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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (7): 126-138    DOI: 10.11925/infotech.2096-3467.2020.0907
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Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model
Xu Yuemei1(),Wang Zihou2,Wu Zixin1
1School of Information Science and Technology, Beijing Foreign Studies of University, Beijing 100089, China
2National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China
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[Objective] Based on the traditional financial data analysis, this paper explores the impacts of online news on stock market, aiming to improve the accuracy of predicting stock trends. [Methods] First, we used the Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) to extract news events and their sentiment orientations. Then, we proposed a prediction model for stock trends, which combines the stock numerical data and the news event sentiments. Finally, we examined the feasibility of this model with two individual stocks (GREE Electric Appliance in the household appliance industry and ZTE in the electronic appliance industry). [Results] The prediction accuracy of our model was 11.6% and 25.6% higher than the exiting algorithms. [Limitations] We did not evaluate the impacts of prediction period on the performance of the proposed model. [Conclusions] The news events and their sentiment orientations could lead to the fluctuation of stock prices.

Key wordsDeep Learning      Feature Combination      Sentiment Analysis      Stock Trends     
Received: 15 September 2020      Published: 15 April 2021
ZTFLH:  TP393  
Fund:Project of Double Top-Class Foundation of Beijing Foeign Studies University(YY19ZZA012)
Corresponding Authors: Xu Yuemei,ORCID:0000-0002-0223-7146     E-mail:

Cite this article:

Xu Yuemei, Wang Zihou, Wu Zixin. Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model. Data Analysis and Knowledge Discovery, 2021, 5(7): 126-138.

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The Flowchart of Stock Trends Prediction Model Based on Combination of News Event andNews Sentiment Orientation
D 1 D 2 D j D p
T 1 $\bar{d}_{11}$ $\bar{d}_{12}$ $\bar{d}_{1j}$ $\bar{d}_{1p}$
T i $\bar{d}_{i1}$ $\bar{d}_{i2}$ $\bar{d}_{ij}$ $\bar{d}_{ip}$
T n $\bar{d}_{n1}$ $\bar{d}_{n2}$ $\bar{d}_{nj}$ $\bar{d}_{np}$
Feature Matrix of Stock Finance
事件类别 事件名称
交易类 停牌 复牌 资金流入 资金流出 大宗交易 股价倒挂 创新高
股权类 挂牌 借壳 举牌 收购并购 资产重组 资产冻结 股权转让
投融资类 投资 投建 中标 发行债券 发行股票 可转债 募资 质押 分红
公司事务类 注册资本变更 快速发展 战略合作 拓展业务 高管减持或离职
外部事件类 登上龙虎榜 交易所处罚 评级利好 评级下调 政策利好
Part of Categories of News Events
S 1 S 2 S j S q
T 1 s 11 s 12 s 1 j s 1 q
T i s i 1 s i 2 s ij s iq
T n s n 1 s n 2 s nj s nq
Feature Matrix of News Event
Analysis Model of News Sentiment Orientation Based on Bi-LSTM
Sampling Period of Stock Trends Prediction Model
词向量维度 300 300
卷积核个数 96 Null
卷积核大小 3,4,5 Null
Dropout 0.5 0.5
Batch_size 128 128
迭代次数 10 20
标题截取长度 Null 15
单层LSTM神经元个数 Null [256,256]
Parameter Settings of the Prediction Model

SVM Maxent CNN
训练集 90.8% 72.0% 93.0%
测试集 85.2% 69.4% 87.7%
Experiment Results on News Event Classification
新闻事件 精确率 召回率 F 1 新闻事件 精确率 召回率 F 1
登上龙虎榜 1.00 1.00 1.00 业绩下降 0.64 0.58 0.61
停牌 0.98 1.00 0.99 政策利好 0.81 0.65 0.72
工商变更 1.00 1.00 1.00 资本变更 1.00 0.22 0.36
中标 1.00 1.00 1.00 聘请高管 0.50 0.40 0.44
可转债 0.97 0.97 0.97 业绩增长 0.68 0.73 0.71
质押 1.00 1.00 1.00 预计下滑 0.67 0.61 0.64
交易所问询 0.94 1.00 0.97 利差消息 0.42 0.47 0.44
退市 1.00 1.00 1.00 利好消息 0.46 0.65 0.54
Performance Statistics of News Event Classification
数据集 SVM精确率 Maxent精确率 Bi-LSTM精确率
训练集 86.6% 82.8% 99.0%
测试集 81.1% 76.1% 91.0%
Experiment Results on News Sentiment Classification
股票 采用财务特征的LSTM 引入新闻事件的LSTM 新闻事件/情感融合的
格力电器 0.699 8 0.754 5 0.625 7 0.781 2
中兴通讯 0.646 7 0.785 1 0.654 5 0.812 7
Experiment Results on Stock Trends Prediction
Individual Stock Trends Prediction on GREE Electric Appliance (000651.SZ)
Individual Stock Trends Prediction on ZTE Communication (000063.SZ)
Importance Score of Different Features Based on GBDT
Impact of Threshold Value on Stock Trend Prediction
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