On Feature Reduction using Deep Learning for Trend Prediction in Finance
This work addresses feature reduction for trend prediction in finance, but it appears incremental as it compares existing methods without introducing a new paradigm.
The paper investigates the application of Restricted Boltzmann Machines (RBM) and Auto-Encoders (AE) for feature reduction in financial trend prediction, focusing on how architectural and input space characteristics affect prediction quality.
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.