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

Paper: arXiv 2408.01499

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.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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

Cited References (432)

1. UK List of \TeX Frequently Asked Questions (2019)
2. The \textsf amsart , \textsf amsproc , and \textsf amsbook document~classes (2004)
3. a lgorithm2e.sty—package for algorithms (2015)
4. The algorithms bundle (2009)
5. The Listings Package (2015)
6. Publication quality tables in \LaTeX (2005)
7. ACM Visual Identity Standards (2007)
8. The subcaption package (2013)

2 of 432 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.