Year: 2017
Type: article
Abstract: Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community for their superior predictive properties including robustness to over fitting. However their application to algorithmic trading has not been previously researched, partly because of their computational complexity. This paper describes the application of DNNs to predicting financial market movement directions. In particular we describe the configuration and training approach and then demonstrate their application to back testing a simple trading strategy over 43 different Commodity and FX future mid-prices at 5-minute intervals. All results in this paper are generated using a C implementation on the Intel Xeon Phi co-processor which is 11.4x faster than the serial version and a Python strategy back testing environment both of which are available as open source code written by the authors.
Source: Algorithmic Finance
DOI: 10.3233/af-170176
Authors: Matthew Francis Dixon, Diego Klabjan, Jin Bang
Institutions: Illinois Institute of Technology, Northwestern University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
Funder: Intel Corporation
OpenAlex ID: W2951899790