Algorithmic Finance, 2016.
20 Pages Posted: 30 Mar 2016 Last revised: 9 Dec 2016
Date Written: July 18, 2016
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.
Keywords: Deep Neural Networks, Algorithmic Trading, Commodity Futures, FX Futures
JEL Classification: C45, G1
Suggested Citation: Suggested Citation
Dixon, Matthew Francis and Klabjan, Diego and Bang, Jin Hoon, Classification-Based Financial Markets Prediction Using Deep Neural Networks (July 18, 2016). Algorithmic Finance, 2016.. Available at SSRN: https://ssrn.com/abstract=2756331
By Andrew Ang