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Indexed by:会议论文

Date of Publication:2018-01-01

Included Journals:CPCI-S、EI

Volume:11109

Page Number:166-177

Key Words:Stock market prediction; Embedding layer; Convolutional neural network; Long short-term memory

Abstract:Short-term prediction of stock market trend has potential application for personal investment without high-frequency-trading infrastructure. Existing studies on stock market trend prediction have introduced machine learning methods with handcrafted features. However, manual labor spent on handcrafting features is expensive. To reduce manual labor, we propose a novel recurrent convolutional neural network for predicting stock market trend. Our network can automatically capture useful information from news on stock market without any handcrafted feature. In our network, we first introduce an entity embedding layer to automatically learn entity embedding using financial news. We then use a convolutional layer to extract key information affecting stock market trend, and use a long short-term memory neural network to learn context-dependent relations in financial news for stock market trend prediction. Experimental results show that our model can achieve significant improvement in terms of both overall prediction and individual stock predictions, compared with the state-of-the-art baseline methods.