A grey system theory-based default prediction model for construction firms
Journal
Computer-Aided Civil and Infrastructure Engineering
Journal Volume
30
Journal Issue
2
Pages
120-134
Date Issued
2015
Author(s)
Abstract
As the prediction of construction firm failure is of great importance for owners, contractors, investors, banks, insurance firms, and creditors, previous studies have developed several models for predicting the probability of construction firm default based on financial ratio analysis. However, to be applied, these models require a considerable quantity of data, including normally distributed data, and the models cannot tolerate too many changing factors. Furthermore, most of the approaches produce sample selection biases. To avoid these disadvantages, this study is the first to integrate the grey system theory with all available firm-year samples during the sample period to provide a new method for predicting the probability of construction firm default. This method not only offers an improved rate of prediction accuracy, but it also offers simpler and clearer procedures as a reference for examining firm default probability and ranks all financial ratios in terms of their level of importance. The research collects and analyzes the financial reports of 92 construction firms in the United States. The proposed model includes only eight ranked variables (financial ratios), and it achieves an 84.8% level of accuracy for predicting construction firm default probability. As a result, practitioners may directly use the model as a means of quickly and conveniently examining their firm default probability with the simple procedures.
Type
journal article