Classification-based financial markets prediction using deep neural networks

Matthew Dixon (), Diego Klabjan and Jin Hoon Bang
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Matthew Dixon: Stuart School of Business, Illinois Institute of Technology, Postal: Chicago, IL, USA
Diego Klabjan: Department of Industrial Engineering and Management Sciences, Northwestern University, Postal: Evanston, IL, USA
Jin Hoon Bang: Department of Computer Science, Northwestern University, Postal: Evanston, IL, USA

Algorithmic Finance, 2017, vol. 6, issue 3-4, 67-77

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 (Krizhevsky et al., 2012 ) for their superior predictive properties including robustness to overfitting. 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 backtesting 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 backtesting environment both of which are available as open source code written by the authors.

Keywords: financial; markets (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (30)

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Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0059

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