Deep Belief Networks for Financial Prediction

Financial business prediction has lately raised a great interest due to the recent world crisis events. In spite of the many advanced shallow computational methods that have extensively been proposed, most algorithms have not yet attained a desirable level of applicability. All show a good performance for a given financial setup but fail in general to create better and reliable models. The main focus of this paper is to present a deep learning model with strong ability to generate high level feature representations for accurate financial prediction. The proposed Deep Belief Network (DBN) approach tested in a real dataset of French companies compares favorably to shallow architectures such as Support Vector Machines (SVM) and single Restricted Boltzmann Machine (RBM). We show that the underlying financial model with deep machine technology has a strong potential thus empowering the finance industry.