[1]林天华,张倩倩,祁旭阳,等.证券大数据分析研究[J].计算机技术与发展,2020,30(10):179-186.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 032]
　LIN Tian-hua,ZHANG Qian-qian,QI Xu-yang,et al.Research and Analysis of Securities Big Data[J].,2020,30(10):179-186.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 032]
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证券大数据分析研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年10期
页码:
179-186
栏目:
应用开发研究
出版日期:
2020-10-10

文章信息/Info

Title:
Research and Analysis of Securities Big Data
文章编号:
1673-629X(2020)10-0179-08
作者:
林天华1; 张倩倩1; 祁旭阳1; 赵 霞2
1. 河北经贸大学 信息技术学院,河北 石家庄 050061; 2. 河北经贸大学 经管实验中心,河北 石家庄 050061
Author(s):
LIN Tian-hua1; ZHANG Qian-qian1; QI Xu-yang1; ZHAO Xia2
1. School of Information Technology,Hebei University of Economics and Business,Shijiazhuang 050061,China; 2. Economic Management Experiment Center,Hebei University of Economics and Business,Shijiazhuang 050061,China
关键词:
证券大数据; 数据分析; 机器学习; 交易监管; 预测分析
Keywords:
securities big data; data analysis; machine learning; transaction regulation; prediction analysis
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 10. 032
摘要:
将大数据技术与机器学习应用到证券领域,探索国内证券行业发展规律是证券行业科技创新的重要举措。 介绍了证券大数据的概念,总结了大数据技术在证券领域的应用,包括交易监察、财务分析、恐慌指数分析、舆论热度分析、个性化服务、预测与量化投资等。 分析了目前处理证券大数据的主要算法模型,包括交易监管算法、财务分析算法、恐慌指数分析算法、情感分析算法以及预测与量化投资算法模型。 并对其中的机器学习预测算法,如支持向量机、卷积神经网络、贝叶斯神经网络、遗传算法对 BP 神经网络的优化等进行详细论述,对传统时序预测模型和基于机器学习的预测模型进行了优劣性对比。 最后对证券领域的大数据应用进行了展望和总结,大数据技术在证券领域的应用日益广泛,采用机器学习算法对证券行情进行预测是研究方向和热点。
Abstract:
Applying big data technology and machine learning to the field of securities and exploring the development law of domestic securities industry is an important measure of science and technology innovation in the securities industry. We introduce the concept of securities big data and summarize the application of big data technology in the securities field,including transaction supervision,financial analysis,panic index analysis,public opinion heat analysis,personalized service,prediction and quantitative investment,etc. We analyze the main algorithm models of dealing with big data of securities,including transaction supervision algorithm,financial analysis algorithm,panic index analysis algorithm, emotion analysis algorithm, pred-iction and quantitative investment algorithm. And the machine learning prediction algorithm,such as support vector machine,convolution neural network,Bayesian neural network,genetic algorithm optimization of BP neural network,etc. are discussed in detail,and the advantages and disadvantages of the traditional time series prediction model and the prediction model based on machine learning are compared. Finally,the application of big data in the field of securities is prospected and summarized. The application of big data technology in the field of securities is more and more extensive. It is a research direction and hot spot to use machine learning algorithm to predict the stock market.

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更新日期/Last Update: 2020-10-10