基于数据挖掘的股票数据分析
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摘要
股票市场是国家市场经济的真实反映,但由于股市其内部规律的复杂性,如:股价(股指)变化的非线性性?股价数据具有高噪声等特点,传统的数理统计技术对股市的预测效果并不理想?神经网络具有可以充分逼近任意复杂的非线性关系和有很强的鲁棒性和容错性的特点,所以非常适合用于对股票数据的分析?
本文利用数据挖掘技术对我国股票行情波动趋势进行研究,其目的是为了预测未来股市的行情走势及其波动情况?本文指出了目前传统的数理统计在股票分析上的不足,使用BP神经网络算法对股市进行预测,通过建立一个三层结构的神经网络,即输入层?隐含层和输出层?对数据预处理后得到的数据进行挖掘,将市场普遍采用的技术指标,如科威特第纳尔指数,相对指标的差异等引入模型,得到了较好的预测模型,提高了预测的精度?
The stock market reflects the fluctuation of the market economy, and receives ten million investors’attention since its initial development. The stock market is characterized by high-risk, high-yield, so investors are concerned about the analysis of the stock market and trying to calculate the trend of the stock market. However, stock market is affected by the politics, economy and many other factors, coupled with the complexity of its internal law, such as price (stock index) changes in the non-linear, and shares data with high noise characteristics, therefore the traditional mathematical statistical techniques to forecast the stock market has not yielded satisfactory results. Neural networks can approximate any complex non-linear relations and has robustness and fault-tolerant features. Therefore, it is very suitable for the analysis of stock data.
In this paper, we apply data mining technology to Chinese stock market in order to research the trend of price, it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. After building the data pre-processing set before data mining, lots of widely used stock market technical indicators such as the KD indicators, similarities and differences between exponential smoothing moving average MACD, Relative Strength Index RSI, will be introduced into the model. Finally, we get a better predictive model to improve forecast accuracy.
本文利用数据挖掘技术对我国股票行情波动趋势进行研究,其目的是为了预测未来股市的行情走势及其波动情况?本文指出了目前传统的数理统计在股票分析上的不足,使用BP神经网络算法对股市进行预测,通过建立一个三层结构的神经网络,即输入层?隐含层和输出层?对数据预处理后得到的数据进行挖掘,将市场普遍采用的技术指标,如科威特第纳尔指数,相对指标的差异等引入模型,得到了较好的预测模型,提高了预测的精度?
The stock market reflects the fluctuation of the market economy, and receives ten million investors’attention since its initial development. The stock market is characterized by high-risk, high-yield, so investors are concerned about the analysis of the stock market and trying to calculate the trend of the stock market. However, stock market is affected by the politics, economy and many other factors, coupled with the complexity of its internal law, such as price (stock index) changes in the non-linear, and shares data with high noise characteristics, therefore the traditional mathematical statistical techniques to forecast the stock market has not yielded satisfactory results. Neural networks can approximate any complex non-linear relations and has robustness and fault-tolerant features. Therefore, it is very suitable for the analysis of stock data.
In this paper, we apply data mining technology to Chinese stock market in order to research the trend of price, it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. After building the data pre-processing set before data mining, lots of widely used stock market technical indicators such as the KD indicators, similarities and differences between exponential smoothing moving average MACD, Relative Strength Index RSI, will be introduced into the model. Finally, we get a better predictive model to improve forecast accuracy.
引文
[1]萧文.股票分析指标大全(M).北京:中国经济出版社2004:208-289,81-103
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[3] Jiawei Han,Micheline Kamber.数据挖掘技术与概念,高等教育出版社,2001.
[4]冯嘉礼.人工神经元的一种定性映射解释.计算机科学,2001, 28(9)增刊:248一253.
[5]约翰·迈吉.罗伯特·D·爱德华著股票趋势技术分析,1966.
[6]冯嘉礼.定性映射诱导的模糊人工神经元和网络.南京大学学报,2003, 39 (2):172-181.
[7]谢国民,李文刚.市技术分析高手.企业管理出版社,1997: 32-159.
[8]王正欧,林晨.一种前向神经网络快速学习算法及其在系统辩识中的应用.自动化学报,1997,23(6).
[9]杨一文,刘贵忠.基于小波网络的非线性时间序列预测及其在股市中的应用模式识别与人工智能.
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[14]钱峻,邵惠鹤.小波神经网络结构设计新算法.上海交通人学学报,1999 04.
[15]谭永红.BP算法学习步长的自适应调整.桂林电子工业学院学,1993 12.
