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Adeleye, T, Huang, M, Huang, Z and Sun, L (2013) Predicting Loss for Large Construction Companies. Journal of Construction Engineering and Management, 139(09), 1224–36.
- Type: Journal Article
- Keywords: Construction industry; Business management; Financial factors; Predictions; Construction companies; Construction industry; Loss; Business administration; Financial distress; Predictions; Quantitative methods;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000696
- Abstract:
Loss is a form of financial distress that refers to total costs of a company exceeding its total revenues. The purpose of this study is to develop models to predict the occurrence of future loss for large construction companies. To ensure the models are useful to both internal and external stakeholders, the study employs a broad range of financial accounting and market variables calculated from publicly available data. The main part of the study develops two models: a full model consisting of 17 predictors, and a reduced model consisting of 11 selected predictors. Both models are developed using a training sample consisting of 959 loss firm-years and 2,313 nonloss firm-years and are validated using a test sample consisting of 368 loss firm-years and 1,035 nonloss firm-years during a sample period spanning the years 1976 to 2010. The models suggest that the level of sales revenue, average sales revenue generated by one unit of total asset, net worth per unit of fixed assets, operating expenses, leverage, presence of special items and foreign transactions, and type of stock exchange are useful factors for predicting loss status. The models also indicate that construction companies engaging in material manufacture and fabrication and those engaging in design and consulting are more likely to experience loss than other types of construction companies. Both models have fairly good out-of-sample prediction accuracy, that is, approximately 74% accuracy in predicting loss and 70% accuracy in predicting nonloss status. Users may find the reduced model more appealing because it involves fewer predictors but offers comparable prediction accuracy. In addition, the research develops a model for predicting future loss in 2 years and a model for predicting a high level of loss the next year. This paper contributes to the scarce literature on construction companies’ financial distress. The models developed in the paper are useful to give stakeholders an early warning about a construction company’s declining financial health, information that will help investors to make better investment decisions and provide notice to executives so they can take necessary steps to prevent more severe financial distress.
Alsamadani, R, Hallowell, M R, Javernick-Will, A and Cabello, J (2013) Relationships among Language Proficiency, Communication Patterns, and Safety Performance in Small Work Crews in the United States. Journal of Construction Engineering and Management, 139(09), 1125–34.
Cruz, C O and Marques, R C (2013) Exogenous Determinants for Renegotiating Public Infrastructure Concessions: Evidence from Portugal. Journal of Construction Engineering and Management, 139(09), 1082–90.
Damci, A, Arditi, D and Polat, G (2013) Multiresource Leveling in Line-of-Balance Scheduling. Journal of Construction Engineering and Management, 139(09), 1108–16.
Franz, B W, Leicht, R M and Riley, D R (2013) Project Impacts of Specialty Mechanical Contractor Design Involvement in the Health Care Industry: Comparative Case Study. Journal of Construction Engineering and Management, 139(09), 1091–7.
Hanna, A S, Thomas, G and Swanson, J R (2013) Construction Risk Identification and Allocation: Cooperative Approach. Journal of Construction Engineering and Management, 139(09), 1098–107.
Hegazy, T, Abdel-Monem, M, Saad, D A and Rashedi, R (2013) Hands-On Exercise for Enhancing Students’ Construction Management Skills. Journal of Construction Engineering and Management, 139(09), 1135–43.
Hollar, D A, Rasdorf, W, Liu, M, Hummer, J E, Arocho, I and Hsiang, S M (2013) Preliminary Engineering Cost Estimation Model for Bridge Projects. Journal of Construction Engineering and Management, 139(09), 1259–67.
Jafari, A and Love, P E D (2013) Quality Costs in Construction: Case of Qom Monorail Project in Iran. Journal of Construction Engineering and Management, 139(09), 1244–9.
Jin, Z, Deng, F, Li, H and Skitmore, M (2013) Practical Framework for Measuring Performance of International Construction Firms. Journal of Construction Engineering and Management, 139(09), 1154–67.
Li, J, Chiang, Y H, Choi, T N Y and Man, K F (2013) Determinants of Efficiency of Contractors in Hong Kong and China: Panel Data Model Analysis. Journal of Construction Engineering and Management, 139(09), 1211–23.
Liu, J Y, Zou, P X W and Gong, W (2013) Managing Project Risk at the Enterprise Level: Exploratory Case Studies in China. Journal of Construction Engineering and Management, 139(09), 1268–74.
Marzouk, M and Amin, A (2013) Predicting Construction Materials Prices Using Fuzzy Logic and Neural Networks. Journal of Construction Engineering and Management, 139(09), 1190–8.
Menesi, W, Golzarpoor, B and Hegazy, T (2013) Fast and Near-Optimum Schedule Optimization for Large-Scale Projects. Journal of Construction Engineering and Management, 139(09), 1117–24.
Shahandashti, S M and Ashuri, B (2013) Forecasting {[}Engineering News-Record{]} Construction Cost Index Using Multivariate Time Series Models. Journal of Construction Engineering and Management, 139(09), 1237–43.
Sunindijo, R Y and Zou, P X W (2013) Conceptualizing Safety Management in Construction Projects. Journal of Construction Engineering and Management, 139(09), 1144–53.
Tas, E, Cakmak, P I and Levent, H (2013) Determination of Behaviors in Building Product Information Acquisition for Developing a Building Product Information System in Turkey. Journal of Construction Engineering and Management, 139(09), 1250–8.
Wang, S, Tang, W and Li, Y (2013) Relationship between Owners’ Capabilities and Project Performance on Development of Hydropower Projects in China. Journal of Construction Engineering and Management, 139(09), 1168–78.
Xie, J and Thomas Ng, S (2013) Multiobjective Bayesian Network Model for Public-Private Partnership Decision Support. Journal of Construction Engineering and Management, 139(09), 1069–81.
Yorucu, V (2013) Construction in an Open Economy: Autoregressive Distributed Lag Modeling Approach and Causality Analysis—Case of North Cyprus. Journal of Construction Engineering and Management, 139(09), 1199–210.
Zhao, X, Hwang, B and Low, S P (2013) Developing Fuzzy Enterprise Risk Management Maturity Model for Construction Firms. Journal of Construction Engineering and Management, 139(09), 1179–89.