Predicting Construction Contractor Default with Option-Based Credit Models—Models’ Performance and Comparison with Financial Ratio Models

Abstract

Construction contractor evaluation is a critical issue in successfully completing a project. It is important for project owners and other stakeholders to identify potentially failing contractors and to avoid awarding them contracts. Previous studies developed construction contractor default prediction models incorporating managerial or economic variables into traditional financial ratio models to enhance predicting power. However, managerial variables are subjective and qualitative, and both economic variables and financial ratios are only available periodically and may not provide the necessary information in time. This study predicts contractor default by employing three option-based credit models (BSM, CB, and BS) based on stock market information, and the empirical results show that all of the models have strong discriminatory power in ranking contractors from riskiest to safest. The misclassification rates of the three models are BSM: 10%, CB: 10%, and BS: 12.7%, all of which are smaller than that of the enhanced ratio model developed by Russell and Zhai (22%), and two of which are smaller than that of the model developed by Severson and colleagues (12.5%). The results show that option-based credit models are good alternatives for construction contractor default prediction.