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.
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Acknowledgements
This work is partially supported by grant from the Natural Science Foundation of China (No. 61632011, 61572102, 61702080, 61602079, 61562080), State Education Ministry and The Research Fund for the Doctoral Program of Higher Education (No. 20090041110002), the Fundamental Research Funds for the Central Universities.
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Xu, B., Zhang, D., Zhang, S., Li, H., Lin, H. (2018). Stock Market Trend Prediction Using Recurrent Convolutional Neural Networks. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_14
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DOI: https://doi.org/10.1007/978-3-319-99501-4_14
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