[1]李庆涛,林培光,王基厚,等.基于板块效应的深度学习股价走势预测方法[J].南京师范大学学报(工程技术版),2022,22(01):030-38.[doi:10.3969/j.issn.1672-1292.2022.01.005]
　Li Qingtao,Lin Peiguang,Wang Jihou,et al.Deep Learning Stock Price Forecasting Method Based on Plate Effect[J].Journal of Nanjing Normal University(Engineering and Technology),2022,22(01):030-38.[doi:10.3969/j.issn.1672-1292.2022.01.005]
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基于板块效应的深度学习股价走势预测方法
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
22卷
期数:
2022年01期
页码:
030-38
栏目:
机器学习
出版日期:
2022-03-15

文章信息/Info

Title:
Deep Learning Stock Price Forecasting Method Based on Plate Effect
文章编号:
1672-1292(2022)01-0030-09
作者:
李庆涛; 林培光; 王基厚; 周佳倩; 张 燕; 蹇木伟
山东财经大学计算机科学与技术学院,山东 济南 250014
Author(s):
Li Qingtao; Lin Peiguang; Wang Jihou; Zhou Jiaqian; Zhang Yan; Jian Muwei
School of Computer Science and Technology,Shandong University of Finance and Economics,Jinan 250014,China
关键词:
同板块股票特征; XGBoost; 股票预测; LSTM; 深度学习
Keywords:
characteristics of the same industry stocks; XGBoost; stock prediction; LSTM; deep learning
分类号:
TP391
DOI:
10.3969/j.issn.1672-1292.2022.01.005
文献标志码:
A
摘要:
股票价格预测作为金融预测领域中一项重要的研究方向,准确预测股票价格的涨跌可以帮助投资者盈利或及时止损. 经研究发现,某些因素(如政策、社会突发事件等)会对同板块下的多只股票价格产生影响,导致同板块的多只股票在某个时间段内出现相似的走势,即板块效应. 因此,同板块下多只股票的价格走势对于股票预测具有参考作用. 针对这一现象,提出了一种基于板块效应的深度学习股价走势预测方法. 首先,使用皮尔森(Pearson)相关系数和XGBoost算法对同板块下多只股票的收盘价进行分析,以筛选出与预测股票相关性高的多只股票,并使用自编码器对这些股票的收盘价进行降维,以提取股票的价格走势; 其次,构建了一个基于卷积神经网络(convolutional neural networks,CNN)与长短期记忆(long short-term memory,LSTM)网络的混合深度学习预测模型,使用一维卷积神经网络提取输入数据的特征,使用LSTM网络对股票价格进行预测. 该模型使用银行、医药、酒业、娱乐传媒4个板块的股票作为实验数据集. 为了提高模型的预测效果,通过随机搜索对LSTM网络的神经元个数进行简单的分析,以选择较优的神经元个数. 最后,通过实验分析,基于同板块数据集的深度学习预测模型具有良好的预测效果.
Abstract:
As an important research direction in the field of financial forecasting,accurate prediction of stock price rise and fall can help investors to make profits or stop losses in time. It has been found that certain factors(such as policies,social emergencies)can have an impact on the prices of multiple stocks in the same sector,resulting in similar movements of multiple stocks in the same sector in a certain period of time,i.e. the sector effect. Therefore,the price trends of multiple stocks under the same segment are useful for stock forecasting. To address this phenomenon,a deep learning stock price trend prediction method based on the plate effect is proposed. Firstly,the Pearson correlation coefficient and XGBoost algorithm are used to analyze the closing prices of many stocks in the same sector so as to screen out the stocks with high correlation with the predicted stocks. Then,the autoencoder is used to reduce the dimension of the closing prices of these stocks,so as to extract the price trend of the stocks. Secondly,a hybrid deep learning prediction model based on convolutional neural network and long short-term memory network is constructed. One-dimensional convolutional neural network is used to extract the features of input data,and LSTM network is used to predict stock prices. The model uses stocks in four sectors,namely,banking,pharmaceuticals,alcohol,and entertainment media,as the experimental data set. In order to improve the prediction effect of the model,the number of neurons of the LSTM network is simply analyzed by random search to select the better number of neurons. Finally,the experimental analysis shows that the deep learning prediction model based on the same board dataset has good prediction effect.

