کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
246352 502363 2015 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Predicting profitability of listed construction companies based on principal component analysis and support vector machine—Evidence from China
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی عمران و سازه
پیش نمایش صفحه اول مقاله
Predicting profitability of listed construction companies based on principal component analysis and support vector machine—Evidence from China
چکیده انگلیسی


• PCA and SVM are used to predict the profitability of construction companies in China.
• A composite index for profitability is constructed from six indicators based on PCA.
• The prediction accuracy by SVM has improved compared with the ANN technique.

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Automation in Construction - Volume 53, May 2015, Pages 22–28
نویسندگان
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