数据挖掘技术在证券分析中的应用研究
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摘要
数据挖掘可以称为数据库中的知识发现,它是从大量数据中发现并提取隐藏在其中的可信的、新颖的、有效的并能被人理解的模式的高级处理过程。数据挖掘解决了传统分析方法的不足,并能够对大规模数据的进行分析处理。数据挖掘从大量数据中提取出隐藏在数据之后的有用的信息,为人们的正确决策提供了很大的帮助。
证券业作为我国较早应用信息技术的行业,已经建立了较完善的事务处理系统。多年的应用也使得证券行业积累了大量的数据,其中隐含着大量有价值的信息。如何利用这些数据,深层次地挖掘信息,为证券投资的决策服务,已成为证券行业研究的当务之急。
(1)本文介绍了数据挖掘技术的发展现状和基本原理,讨论了数据挖掘技术在证券行业中的应用意义。
(2)提出了一个集数据采集,转换和挖掘于一体的数据挖掘系统,并对其主要功能进行了比较详细的介绍。
(3)聚类分析在证券投资方面的研究有很大的发掘空间。本文将聚类分析方法引入到证券投资分析中,对股票的行业因素、公司因素、收益性、成长性等基本层面进行考察,建立了较为全面的综合评价指标体系,衡量样本股票的“相似程度”。然后通过聚类分析模型来确定投资范围和投资价值。结果表明该方法能帮助投资者准确地了解和把握股票的总体特性,预测股票的发展潜力,并通过类的总体价格水平来预测股票价格的变动趋势,选择有利的投资时机。实证研究表明该方法对指导证券投资具有有效性和实用性。
(4)数据挖掘的结果进行了分析,提出了下一步的具体工作。
The data mining is also called the knowledge discovery in database, it discovers from large quaintly of data and find authentic, novel and effective model that can be comprehended by people. The data mining finds useful information from data of large quantity to support for reasonable decision-making. Mining data from large amounts of data to extract hidden in the data after the useful information for people to the correct policy decision to provide a lot of help.
China's securities industry as an earlier application of information technology industry has been a better transaction processing system. Years of application also makes the securities industry has accumulated large amounts of data, which implied a large number of valuable information. How to make use of these data, deep-level mining, information, decision-making for investment in securities services, and the securities industry research has become a pressing task.
Firstly, this paper introduced data mining technology and the development of basic principles, discussed the data mining technology in the securities industry of significance.
Secondly, the paper proposed a set of data collection, conversion and mining data collected in one of the mining systems, and its main function of a more detailed briefing.
Thirdly, the primary methods of stock investment analysis are fundamental analysis, technical analysis and portfolio investment analysis. Based on analyzing the applicability and limitation of these methods, the paper is applying the cluster analysis in the stock investment, and pointing out it is a feasible and effective way to give advices to stock investment. Cluster analysis firstly looks into the industry factor, company factor, profitability and pullulating of each stock, so people can discover the investment value through stock' immanent value which is decided by their fundamental characters. Together the similar stock, a synthetic evaluation index system is erected to measure the similarity of stock ail-roundly and objectively. Cluster analysis can help investors hold the whole character of stocks, make certain of investing scale and suppose the possible variation of stock price. So it will be easier for investors to choose investing occasion.
Finally, the paper Mining data acquisition system for the overall debugging, and the results are analyzed, the next step of the concrete work.
