Knowledge Commons of Institute of Automation,CAS
Attention-Based Event Relevance Model for Stock Price Movement Prediction | |
Liu, Jian1,2; Chen, Yubo1; Liu, Kang1,2; Zhao, Jun1,2 | |
2018-01 | |
会议名称 | CCKS |
会议日期 | 2018-01 |
会议地点 | 成都 |
摘要 | Stock prices, in general, can be affected by world events such as wars, natural disasters, government policies, etc. However, the correlations between events and stock prices are often implicit and the influences of events on stock prices can be in indirect ways and act in chain reactions, which brings essential difficulties for precise market prediction. In this paper, we propose an attention-based event relevance model (ATT-ERNN) to explicitly model event relevance for predicting stock price movement. Specifically, in our model, we use long short-term memory neural network (LSTM) and convolution neural network (CNN) to encode event information and stock information to distributional representations. After that, we employ attention mechanism to find related events for each stock to do price movement prediction. Attention weights in our model have a quantitative interpretation as the relevance degree of events affecting the price of a specific stock. We have conduct extensive experiments on a manually collected real-world dataset. Experimental results show the superiority of our model over many baselines, which proves the effectiveness of our model in this prediction problem |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39220 |
专题 | 多模态人工智能系统全国重点实验室_自然语言处理 |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Liu, Jian,Chen, Yubo,Liu, Kang,et al. Attention-Based Event Relevance Model for Stock Price Movement Prediction[C],2018. |
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