Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning

TL;DR

This paper introduces a dynamic generative factor model using Attention-GRU networks for modeling multivariate stock returns, enabling CVaR portfolio optimization with improved risk management and higher reward-risk ratios.

Contribution

It presents a novel dynamic generative model with attention mechanisms for tail-aware dependence modeling and a two-step training algorithm for portfolio optimization.

Findings

Higher reward-risk ratios achieved

Lower tail risks observed

Effective dynamic dependence modeling

Abstract

The dynamic portfolio construction problem requires dynamic modeling of the joint distribution of multivariate stock returns. To achieve this, we propose a dynamic generative factor model which uses random variable transformation as an implicit way of distribution modeling and relies on the Attention-GRU network for dynamic learning and forecasting. The proposed model captures the dynamic dependence among multivariate stock returns, especially focusing on the tail-side properties. We also propose a two-step iterative algorithm to train the model and then predict the time-varying model parameters, including the time-invariant tail parameters. At each investment date, we can easily simulate new samples from the learned generative model, and we further perform CVaR portfolio optimization with the simulated samples to form a dynamic portfolio strategy. The numerical experiment on stock data shows that our model leads to wiser investments that promise higher reward-risk ratios and present lower tail risks.