NeuralFactors: A Novel Factor Learning Approach to Generative Modeling of Equities

ArXiv ID: 2408.01499 “View on arXiv”

Authors: Unknown

Abstract

The use of machine learning for statistical modeling (and thus, generative modeling) has grown in popularity with the proliferation of time series models, text-to-image models, and especially large language models. Fundamentally, the goal of classical factor modeling is statistical modeling of stock returns, and in this work, we explore using deep generative modeling to enhance classical factor models. Prior work has explored the use of deep generative models in order to model hundreds of stocks, leading to accurate risk forecasting and alpha portfolio construction; however, that specific model does not allow for easy factor modeling interpretation in that the factor exposures cannot be deduced. In this work, we introduce NeuralFactors, a novel machine-learning based approach to factor analysis where a neural network outputs factor exposures and factor returns, trained using the same methodology as variational autoencoders. We show that this model outperforms prior approaches both in terms of log-likelihood performance and computational efficiency. Further, we show that this method is competitive to prior work in generating realistic synthetic data, covariance estimation, risk analysis (e.g., value at risk, or VaR, of portfolios), and portfolio optimization. Finally, due to the connection to classical factor analysis, we analyze how the factors our model learns cluster together and show that the factor exposures could be used for embedding stocks.

Keywords: NeuralFactors, Deep generative modeling, Factor analysis, Variational autoencoders, Covariance estimation, Equity

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper involves significant mathematical complexity with deep probabilistic modeling, VAEs, and Student’s T distributions, while also demonstrating strong empirical rigor through extensive backtesting on S&P 500 data, multiple financial metrics (VaR, portfolio optimization), and comparisons to baselines.

  flowchart TD
    A["Research Goal"] --> B["NeuralFactors Model Architecture"]
    A --> C["Dataset: Equity Returns"]
    B --> D["Process: VAE Training"]
    C --> D
    D --> E["Key Outcomes"]
    
    subgraph A ["Research Goal"]
        A1["Enhance Factor Models with<br>Deep Generative Modeling"]
    end

    subgraph B ["Model Architecture"]
        B1["Factor Exposures<br>via Neural Network"]
        B2["Factor Returns<br>via Neural Network"]
    end

    subgraph D ["Computation"]
        D1["Variational Autoencoder<br>Training Methodology"]
    end

    subgraph E ["Outcomes"]
        E1["Superior Log-Likelihood<br>& Efficiency"]
        E2["Validated Applications:<br>Risk & Portfolio Optimization"]
        E3["Factor Interpretability &<br>Stock Embeddings"]
    end