On Feature Reduction using Deep Learning for Trend Prediction in Finance

TL;DR

This paper explores how deep learning models like RBMs and Auto-Encoders can reduce features effectively for financial trend prediction, analyzing their architectural impacts on prediction quality.

Contribution

It compares RBMs and Auto-Encoders for feature reduction in finance, highlighting how architecture and input space influence prediction performance.

Findings

Auto-Encoders can be a viable alternative to RBMs for feature reduction.

Architectural choices significantly affect prediction accuracy.

Non-linear feature reduction improves trend prediction in finance.

Abstract

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.