Application of computational verb theory to analysis of stock market data

Author

Zhang, Mengfan ; Yang, Tao

Author_Institution

Dept. of Electron. Eng., Xiamen Univ., Xiamen, China

fYear

2010

Firstpage

261

Lastpage

264

Abstract

In this paper, computational verb theory (CVT) is applied to the analysis of stock market data. By using CVT, stock market data are clustered into different categories and represented by typical curves for each category. In this paper, researches on the market data samples from Shanghai Stock Exchange in March 2010 are reported. Firstly, MATLAB programs are used to preprocess the stock data. The preprocess consists of curve smoothing, which is achieved by low-pass filtering, and normalization. Secondly, computational verb similarities are used to process the smoothed time series by comparing them with standard computational verbs. Thirdly, Kmeans clustering algorithm is used to cluster the stock data and yields the most representative curves in the stock market.

Keywords

computational linguistics; data analysis; pattern clustering; stock markets; time series; Matlab programs; Shanghai Stock Exchange; computational verb theory; curve smoothing process; data analysis; k-means clustering algorithm; low-pass filtering; normalization; smoothed time series; stock market data; Clustering algorithms; Consumer electronics; Databases; Natural languages; Pragmatics; Stock markets; Time series analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Anti-Counterfeiting Security and Identification in Communication (ASID), 2010 International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-6731-0

Type

conf

DOI

10.1109/ICASID.2010.5551335

Filename

5551335