Technical Papers
Jun 20, 2014

Dynamic Threshold Cash Flow–Based Structural Model for Contractor Financial Prequalification

Publication: Journal of Construction Engineering and Management
Volume 140, Issue 10

Abstract

It is important for project owners to select only those construction contractors who are uniquely qualified to perform the work because this leads to the greatest chance for achieving project success. Owners typically screen contractors by using the following key criteria: financial stability, technical ability, management capability, health and safety, and reputation. This study focuses primarily on the construction contractor’s financial stability during the prequalification phase and employs a dynamic threshold cash flow based structural model (DCFM) to assess the credit quality score for each construction contractor. This model differs from the existing credit model because it only requires accounting statement information; thus, it is applicable to both publicly listed and private construction contractors. Moreover, only a small portion of companies are rated in the construction industry; this model is especially useful for owners to assess the credit quality of unrated construction companies. The Standard & Poor’s issuer credit rating is used as the benchmark to evaluate the model’s discrimination ability to differentiate financially qualified contractors from unqualified contractors. Additionally, the validation indicator area under curve (AUC) is utilized to demonstrate whether the DCFM can identify different credit grade firms according to the model’s credit quality scores. The AUC results of the first three years of this model are 0.861, 0.833, and 0.819, indicating that this model achieves excellent discriminatory ability and is useful for assessing the credit risk of construction contractors.

