  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.
