Predicting Business Failure of Construction Contractors Using Long Short-Term Memory Recurrent Neural Network
Publication: Journal of Construction Engineering and Management
Volume 145, Issue 11
Abstract
Predicting business failure of construction contractors is critical for both contractors and other stakeholders such as project owners, surety underwriters, investors, and government entities. To identify a new model with better prediction of business failure of the construction contractors, this study utilized long short-term memory (LSTM) recurrent neural network (RNN). The financial ratios of the construction contractors in the United States were collected, and synthetic minority oversampling technique (SMOTE) and Tomek links were employed to obtain a balanced data set. The proposed LSTM RNN model was evaluated by comparing its accuracy and F1-score with feedforward neural network (FNN) and support vector machine (SVM) models for the optimized parameters selected from a grid search with five-fold cross-validation. The results successfully demonstrate that the prediction performance of the proposed LSTM RNN model outperforms FNN and SVM models for both test and original data set. Therefore, the proposed LSTM RNN model is a promising alternative to assist managers, investors, auditors, and government entities in predicting business failure of construction contractors, and can also be adapted to other industry cases.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT, and Future Planning (No. 2015R1A5A1037548).
References
Abidali, A. F., and F. C. Harris. 1995. “A methodology for predicting company failure in the construction industry.” Constr. Manage. Econ. 13 (3): 189–196. https://doi.org/10.1080/01446199500000023.
Adeleye, T., M. Huang, Z. Huang, and L. Sun. 2013. “Predicting loss for large construction companies.” J. Constr. Eng. Manage. 139 (9): 1224–1236. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000696.
Al-Sobiei, O. S., D. Arditi, and G. Polat. 2005. “Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques.” Constr. Manage. Econ. 23 (4): 423–430. https://doi.org/10.1080/01446190500041578.
Arditi, D., A. Koksal, and S. Kale. 2000. “Business failures in the construction industry.” Eng. Constr. Archit. Manage. 7 (2): 120–132. https://doi.org/10.1108/eb021137.
Bal, J., Y. Cheng, and H. C. Wu. 2013. “Entropy for business failure prediction: An improved prediction model for the construction industry.” Adv. Decis. Sci. 2014: 459751. https://doi.org/10.1155/2013/459751.
Bengio, Y., P. Simard, and P. Frasconi. 1994. “Learning long-term dependencies with gradient descent is difficult.” IEEE Trans. Neural Network 5 (2): 157–166. https://doi.org/10.1109/72.279181.
Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2002. “Synthetic minority over-sampling technique.” J. Artif. Intell. Res. 16 (Jun): 321–357. https://doi.org/10.1613/jair.953.
Chen, J. H. 2012. “Developing SFNN models to predict financial distress of construction companies.” Expert Syst. Appl. 39 (1): 823–827. https://doi.org/10.1016/j.eswa.2011.07.080.
Cheng, M. Y., and N. D. Hoang. 2015. “Evaluating contractor financial status using a hybrid fuzzy instance-based classifier: Case study in the construction industry.” IEEE Trans. Eng. Manage. 62 (2): 184–192. https://doi.org/10.1109/TEM.2014.2384513.
Cheng, M. Y., N. D. Hoang, L. Limanto, and Y. W. Wu. 2014. “A noble hybrid intelligent approach for contractor default status prediction.” Knowl.-Based Syst. 71 (Nov): 314–321. https://doi.org/10.1016/j.knosys.2014.08.009.
Grice, J. S., and M. T. Dugan. 2001. “The limitations of Bankruptcy prediction models: Some cautions for the researcher.” Rev. Quant. Finance Account. 17 (2): 151–166. https://doi.org/10.1023/A:1017973604789.
Heo, J., and J. Y. Yang. 2014. “AdaBoost based bankruptcy forecasting of Korean construction companies.” Appl. Soft Comput. 24 (Nov): 494–499. https://doi.org/10.1016/j.asoc.2014.08.009.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Horta, I. M., and A. S. Camano. 2013. “Company failure prediction in the construction industry.” Expert Syst. Appl. 40 (16): 6253–6257. https://doi.org/10.1016/j.eswa.2013.05.045.
Kangari, R., F. Farid, and H. Elgharib. 1992. “Financial performance analysis for construction industry.” J. Constr. Eng. Manage. 118 (2): 349–361. https://doi.org/10.1061/(ASCE)0733-9364(1992)118:2(349).
