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Elbarkouky, M M G and Fayek, A R (2011) Fuzzy Similarity Consensus Model for Early Alignment of Construction Project Teams on the Extent of Their Roles and Responsibilities. Journal of Construction Engineering and Management, 137(06), 432–40.
Evia, C (2011) Localizing and Designing Computer-Based Safety Training Solutions for Hispanic Construction Workers. Journal of Construction Engineering and Management, 137(06), 452–9.
Hong, T, Cho, K, Hyun, C and Han, S (2011) Simulation-Based Schedule Estimation Model for ACS-Based Core Wall Construction of High-Rise Building. Journal of Construction Engineering and Management, 137(06), 393–402.
Lee, S, Jeon, R, Kim, J and Kim, J (2011) Strategies for Developing Countries to Expand Their Shares in the Global Construction Market: Phase-Based SWOT and AAA Analyses of Korea. Journal of Construction Engineering and Management, 137(06), 460–70.
Ozorhon, B, Arditi, D, Dikmen, I and Birgonul, M T (2011) Toward a Multidimensional Performance Measure for International Joint Ventures in Construction. Journal of Construction Engineering and Management, 137(06), 403–11.
Said, H and El-Rayes, K (2011) Optimizing Material Procurement and Storage on Construction Sites. Journal of Construction Engineering and Management, 137(06), 421–31.
Shen, L, Wu, Y and Zhang, X (2011) Key Assessment Indicators for the Sustainability of Infrastructure Projects. Journal of Construction Engineering and Management, 137(06), 441–51.
Tserng, H P, Liao, H, Tsai, L K and Chen, P (2011) Predicting Construction Contractor Default with Option-Based Credit Models—Models’ Performance and Comparison with Financial Ratio Models. Journal of Construction Engineering and Management, 137(06), 412–20.
- Type: Journal Article
- Keywords: Construction management; Contractors; Risk management; Financial factors; Construction contractor; Default prediction; Credit risk; Option-based model;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000311
- 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.