Stock Market Trend Prediction Using Recurrent Convolutional Neural Networks

摘要：

　　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.

关键词： Stock market prediction Embedding layer Convolutional neural network Long short-term memory

作者: Bo Xu Dongyu Zhang Shaowu Zhang Hengchao Li Hongfei Lin

作者单位: School of Computer Science and Technology,Dalian University of Technology,Dalian,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 166-177

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）