Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21278
Title: Predicting profitability of listed construction companies based on principal component analysis and support vector machine - Evidence from China
Authors: Zhang, H
Yang, F
Li, Y
Li, H 
Keywords: Composite profitability index
Corporate profitability prediction
Listed construction companies
Principal component analysis (PCA)
Support vector machine (SVM)
Issue Date: 2015
Publisher: Elsevier
Source: Automation in construction, 2015, v. 53, p. 22-28 How to cite?
Journal: Automation in construction 
Abstract: In order to monitor the operating conditions of the construction industry, this paper incorporates the principal component analysis (PCA) and support vector machine (SVM) to predict the profitability of the construction companies listed on A-share market in China. With annual financial data in 2001-2012, this paper selected six indicators from different profitable perspectives to build a composite profitability index based on the PCA technique, and then established a SVM model to make the corporate profitability prediction of the construction companies in China. The results indicate that, the technical combination of the PCA and SVM can improve the profitability prediction significantly. In 2003-2012, the accuracy of predicting the profitability of the Chinese construction companies exceeded 80% on average. Compared with the artificial neural network (ANN), the SVM model has the superiority in the accuracy prediction of the Chinese construction companies.
URI: http://hdl.handle.net/10397/21278
ISSN: 0926-5805
EISSN: 1872-7891
DOI: 10.1016/j.autcon.2015.03.001
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