Journal
KNOWLEDGE-BASED SYSTEMS
Volume 164, Issue -, Pages 163-173Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2018.10.034
Keywords
Stock price prediction; Feature engineering; Deep learning
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Funding
- National Natural Science Foundation of China [71771204, 71331005, 91546201]
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Stock price modeling and prediction have been challenging objectives for researchers and speculators because of noisy and non-stationary characteristics of samples. With the growth in deep learning, the task of feature learning can be performed more effectively by purposely designed network. in this paper, we propose a novel end-to-end model named multi-filters neural network (MFNN) specifically for feature extraction on financial time series samples and price movement prediction task. Both convolutional and recurrent neurons are integrated to build the multi-filters structure, so that the information from different feature spaces and market views can be obtained. We apply our MFNN for extreme market prediction and signal-based trading simulation tasks on Chinese stock market index CSI 300. Experimental results show that our network outperforms traditional machine learning models, statistical models, and single-structure(convolutional, recurrent, and LSTM) networks in terms of the accuracy, profitability, and stability. (C) 2018 Elsevier B.V. All rights reserved.
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