[16]胡寿松,周川,王源.基于小波神经网络的组合故障模式识别.2002 vo1.28.
[17]邱永汉.股票入门.电子工业出版社,1998.
[18]冯天谨.神经网络技术.海洋大学出版社.
[19]李焕荣,王树明.一种改进的BP神经网络的预测方法及其研究.系统工程,2000.9,76~78.
[20]张智星,孙春在,水谷英二(日).神经-模糊和软计算.西安交通大学出版社.
[21]刘明月.基于Web日志的用户行为分析.北京交通大学硕士论文, 2008 06.
[22]孙仁诚.基于单元的孤立点算法研究及客户忠诚度分析系统构建.青岛大学硕士论文,2003 04.
[23] Sridhar Ramaswamy, Rajeev Rastogi, Kyuseok Shim: Efficient Algorithms for Mining Outliers from Large Data Sets. SIGMOD Conference 2000: 427-438.
[24] M. M. Breunig, H.-P. Kriegel, R. Ng, J. Sander. LOF: Identifying Density-Based Local Outliers. In ACM SIGMOD Conference Proceedings, 2000.
[25] C. C. Aggarwal and P. Yu. Outlier Detection for High Dimensional Data. In Proc. of ACM SIGMOD 2001.
[26] S. Ramaswamy, R. Rastogi, K. Shim. Efficient Algorithms for Mining Outliers from Large Data Sets. ACM SIGMOD Conference Proceedings, 2000.
[2]张云涛,龚玲.数据挖掘原理与技术,电子工业出版社,2004.
[3] Jiawei Han,Micheline Kamber.数据挖掘技术与概念,高等教育出版社,2001.
[4]冯嘉礼.人工神经元的一种定性映射解释.计算机科学,2001, 28(9)增刊:248一253.
[5]约翰·迈吉.罗伯特·D·爱德华著股票趋势技术分析,1966.
[6]冯嘉礼.定性映射诱导的模糊人工神经元和网络.南京大学学报,2003, 39 (2):172-181.
[7]谢国民,李文刚.市技术分析高手.企业管理出版社,1997: 32-159.
[8]王正欧,林晨.一种前向神经网络快速学习算法及其在系统辩识中的应用.自动化学报,1997,23(6).
[9]杨一文,刘贵忠.基于小波网络的非线性时间序列预测及其在股市中的应用模式识别与人工智能.
[10]沈清,杨霖.模式识别导论,长沙,国防科学技术大学出版社,1991.
[11]边肇棋,张学.T.模式识别,北京,清华大学出版社, 2000.
[12]金钟,胡钟山,杨静宁.基于BP神经网络的人脸识别方法.计算机研究与发展,1999 32.
[13]李银国,张邦礼,曹长修.小波神经网络及其设计方法模式识别与人工智能,1997.
[14]钱峻,邵惠鹤.小波神经网络结构设计新算法.上海交通人学学报,1999 04.
[15]谭永红.BP算法学习步长的自适应调整.桂林电子工业学院学,1993 12.
[16]胡寿松,周川,王源.基于小波神经网络的组合故障模式识别.2002 vo1.28.
[17]邱永汉.股票入门.电子工业出版社,1998.
[18]冯天谨.神经网络技术.海洋大学出版社.
[19]李焕荣,王树明.一种改进的BP神经网络的预测方法及其研究.系统工程,2000.9,76~78.
[20]张智星,孙春在,水谷英二(日).神经-模糊和软计算.西安交通大学出版社.
[21]刘明月.基于Web日志的用户行为分析.北京交通大学硕士论文, 2008 06.
[22]孙仁诚.基于单元的孤立点算法研究及客户忠诚度分析系统构建.青岛大学硕士论文,2003 04.
[23] Sridhar Ramaswamy, Rajeev Rastogi, Kyuseok Shim: Efficient Algorithms for Mining Outliers from Large Data Sets. SIGMOD Conference 2000: 427-438.
[24] M. M. Breunig, H.-P. Kriegel, R. Ng, J. Sander. LOF: Identifying Density-Based Local Outliers. In ACM SIGMOD Conference Proceedings, 2000.
[25] C. C. Aggarwal and P. Yu. Outlier Detection for High Dimensional Data. In Proc. of ACM SIGMOD 2001.
[26] S. Ramaswamy, R. Rastogi, K. Shim. Efficient Algorithms for Mining Outliers from Large Data Sets. ACM SIGMOD Conference Proceedings, 2000.