Lam, K. C., E. Palaneeswaran, and C. Yu. 2009. “A support vector machine model for contractor prequalification.” Automat. Constr. 18 (3): 321–329. https://doi.org/10.1016/j.autcon.2008.09.007.
Market Report Store. 2016. Global construction outlook 2020. City of Industry, CA: Market Report Store.
Mason, R. J., and F. C. Harris. 1979. “Predicting company failure in the construction industry.” Proc. Inst. Civ. Eng. 66 (2): 301–307. https://doi.org/10.1680/iicep.1979.2356.
Niemann, M., J. H. Schmidt, and M. Neukirchen. 2008. “Improving performance of corporate rating prediction models by reducing financial ratio heterogeneity.” J. Banking Finance 32 (3): 434–446. https://doi.org/10.1016/j.jbankfin.2007.05.015.
Russell, J. S., and H. Zhai. 1996. “Predicting contractor failure using stochastic dynamics of economic and financial variables.” J. Const. Eng. Manage. 122 (2): 183–191. https://doi.org/10.1061/(ASCE)0733-9364(1996)122:2(183).
Samarasinghe, S. 2006. Neural networks for applied sciences and engineering. New York: Taylor & Francis.
Sang, J., N. H. Ham, J. H. Kim, and J. J. Kim. 2014. “Impacts of macroeconomic fluctuations on insolvency: Case of Korean construction companies.” J. Manage. Eng. 20 (5): 05014009. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000235.
Tomek, I. 1976. “Two modifications of CNN.” IEEE Trans. Syst. Man Cybern. 6 (11): 769–772. https://doi.org/10.1109/TSMC.1976.4309452.
Tsai, C. F. 2009. “Feature selection in bankruptcy prediction.” Knowl.-Based Syst. 22 (2): 120–127. https://doi.org/10.1016/j.knosys.2008.08.002.
Tsai, L. K., H. Tserng, H. H. Liao, P. C. Chen, and W. Wang. 2012. “Integration of accounting-based and option-based models to predict construction contractor default.” J. Mar. Sci. Technol. 20 (5): 479–484. https://doi.org/10.6119/JMST-010-1112-1.
Tserng, H. P., P. C. Chen, W. H. Huang, M. C. Lei, and Q. H. Tran. 2014. “Predicting of default probability for construction firms using the logit model.” J. Civ. Eng. Manage. 20 (2): 247–255. https://doi.org/10.3846/13923730.2013.801886.
Tserng, H. P., H. H. Liao, E. J. Jaselskis, L. K. Tsai, and P. C. Chen. 2012. “Predicting construction contractor default with barrier option model option model.” J. Constr. Eng. Manage. 138 (5): 621–630. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000465.
Tserng, H. P., G. F. Lin, L. K. Tsai, and P. C. Chen. 2011. “An enforced support vector machine model for construction contractor default prediction.” Autom. Constr. 20 (8): 1242–1249. https://doi.org/10.1016/j.autcon.2011.05.007.
Tserng, H. P., T. L. Ngo, P. C. Chen, and L. Q. Tran. 2015. “A grey system theory-based default prediction model for construction firms.” Comput.-Aided Civ. Infrastruct. Eng. 30 (2): 120–134. https://doi.org/10.1111/mice.12074.
US Census Bureau. 2016. Construction spending. Suitland, MD: US Census Bureau.
Vapnik, V. N. 1995. The nature of statistical learning theory. New York: Springer.
Wharton Research Data Services. 2018. “Data research service provider.” Accessed August 7, 2019. http://wrds-web.wharton.upenn.edu/wrds/.
Yeh, C. C., D. J. Chi, and M. F. Hsu. 2010. “A hybrid approach of DEA, rough set and support vector machines for business failure prediction.” Expert Syst. Appl. 37 (2): 1535–1541. https://doi.org/10.1016/j.eswa.2009.06.088.
Information & Authors
Information
Published In
Copyright
©2019 American Society of Civil Engineers.
History
Received: Aug 18, 2018
Accepted: Mar 19, 2019
Published online: Aug 27, 2019
Published in print: Nov 1, 2019
Discussion open until: Jan 27, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.