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

TL;DR

This paper introduces RVRAE, a novel probabilistic dynamic factor model based on variational recurrent autoencoders, designed to improve stock return predictions by capturing complex temporal dependencies and market noise.

Contribution

The paper presents RVRAE, a new deep learning-based dynamic factor model that integrates variational recurrent autoencoders with a prior-posterior learning approach for financial data.

Findings

RVRAE outperforms baseline models in stock return prediction.

The model effectively captures market volatility and temporal dependencies.

Empirical results demonstrate improved risk and return estimation.

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.