Title: Two-Step Disentanglement for Financial Data,
Abstract: In this work, we address the problem of disentanglement of factors that
generate a given data into those that are correlated with the labeling and
those that are not. Our solution is simpler than previous solutions and employs
adversarial training in a straightforward manner. We demonstrate the new method
on visual datasets as well as on financial data. In order to evaluate the
latter, we developed a hypothetical trading strategy whose performance is
affected by the performance of the disentanglement, namely, it trades better
when the factors are better separated.