Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies

•Deep learning networks are applied to stock market analysis and prediction.•A comprehensive analysis with different data representation methods is offered.•Five-minute intraday data from the Korean KOSPI stock market is used.•The network applied to residuals of autoregressive model improves predict...

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Veröffentlicht in:Expert systems with applications Jg. 83; S. 187 - 205
Hauptverfasser: Chong, Eunsuk, Han, Chulwoo, Park, Frank C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 15.10.2017
Elsevier BV
Schlagworte:
Algorithms
Artificial intelligence
Artificial neural networks
Autoregressive models
Autoregressive processes
Case depth
Covariance
Covariance estimation
Deep learning
Empirical analysis
Expert systems
Feature extraction
High frequencies
Markets
Multilayer neural network
Neural networks
Predictions
Principal components analysis
Representations
Securities markets
Stock market prediction
Structural analysis
Deep learning
Multilayer neural network
Stock market prediction
Covariance estimation
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•Deep learning networks are applied to stock market analysis and prediction.•A comprehensive analysis with different data representation methods is offered.•Five-minute intraday data from the Korean KOSPI stock market is used.•The network applied to residuals of autoregressive model improves prediction.•Covariance estimation for market structure analysis is improved with the network. We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high-frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methods—principal component analysis, autoencoder, and the restricted Boltzmann machine—on the network’s overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2017.04.030