LOGIT MODELS FOR EARLY WARNING OF DISTRESSED CAPITAL PROJECTS

Abstract

The focus of this study is to demonstrate how probabilistic models may be employed to provide early warnings for distressed capital projects. While identifying the key determinants of project performance is important, few studies test discriminatory power of variables for predicting distressed capital projects. Thus, this longitudinal study of 121 capital projects identifies key variables in the initiation and planning phases of projects that differentiate between healthy and distressed projects at completion. Subsequent univariate logistic analysis shows that the Quality variable provides the highest univariate classification accuracy. Hierarchical logistic-regression analysis reveals high classification accuracy and relatively small differences in overall classification rates. Out-of-sample forecasting validation demonstrates that the optimal model provides a reasonably good overall classification rate of 85.37%. Ultimately, our findings suggest that it is feasible to discriminate simultaneously between healthy and distressed projects prior to the project execution phase in the capital facility delivery process, providing an early warning of projects in distress.