Predicting profitability of listed construction companies based on principal component analysis and support vector machine - Evidence from China

Hong Zhang, Fei Yang, Yang Li, Heng Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)22-28
Number of pages7
JournalAutomation in Construction
Publication statusPublished - 1 Jan 2015


  • Composite profitability index
  • Corporate profitability prediction
  • Listed construction companies
  • Principal component analysis (PCA)
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

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