参考文献/References:

[1] WEI D. Prediction of stock price based on LSTM neural network[C]//Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing(AIAM). Dublin:IEEE,2019:544-547.
[2]ZHOU Y X,ZHANG J. Stock data analysis based on BP neural network[C]//Proceedings of the 2010 Second International Conference on Communication Software and Networks,Singapore:IEEE,2010:396-399.
[3]刘博,王明烁,李永,等. 深度学习在时空序列预测中的应用综述[J]. 北京工业大学学报,2021,47(8):925-941.
[4]李金轩,杜军平,薛哲. 基于多视角股票特征的股票预测研究[J]. 南京大学学报(自然科学),2021,57(1):68-74.
[5]张成云,汪俊. 北京奥运会对A股奥运板块的上市公司效益影响的研究[J]. 特区经济,2007(9):109-111.
[6]HUANG Y,CAPRETZ L F,HO D. Neural network models for stock selection based on fundamental analysis[C]//Proceedings of the 2019 IEEE Canadian Conference of Electrical and Computer Engineering(CCECE),Edmonton,Canada:IEEE,2019:1-4.
[7]LI X,WU P,WANG W. Incorporating stock prices and news sentiments for stock market prediction:a case of Hong Kong[J]. Information Processing & Management,2020,57(5):102212.
[8]YU H,CHEN R,ZHANG G. A SVM stock selection model within PCA[J]. Procedia Computer Science,2014,31:406-412.
[9]BAO W,YUE J,RAO Y. A deep learning framework for financial time series using stacked autoencoders and long-short term memory[J]. PloS One,2017,12(7):e0180944.
[10]CHEN C,ZHANG Q,YU B,et al. Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier[J]. Computers in Biology and Medicine,2020,123:103899.
[11]李冰. 沪深300股指预测——基于ARIMA模型和人工神经网络模型相结合的方法[D]. 广州:暨南大学,2018.
[12]李辉,赵玉涵. 基于DFS-BPSO-SVM的股票趋势预测方法[J]. 软件导刊,2017,16(12):147-151.
[13]XIAO J,ZHU X,HUANG C,et al. A new approach for stock price analysis and prediction based on SSA and SVM[J]. International Journal of Information Technology and Decision Making,2019,18(1):287-310.
[14]徐月梅,王子厚,吴子歆. 一种基于CNN-BiLSTM多特征融合的股票走势预测模型[J]. 数据分析与知识发现,2021,5(7):126-137.
[15]陈佳. RNN神经网络在股指预测中的应用研究[D]. 天津:天津科技大学,2019.
[16]林培光,周佳倩,温玉莲. SCONV:一种基于情感分析的金融市场趋势预测方法[J]. 计算机研究与发展,2020,57(8):1769-1778.
[17]胡聿文. 基于优化LSTM模型的股票预测[J]. 计算机科学,2021,48(Suppl 1):151-157.
[18]DING G,QIN L. Study on the prediction of stock price based on the associated network model of LSTM[J]. International Journal of Machine Learning and Cybernetics,2020,11(6):1307-1317.
[19]SKEHIN T,CRANE M,BEZBRADICA M. Day ahead forecasting of FAANG stocks using ARIMA,LSTM networks and wavelets[C]//Proceedings for the 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science. Dublin:CEUR Workshop Proceedings,2018.
[20]MEHTAB S,SEN J. Stock price prediction using CNN and LSTM-based deep learning models[C]//Proceedings of the 2020 International Conference on Decision Aid Sciences and Application(DASA). Sakheer,Bahrain:IEEE,2020:447-453.
[21]CHEN T Q,GUESTRIN C. Xgboost:a scalable tree boosting system[C]//Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. New York,USA:Association for Computing Machinery,2016:785-794.

备注/Memo

备注/Memo:
收稿日期:2021-08-31.
基金项目:国家自然科学基金项目(61802230).
通讯作者:林培光,博士,副教授,研究方向:信息检索、自然语言处理、机器学习. E-mail:llpwgh@163.com
更新日期/Last Update: 2022-03-15