证券业作为我国较早应用信息技术的行业,已经建立了较完善的事务处理系统。多年的应用也使得证券行业积累了大量的数据,其中隐含着大量有价值的信息。如何利用这些数据,深层次地挖掘信息,为证券投资的决策服务,已成为证券行业研究的当务之急。
(1)本文介绍了数据挖掘技术的发展现状和基本原理,讨论了数据挖掘技术在证券行业中的应用意义。
(2)提出了一个集数据采集,转换和挖掘于一体的数据挖掘系统,并对其主要功能进行了比较详细的介绍。
(3)聚类分析在证券投资方面的研究有很大的发掘空间。本文将聚类分析方法引入到证券投资分析中,对股票的行业因素、公司因素、收益性、成长性等基本层面进行考察,建立了较为全面的综合评价指标体系,衡量样本股票的“相似程度”。然后通过聚类分析模型来确定投资范围和投资价值。结果表明该方法能帮助投资者准确地了解和把握股票的总体特性,预测股票的发展潜力,并通过类的总体价格水平来预测股票价格的变动趋势,选择有利的投资时机。实证研究表明该方法对指导证券投资具有有效性和实用性。
(4)数据挖掘的结果进行了分析,提出了下一步的具体工作。
The data mining is also called the knowledge discovery in database, it discovers from large quaintly of data and find authentic, novel and effective model that can be comprehended by people. The data mining finds useful information from data of large quantity to support for reasonable decision-making. Mining data from large amounts of data to extract hidden in the data after the useful information for people to the correct policy decision to provide a lot of help.
China's securities industry as an earlier application of information technology industry has been a better transaction processing system. Years of application also makes the securities industry has accumulated large amounts of data, which implied a large number of valuable information. How to make use of these data, deep-level mining, information, decision-making for investment in securities services, and the securities industry research has become a pressing task.
Firstly, this paper introduced data mining technology and the development of basic principles, discussed the data mining technology in the securities industry of significance.
Secondly, the paper proposed a set of data collection, conversion and mining data collected in one of the mining systems, and its main function of a more detailed briefing.
Thirdly, the primary methods of stock investment analysis are fundamental analysis, technical analysis and portfolio investment analysis. Based on analyzing the applicability and limitation of these methods, the paper is applying the cluster analysis in the stock investment, and pointing out it is a feasible and effective way to give advices to stock investment. Cluster analysis firstly looks into the industry factor, company factor, profitability and pullulating of each stock, so people can discover the investment value through stock' immanent value which is decided by their fundamental characters. Together the similar stock, a synthetic evaluation index system is erected to measure the similarity of stock ail-roundly and objectively. Cluster analysis can help investors hold the whole character of stocks, make certain of investing scale and suppose the possible variation of stock price. So it will be easier for investors to choose investing occasion.
Finally, the paper Mining data acquisition system for the overall debugging, and the results are analyzed, the next step of the concrete work.
引文
[1]约翰.墨菲.丁圣元译.期货市场技术分析.北京:地震出版社,2001.5:20-157
[2]何光汉.证券投资与证券管理.武汉:华中理工大学出版社,1997
