  One of the major advantages in using Deep Learning for Finance is to embed a
large collection of information into investment decisions. A way to do that is
by means of compression, that lead us to consider a smaller feature space.
Several studies are proving that non-linear feature reduction performed by Deep
Learning tools is effective in price trend prediction. The focus has been put
mainly on Restricted Boltzmann Machines (RBM) and on output obtained by them.
Few attention has been payed to Auto-Encoders (AE) as an alternative means to
perform a feature reduction. In this paper we investigate the application of
both RBM and AE in more general terms, attempting to outline how architectural
and input space characteristics can affect the quality of prediction.
