DocumentCode
2763493
Title
Stock Returns Prediction Using Manifold Wavelet Kernel
Author
Tang, Ling-Bing ; Sheng, Huan-Ye ; Tang, Ling-Xiao
Author_Institution
Dept. of Comput. & Electron. Eng., Hunan Bus. Coll., Changsha, China
fYear
2009
fDate
6-7 June 2009
Firstpage
306
Lastpage
309
Abstract
An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine (MWSVM) for forecasting stock returns. The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine (SVM). Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities, the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately. The applicability and validity of MWSVM for stock returns forecast is confirmed through experiments on real-world stock data.
Keywords
economic forecasting; financial data processing; function approximation; learning (artificial intelligence); nonlinear functions; pattern classification; regression analysis; stock markets; support vector machines; time series; wavelet transforms; MWSVM; admissible manifold wavelet kernel function; arbitrary nonlinear function approximation; machine learning; pattern classification; regression method; stock return forecasting; stock return prediction; stock time series; support vector machine; Computer science; Economic forecasting; Educational institutions; Electronic commerce; Geometry; Kernel; Manifolds; Support vector machine classification; Support vector machines; Technology forecasting; MWSVM; stock returns;
fLanguage
English
Publisher
ieee
Conference_Titel
Electronic Commerce and Business Intelligence, 2009. ECBI 2009. International Conference on
Conference_Location
Beijing
Print_ISBN
978-0-7695-3661-3
Type
conf
DOI
10.1109/ECBI.2009.67
Filename
5190462
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