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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.