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Deep Learning Networks for Stock Market Analysis and Prediction
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资源说明：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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