Research Area: Machine Learning
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.
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Author(s) Name: Wen Long, Zhichen Lu, Lingxiao Cui
Journal name: Knowledge-Based Systems
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Publisher name: ELSEVIER
DOI: 10.1016/j.knosys.2018.10.034
Volume Information: Volume 164, 15 January 2019, Pages 163-173
