Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model

doi:10.11925/infotech.2096-3467.2020.0907

Data Analysis and Knowledge Discovery

2021, Vol. 5

Issue (7): 126-138 DOI: 10.11925/infotech.2096-3467.2020.0907

Current Issue | Archive | Adv Search

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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Abstract

[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 words： Deep 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: xuyuemei@bfsu.edu.cn

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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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0907 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I7/126

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

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

Parameter Settings of the Prediction Model

Experiment Results on News Event Classification

分类性能较好的新闻事件类型示例				分类性能较差的新闻事件类型示例
新闻事件	精确率	召回率	$F 1$ 值	新闻事件	精确率	召回率	$F 1$ 值
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

Experiment Results on News Sentiment Classification

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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