Classification-based financial markets prediction using deep neural networks

Matthew Francis Dixon · Diego Klabjan · Jin Hoon Bang

215 citations0/0 files storedAlgorithmic Finance

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

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.

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References

2000Forecasting stock indices: a comparison of classification and level estimation models 1998Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data 1995Neural networks in the capital markets

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