, indicating high mathematical density. While it includes real-world benchmarks and backtests over four stock markets, the summary focuses more on model design and theoretical validation than on detailed data preprocessing, hyperparameter tuning, or full implementation details typically found in production-ready strategies.

  flowchart TD
    A["Research Goal: Develop an online & adaptive factor model for stock prediction?"] --> B{"Data Inputs<br/>Historical Point-in-Time<br/>Market & Stock Data"}
    B --> C["Methodology: HireVAE Architecture"]
    C --> D["Hierarchical Latent Space<br/>(Market to Factor VAEs)"]
    C --> E["Regime-Switching Mechanism<br/>(Adaptive Online Learning)"]
    D & E --> F["Computational Process: Continuous<br/>Market Regime Identification &<br/>Latent Factor Estimation"]
    F --> G["Output: Accurate<br/>Stock Return Predictions"]
    G --> H["Key Findings: Superior<br/>Active Returns vs. Benchmarks"]