Title :
Stock Selection Based on Data Clustering Method
Author_Institution :
Inf. Eng. Sch., Tianjin Univ. of Commerce, Tianjin, China
Abstract :
Stock selection is stock market participants facing a difficult problem. This article aims to apply clustering in data mining techniques to analyze the financial data and stock trading data, give the classification of stocks, provide the decision support of stock selections for the dealer. The main work include: the construction of the financial data and stock market data, the pre-processing of the data in the data set, the analysis of the data set applying the clustering techniques, giving the stock´s classification, verifying the classification results. The stock selection base on cluster analysis improves the success rate and yield, and have important practical value of guidance of investment decisions.
Keywords :
data analysis; data mining; decision support systems; financial data processing; investment; pattern classification; pattern clustering; stock markets; data clustering method; data mining techniques; data preprocessing; data set analysis; financial data; investment decisions; stock classification; stock market participants; stock selection decision support; stock trading data; Companies; Data mining; Investments; Security; Stock markets; Cluster analysis; Data mining; Stock selection;
Conference_Titel :
Computational Intelligence and Security (CIS), 2011 Seventh International Conference on
Conference_Location :
Hainan
Print_ISBN :
978-1-4577-2008-6
DOI :
10.1109/CIS.2011.345
