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Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon Phi

Matthew Dixon, Diego Klabjan, Jin Hoon Bang
Stuart School of Business, Illinois Institute of Technology, 10 West 35th Street, Chicago, IL 60616
8th Workshop on High Performance Computational Finance, 2015
@inproceedings{dixon2015implementing,

   title={Implementing deep neural networks for financial market prediction on the Intel Xeon Phi},

   author={Dixon, Matthew and Klabjan, Diego and Bang, Jin Hoon},

   booktitle={Proceedings of the 8th Workshop on High Performance Computational Finance},

   pages={6},

   year={2015},

   organization={ACM}

}

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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 financial market prediction has not been previously researched, partly because of their computational complexity. This paper describes the application of DNNs to predicting financial market movement directions. A critical step in the viability of the approach in practice is the ability to effectively deploy the algorithm on general purpose high performance computing infrastructure. Using an Intel Xeon Phi co-processor with 61 cores, we describe the process for efficient implementation of the batched stochastic gradient descent algorithm and demonstrate a 11.4x speedup on the Intel Xeon Phi over a serial implementation on the Intel Xeon.
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