  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.
