RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction

ArXiv ID: 2403.02500 “View on arXiv”

Authors: Unknown

Abstract

In recent years, the dynamic factor model has emerged as a dominant tool in economics and finance, particularly for investment strategies. This model offers improved handling of complex, nonlinear, and noisy market conditions compared to traditional static factor models. The advancement of machine learning, especially in dealing with nonlinear data, has further enhanced asset pricing methodologies. This paper introduces a groundbreaking dynamic factor model named RVRAE. This model is a probabilistic approach that addresses the temporal dependencies and noise in market data. RVRAE ingeniously combines the principles of dynamic factor modeling with the variational recurrent autoencoder (VRAE) from deep learning. A key feature of RVRAE is its use of a prior-posterior learning method. This method fine-tunes the model’s learning process by seeking an optimal posterior factor model informed by future data. Notably, RVRAE is adept at risk modeling in volatile stock markets, estimating variances from latent space distributions while also predicting returns. Our empirical tests with real stock market data underscore RVRAE’s superior performance compared to various established baseline methods.

Keywords: Dynamic Factor Model, Variational Recurrent Autoencoder (VRAE), Latent Space Distributions, Probabilistic Modeling, Asset Pricing, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper involves advanced deep learning architecture combining variational inference and recurrent neural networks, resulting in high math complexity; empirical rigor is high due to the use of real stock market data, backtesting against baselines, and detailed methodology for implementation.

  flowchart TD
    A["Research Goal: Predict Stock Returns using Dynamic Factors"] --> B{"Methodology"}
    B --> C["Data: Real Stock Market Data<br/>(Input for Modeling)"]
    C --> D["Computational Process: RVRAE Model<br/>Combines Dynamic Factor Model + VRAE<br/>Uses Prior-Posterior Learning"]
    D --> E["Key Findings/Outcomes"]
    E --> F["1. Superior Prediction Performance<br/>vs. Baseline Methods"]
    E --> G["2. Effective Risk Modeling<br/>Estimates Variances from Latent Space"]
    E --> H["3. Handles Nonlinear & Noisy<br/>Temporal Market Data"]