[3]卢祖帝.2003年诺贝尔经济学奖得主的获奖工作介绍及对中国经济与金融学研究的启示.2003.7
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[7]胡运权.运筹学教程.北京:清华大学出版社,1998.6:347
[8]高洪深.决策支持系统(DSS)理论方法案例(第3版).北京:清华大学出版社,2005.5:23
[9]薛薇.数据挖掘概述.统计与精算,2001.3
[10]卢启程,邹平.数据挖掘的研究与应用发展.昆明理工大学学报,2002.27(5):62-66:
[11]W.H.Inmon.Building the Data Warehouse.John Wiley & Sons,1991
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[13]R.Agrawal,C Faloutsos,A Swami.Efficient similarity search in sequences database.4th International Conference on Foundations of Data Organization and Algorithms,Chicago Illinois USA.1993:69-84
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[15]Tolga Bozkaya,Nasser Yazdani,Meral zsoyaglu.Matching and Indexing Sequences of Different Lengths.Proceeding of the Sixth International Conference on Information and Knowledge Management,Las Vegas,Nevada.1997:128-13
[16]Chang Shing Peng,Haixun Wang,Sylvia R Zhang ET al.Landmarks,a new model for similarity-based pattern querying in time series database.Proceedings of the 16th International Conference on Data Engineering,an Diego California USA,2000:33-42
[17]夏火松.数据仓库与数据挖掘技术.北京:科学出版社,2004.3
[18]Claude Seidman.刘艺,王鲁军,蒋丹丹等译.SQL Server 2000数据挖掘技术指南.北京:机械工业出版社,2002
[19]刘晓华.精通.NET核心技术.北京:电子工业出版社,2002.8
[20]Simon Robinson.C#高级编程.北京:清华大学出版社,2002.8
[21]Bob Beauchemin.ADO.NET本质论.北京:清华大学出版社,2003
[22]Adrian Turtschi,Jason Werry,Greg Hack,Joseph Albahari:c#.NET Web Developer's Guide[M]Syngress Publishing,Inc,2002
[23]Adrian Turtschi,Jason Werry,Greg Hack,Joseph Albahari:c#.NET Web Developer's Guide.Syngress Publishing,Inc,2002
[24]杜青丹,周禄军.多元统计分析及应用研究.成都:西南财经大学出版社,1999
[25]刘吉明,张小京,刘红杰等.数据仓库在证券交易中的研究与应用.计算机工程,200.2:47-49
[26]Zhu W D,Ren M L,Yang S Let al.A framework for synthetic intelligent decision support system based on Internet information extraction.Computer Science and Technology in New Century[C].International Academic Publishers World Publishing Corporation,2001.327-33
[27]邓秀勤,聚类分析在股票市场板块分析中的应用.数理统计与管理,1999,18(5):1-4
[28]陈共,周升业,吴晓求.证券投资分析.北京:中国人民大学出版社,1997
[29]Microsoft Corporation,Microsoft SQL Server 2000 Analysis Services,Microsoft Corporation,2002
[30]何光汉.证券投资与证券管理.武汉:华中理工大学出版社,1997
[31]杜金岷.论现代证券投资组合理论的发展与局限.投资与证券,1999.9:42-43
[32]徐国祥,檀向球,胡穗华.上市公司经营业绩综合评价及其实证研究.统计研究,2000.9:44-49
[33]方开泰,实用多元统计分析,北京:华东师范大学出版社,1989
[34]王冬梅,建立我国上市公司业绩综合评价指标体系.证券与投资,2001.1:84-87
[35]R.格罗思.候迪,宋擒豹.数据挖掘-构筑企业竞争优势.西安:西安交通大学出版社,2001
[36]夏火松.数据仓库与数据挖掘技术.北京:科学出版社,2004.3
[37]Dorian Pyle,Business Modeling and Data Mining,Morgan Kaufmann Publishers,2003
[38]J.Han and M.Kamber,Data Mining:Concepts and Techniques,Morgan Kaufmann Publishers,2000
[39]刘明吉,张晓京,刘洪杰,王秀峰,王治宝.数据仓库在证券交易中的研究与应用.计算机工程,2000(2):47-49
[40]李德勇,瞿东升.面向证券分析的数据仓库系统研究.北京工业大学学报,2002.28:253-256
[41]张圆.数据挖掘与数据分析技术.北京:科学出版社,2005.7
[2]何光汉.证券投资与证券管理.武汉:华中理工大学出版社,1997
[3]卢祖帝.2003年诺贝尔经济学奖得主的获奖工作介绍及对中国经济与金融学研究的启示.2003.7
[4]Anne H.Milley,James D.Seabolt,John S.Williams.Data Mining and the Case for Sampling [M].SAS Institute Inc,1998.6
[5]卢纹岱.SPSS for Windows统计分析(第2版).北京:电子工业出版社,2002.9
