Predicting profitability of listed construction companies based on principal component analysis and support vector machine—Evidence from China |
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| Affiliation: | 1. Department of Construction Management, Tsinghua University, Beijing, China;2. Department of Building and Real Estate, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China;1. Department of Environmental Management, Faculty of Architecture, Wroc?aw University of Science and Technology, Wroc?aw, Poland;2. Department of Building Physics and Computer Design Methods, Faculty of Civil Engineering, Wroc?aw University of Science and Technology, Wroc?aw, Poland;1. Administración Nacional de Combustibles, Alcohol y Pórtland, Montevideo, Uruguay;2. Departamento de Investigación Operativa, Facultad de Ingeniería, Universidad de la República Montevideo, Uruguay;1. Department of applied engineering, Millersville University, PO Box 1002, Millersville, PA 17551-0302, USA;2. School of building construction, Georgia Institute of Technology, 280 Ferst Drive NW, Atlanta, GA 30332-0155, USA |
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| 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. |
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