Publications / 2017 Proceedings of the 34rd ISARC, Taipei, Taiwan

Comparison of Single Classifier Models for Predicting Long-term Business Failure of Construction Companies Using Finance-based Definition of the Failure

Hyunchul Choi, Hyojoo Son, Changwan Kim, Hyeonwoo Seong, Hyukman Cho and Sungwook Lee
Pages 282-287 (2017 Proceedings of the 34rd ISARC, Taipei, Taiwan, ISBN 978-80-263-1371-7, ISSN 2413-5844)

This study compares the performance of six classifier models (ANN, KNN, C4.5, SVM, LR, and NB) for predicting the business failure of construction companies after three years from 2009 to 2012. Although previous studies have explored numerous business failure prediction models for construction companies, these models have focused on short-term failure, defined as failure occurring within one year, and have defined business failure based on companies’ experiencing serious legal events, including bankruptcy, delisting, and default. However, the construction industry is typically characterized by projects with longer durations, usually exceeding one year. This implies that previous short-term models cannot predict the business failure of construction companies until the end of particular projects. Moreover, this problem is compounded by the fact that legal events can involve lengthy proceedings, which are often initiated much later than the actual moment of the business failure. Therefore, in this study, six classifier models will be used to predict the business failure of the construction companies within three years using a finance-based definition of failure. The results show that all six models’ performances noticeably decrease when they predict more than one year. These results demonstrate that previous short-term prediction models with outstanding performance cannot be practical in predicting the long-term business failure of construction companies.

Keywords: Data Mining, Machine Learning, Project Management, Business failure, Prediction model