[6]罗伯特S.平狄克,丹尼尔L.鲁宾费尔德.钱小军译.计量经济模型与经济预测(第4版).北京:机械工业出版社,1999.11
[7]胡运权.运筹学教程.北京:清华大学出版社,1998.6:347
[8]高洪深.决策支持系统(DSS)理论方法案例(第3版).北京:清华大学出版社,2005.5:23
[9]薛薇.数据挖掘概述.统计与精算,2001.3
[10]卢启程,邹平.数据挖掘的研究与应用发展.昆明理工大学学报,2002.27(5):62-66:
[11]W.H.Inmon.Building the Data Warehouse.John Wiley & Sons,1991
[12]C.Faloutsos,M Ranganathan,Y Monopoles.Fast subsequence marching in time-series databases.Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data,Minneapolis,Minesota.1994:419-429
[13]R.Agrawal,C Faloutsos,A Swami.Efficient similarity search in sequences database.4th International Conference on Foundations of Data Organization and Algorithms,Chicago Illinois USA.1993:69-84
[14]B-K Yi,H jagadish,C Faloutsos.Efficient retrieval of similar time sequences under time warping.Proceeding of the Fourteenth International Conference on Data Engineering,Orlando,Florida USA.1998:201-248
[15]Tolga Bozkaya,Nasser Yazdani,Meral zsoyaglu.Matching and Indexing Sequences of Different Lengths.Proceeding of the Sixth International Conference on Information and Knowledge Management,Las Vegas,Nevada.1997:128-13
[16]Chang Shing Peng,Haixun Wang,Sylvia R Zhang ET al.Landmarks,a new model for similarity-based pattern querying in time series database.Proceedings of the 16th International Conference on Data Engineering,an Diego California USA,2000:33-42
[17]夏火松.数据仓库与数据挖掘技术.北京:科学出版社,2004.3
[18]Claude Seidman.刘艺,王鲁军,蒋丹丹等译.SQL Server 2000数据挖掘技术指南.北京:机械工业出版社,2002
[19]刘晓华.精通.NET核心技术.北京:电子工业出版社,2002.8
[20]Simon Robinson.C#高级编程.北京:清华大学出版社,2002.8
[21]Bob Beauchemin.ADO.NET本质论.北京:清华大学出版社,2003
[22]Adrian Turtschi,Jason Werry,Greg Hack,Joseph Albahari:c#.NET Web Developer's Guide[M]Syngress Publishing,Inc,2002
[23]Adrian Turtschi,Jason Werry,Greg Hack,Joseph Albahari:c#.NET Web Developer's Guide.Syngress Publishing,Inc,2002
[24]杜青丹,周禄军.多元统计分析及应用研究.成都:西南财经大学出版社,1999
[25]刘吉明,张小京,刘红杰等.数据仓库在证券交易中的研究与应用.计算机工程,200.2:47-49
[26]Zhu W D,Ren M L,Yang S Let al.A framework for synthetic intelligent decision support system based on Internet information extraction.Computer Science and Technology in New Century[C].International Academic Publishers World Publishing Corporation,2001.327-33
[27]邓秀勤,聚类分析在股票市场板块分析中的应用.数理统计与管理,1999,18(5):1-4
[28]陈共,周升业,吴晓求.证券投资分析.北京:中国人民大学出版社,1997
[29]Microsoft Corporation,Microsoft SQL Server 2000 Analysis Services,Microsoft Corporation,2002
[30]何光汉.证券投资与证券管理.武汉:华中理工大学出版社,1997
[31]杜金岷.论现代证券投资组合理论的发展与局限.投资与证券,1999.9:42-43
[32]徐国祥,檀向球,胡穗华.上市公司经营业绩综合评价及其实证研究.统计研究,2000.9:44-49
[33]方开泰,实用多元统计分析,北京:华东师范大学出版社,1989
[34]王冬梅,建立我国上市公司业绩综合评价指标体系.证券与投资,2001.1:84-87
[35]R.格罗思.候迪,宋擒豹.数据挖掘-构筑企业竞争优势.西安:西安交通大学出版社,2001
[36]夏火松.数据仓库与数据挖掘技术.北京:科学出版社,2004.3
[37]Dorian Pyle,Business Modeling and Data Mining,Morgan Kaufmann Publishers,2003
[38]J.Han and M.Kamber,Data Mining:Concepts and Techniques,Morgan Kaufmann Publishers,2000
[39]刘明吉,张晓京,刘洪杰,王秀峰,王治宝.数据仓库在证券交易中的研究与应用.计算机工程,2000(2):47-49
[40]李德勇,瞿东升.面向证券分析的数据仓库系统研究.北京工业大学学报,2002.28:253-256
[41]张圆.数据挖掘与数据分析技术.北京:科学出版社,2005.7