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References

Abidali, A. F., and Harris, F. C. (1995). “A methodology for predicting company failure in the construction industry.” Constr. Manage. Econ., 13(3), 189–196.
Altman, E. I. (1968). “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy.” J. Finance, 23(4), 589–609.
Chen, T. K., Liao, H. H., and Lu, C. W. (2011). “A flow based corporate credit model.” Rev. Quant. Finance Account., 36(4), 517–532.
Collin-Dufresne, P., and Goldstein, R. S. (2001). “Do credit spreads reflect stationary leverage ratios?” J. Finance, 56(5), 1929–1957.
Duffie, D., and Lando, D. (2001). “Term structures of credit spreads with incomplete accounting information.” Econometrica, 69(3), 633–644.
El-Sawalhi, N., Eaton, D., and Rustom, R. (2007). “Contractor prequalification model: State-of-the-art.” Int. J. Proj. Manage., 25(5), 465–474.
Fong, P. S.-W., and Choi, S. K. Y. (2000). “Final contractor selection using the analytical hierarchy process.” Constr. Manage. Econ., 18(5), 547–557.
Gharghori, P., Chan, H., and Faff, R. (2006). “Investigating the performance of alternative default-risk models: Option-based versus accounting-based approaches.” Aust. J. Manage., 31(2), 207–234.
Giesecke, K. (2004). “Correlated default with incomplete information.” J. Bank. Finance, 28(7), 1521–1545.
Holt, G. D., Olomolaiye, P. O., and Harris, F. C. (1994). “Applying multi-attribute analysis to contractor selection decisions.” Eur. J. Purch. Supply Manage., 1(3), 139–148.
Hosmer, D. W., and Lemeshow, S. (2000). Applied logistic regression, 2nd Ed., Wiley, New York.
Huang, W. H., Tserng, H. H., Liao, H. H., Yin, S. Y. L., Chen, P. C., and Lei, M. C. (2013). “Contractor financial prequalification using simulation method based on cash flow model.” Autom. Constr., 35, 254–262.
Ibbotson Associates. (2011). Stocks, bonds, bills and inflation, Chicago.
Khosrowshahi, F. (1999). “Neural network model for contractors’ prequalification for local authority projects.” Eng. Constr. Architect. Manage., 6(3), 315–328.
Lam, K. C., Ng, S. T., Hu, T., and Skitmore, M. (2000). “Decision support system for contractor prequalification—Artificial neural network model.” Eng. Constr. Architect. Manage., 7(3), 251–266.
Lam, K. C., Palaneeswaran, E., and Yu, C. Y. (2009). “A support vector machine model for contractor prequalification.” Autom. Constr., 18(3), 321–329.
Li, Y. W., Nie, X. T., and Chen, S. Y. (2007). “Fuzzy approach to prequalifying construction contractors.” J. Constr. Eng. Manage., 40–49.
Liao, H. H., and Chen, T. K. (2006). “A cash flow based multi-period corporate credit model.” SSRN, 〈http://ssrn.com/abstract=753304〉 (Aug. 26, 2011).
Liao, H. H., Chen, T. K., and Su, Y. H. (2007). “Credit analysis of corporate credit portfolio—A cash flow based conditional independent default approach.” Asian Finance Association 2009 Int. Conf., 〈http://www.fin.ntu.edu.tw/∼liao/CopulaFTM0925NTU.pdf〉 (Aug. 26, 2011).
Mahdi, I. M., Riley, M. J., Fereig, S. M., and Alex, A. P. (2002). “A multi-criteria approach to contractor selection.” Eng. Constr. Architect. Manage., 9(1), 29–37.
Mason, R. J., and Harris, F. C. (1979). “Predicting company failure in the construction industry.” Proc. Inst. Civ. Eng., 66(2), 301–307.
Merna, A., and Smith, N. J. (1990). “Bid evaluation for U.K. public sector construction contractors.” Proc. Inst. Civ. Eng., 1(88), 91–105.
Navon, R. (1994). “Company-level cash-flow management.” J. Constr. Eng. Manage., 22–29.
Nguyen, V. U. (1985). “Tender evaluation by fuzzy sets.” J. Constr. Eng. Manage., 231–243.
Ohlson, J. A. (1980). “Financial ratios and the probabilistic prediction of bankruptcy.” J. Account. Res., 18(1), 109–131.
Park, H. K. S. H., and Russell, J. S. (2005). “Cash flow forecasting model for general contractors using moving weights of cost categories.” J. Manage. Eng., 164–172.
Peer, S., and Rosental, H. (1982). Development of cost flow model for industrialized housing, National Building Research Station, Technion, Haifa, Israel (in Hebrew).
Plebankiewicz, E. (2009). “Contractor prequalification model using fuzzy sets.” J. Civ. Eng. Manage., 15(4), 377–385.
Plebankiewicz, E. (2010). “Construction contractor prequalification from Polish clients’ perspective.” J. Civ. Eng. Manage., 16(1), 57–64.
Plebankiewicz, E. (2012). “A fuzzy sets based contractor prequalification procedure.” Autom. Constr., 22, 433–443.
Reisz, A. S., and Perlich, C. (2007). “A market-based framework for bankruptcy prediction.” J. Financ. Stabil., 3(2), 85–131.
Russell, J. S. (1996). Constructor prequalification: Choosing the best constructor and avoiding constructor failure, ASCE, Reston, VA.
Russell, J. S., and Skibniewski, M. J. (1988). “Decision criteria in contractor prequalification.” J. Manage. Eng., 148–164.
Russell, J. S., and Skibniewski, M. J. (1990). “QUALIFIER-1: Contractor prequalification model.” J. Comput. Civ. Eng., 77–90.
Russell, J. S., Skibniewski, M. J., and Cozier, D. R. (1990). “QUALIFIER-2: Knowledge-based system for contractor prequalification.” J. Constr. Eng. Manage., 157–171.
Russell, J. S., and Zhai, H. (1996). “Predicting contractor failure using stochastic dynamics of economic and financial variables.” J. Constr. Eng. Manage., 183–191.
Schweser, K. (2008). Free cash flow valuation, CFA Institute, New York.
Severson, G. D., Russell, J. S., and Jaselskis, E. J. (1994). “Predicting contract surety bond claims using contractor financial data.” J. Constr. Eng. Manage., 405–420.
Singh, S., and Lakanathan, G. (1992). “Computer-based cash flow model.” Proc., 36th Annual Transactions of the American Association of Cost Engineers, AACE, Morgantown, WV, R.5.1–R.5.14.
Singh, D., and Tiong, R. L. K. (2005). “A fuzzy decision framework for contractor selection.” J. Constr. Eng. Manage., 62–70.
Standard & Poors. (2012). Standard & Poor’s ratings definitions, 〈http://www.standardandpoors.com/spf/general/RatingsDirect_Commentary_979212_06_22_2012_12_42_54.pdf〉 (Mar. 1, 2013).
Statistical Package for Social Sciences (SPSS) [Computer software]. Armonk, NY, IBM Corporation.
Topcu, Y. I. (2004). “A decision model proposal for construction contractor selection in Turkey.” Build. Environ., 39(4), 469–481.
Tserng, H. P., Liao, H. H., Tsai, L. K., and Chen, P. C. (2011). “Predicting construction contractor default with option-based credit models—Models’ performance and comparison with financial ratio models.” J. Constr. Eng. Manage., 412–420.
Tserng, H. P., Lin, G. F., Tsai, L. K., and Chen, P. C. (2011). “An enforced support vector machine model for construction contractor default prediction.” Autom. Constr., 20(8), 1242–1249.
Wharton Research Data Services. (2011). The Wharton School of the University of Pennysylvania, 〈http://wrds.wharton.upenn.edu〉 (Nov. 11, 2011).

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Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 140Issue 10October 2014

History

Received: Oct 15, 2013
Accepted: May 28, 2014
Published online: Jun 20, 2014
Published in print: Oct 1, 2014
Discussion open until: Nov 20, 2014

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Authors

Affiliations

Wen-Haw Huang [email protected]
Ph.D. Candidate, Dept. of Civil Engineering, National Taiwan Univ., No. 1 Roosevelt Rd., Sec. 4, Taipei, Taiwan; and CEO, Long Reign Development Co., 16F-2, No. 76, Sec. 2 Dunhua S. Rd., Taipei 106, Taiwan. E-mail: [email protected]
H. Ping Tserng [email protected]
Professor, Dept. of Civil Engineering, National Taiwan Univ., No. 1 Roosevelt Rd., Sec. 4, Taipei 106, Taiwan (corresponding author). E-mail: [email protected]
Edward J. Jaselskis [email protected]
Professor, Dept. of Civil, Construction, and Environmental Engineering, North Carolina State Univ., Campus Box 7908, Raleigh, NC 27695-7908. E-mail: [email protected]
Dept. of Civil Engineering, National Taiwan Univ., No. 1 Roosevelt Rd., Sec. 4, Taipei 106, Taiwan. E-mail: [email protected]

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