Prediction of default probability for construction firms using the logit model

Abstract

Recently, the high incidence of construction firm bankruptcies has underlined the importance of forecasting defaults in the construction industry. Early warning systems need to be developed to prevent or avert contractor default; additionally, this evaluation result could facilitate the selection of firms as collaboration or investment partners. Financial statements are considered one of the key basic evaluation tools for demonstrating firm strength. This investigation provides a framework for assessing the probability of construction contractor default based on financial ratios by using the Logit model. A total of 21 ratios, gathered into five financial groups, are utilized to perform univariate logit analysis and multivariate logit analysis for assessing contractor default probability. The empirical results indicate that using multivariate analysis by adding market factor to the liquidity, leverage, activity and profitability factors can increase the accuracy of default prediction more than using only four financial factors. While considering the market factor in the multivariate Logit model, clear incremental prediction performance appears in 1-year evaluation. This study thus suggests that the market factor comprises important information to increase the prediction performance of the model when applied to construction contractors, particularly in short-term evaluation.

Keywords:

• default probability
• financial ratios
• Logit model
• bankruptcy prediction

Additional information

Notes on contributors

H. Ping Tserng

H. Ping TSERNG is a Full Professor at the Department of Civil Engineering of NationalTaiwan University. He is also a Corresponding Member of Russian Academy of Engineering. He has a PhD degree in Construction Engineering and Management from University of Wisconsin-Madison and he is an Official Reviewer or Editorial Board Member of several international journals. His research interests include advanced techniques for project management, construction finance, knowledge management, management information system, GPS/wireless sensor network, and automation in construction.

Po-Cheng Chen

Po-Cheng CHEN is a PhD candidate in the Department of Civil Engineering at National Taiwan University. His research interests include contractor default prediction, and construction finance.

Wen-Haw Huang

Wen-Haw HUANG is the CEO for Long Reign Development Company. He has a MBA degree from Loyola Marymount University. Currently, he is a part-time PhD student in the Department of Civil Engineering at National Taiwan University. His research interests include contractor prequalification, contractor default prediction, financial management, and construction management.

Man Cheng Lei

Man Cheng LEI is a Master of Science in Civil Engineering from National Taiwan University. Her research interests include construction finance, construction management, and project performance evaluation.

Quang Hung Tran

Quang Hung TRAN is a Master of Science in Civil Engineering from National Taiwan University, Taiwan. His research interests include construction finance, risk management, and construction prequalification.