  Since decades, the data science community tries to propose prediction models
of financial time series. Yet, driven by the rapid development of information
technology and machine intelligence, the velocity of today's information leads
to high market efficiency. Sound financial theories demonstrate that in an
efficient marketplace all information available today, including expectations
on future events, are represented in today prices whereas future price trend is
driven by the uncertainty. This jeopardizes the efforts put in designing
prediction models. To deal with the unpredictability of financial systems,
today's portfolio management is largely based on the Markowitz framework which
puts more emphasis in the analysis of the market uncertainty and less in the
price prediction. The limitation of the Markowitz framework stands in taking
very strong ideal assumptions about future returns probability distribution.
  To address this situation we propose PAGAN, a pioneering methodology based on
deep generative models. The goal is modeling the market uncertainty that
ultimately is the main factor driving future trends. The generative model
learns the joint probability distribution of price trends for a set of
financial assets to match the probability distribution of the real market. Once
the model is trained, a portfolio is optimized by deciding the best
diversification to minimize the risk and maximize the expected returns observed
over the execution of several simulations. Applying the model for analyzing
possible futures, is as simple as executing a Monte Carlo simulation, a
technique very familiar to finance experts. The experimental results on
different portfolios representing different geopolitical areas and industrial
segments constructed using real-world public data sets demonstrate promising
results.
