Classification-based financial markets prediction using deep neural networks

By Matthew F. Dixon, Diego Klabjan, Jin Hoon Bang. 2016

199 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research, Economics & Operations Research

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “In [155], multiple DMLP models were developed for predicting AD and BP futures using 5minute data in a 130 day period.”
Deep Learning for Financial Applications : A Survey (2020, methodology): “In [46], feedforward deep MLP was implemented for the prediction of commodities and FX trading prices.”
Using deep learning to detect price change indications in financial markets (2017, methodology): “Given some input features machine learning models can be used to predict the behaviour of various aspects of financial markets [1], [2], [3], [4].”
A High Frequency Trade Execution Model for Supervised Learning (2017, unknown): “Sequence classiﬁcation has been considered elsewhere in the literature for lower frequency price movement prediction from historical prices (Leung et al., 2000; Dixon et al., 2016).”
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, unknown): “[20], [22]–[29].”
Deep learning for limit order books (2016, unknown): “Dixon et al. (2017) use deep neural networks for predicting financial market movements.”
Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications (2018, methodology): “A similar problem is faced by Dixon, Klabjan and Hoon Bang [12], where they make use of a Deep Neural Network (DNN) as predictor of price movements over the following 5minutes for several commodities and forex futures.”
Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks (2017, methodology): “Given some input features machine learning models can be used to predict the behaviour of various aspects of financial markets [1], [2], [3], [4].”
Sequence classification of the limit order book using recurrent neural networks (2017, methodology): “A number of researchers have applied machine learning methods to the study of limit order book dynamics (Kearns and Nevmyvaka, 2013; Kercheval and Zhang, 2015; Sirignano, 2016; Dixon et al., 2017).”
Temporal Relational Ranking for Stock Prediction (2018, methodology): “Following [9], we adopt a daily buy-hold-sell trading strategy to evaluate the performance of stock prediction methods regarding the revenue.”
Thresholded ConvNet ensembles: neural networks for technical forecasting (2018, methodology): “[5] further demonstrated the effectiveness of neural nets on intraday data, deploying MLPs to classify returns on commodity and FX futures over discrete 5-min intervals.”