YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Construction Engineering and Management
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Construction Engineering and Management
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Predicting Loss for Large Construction Companies

    Source: Journal of Construction Engineering and Management:;2013:;Volume ( 139 ):;issue: 009
    Author:
    Titilola Adeleye
    ,
    Meng Huang
    ,
    Zhenhua Huang
    ,
    Lili Sun
    DOI: 10.1061/(ASCE)CO.1943-7862.0000696
    Publisher: American Society of Civil Engineers
    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.
    • Download: (307.9Kb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Predicting Loss for Large Construction Companies

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/58858
    Collections
    • Journal of Construction Engineering and Management

    Show full item record

    contributor authorTitilola Adeleye
    contributor authorMeng Huang
    contributor authorZhenhua Huang
    contributor authorLili Sun
    date accessioned2017-05-08T21:39:59Z
    date available2017-05-08T21:39:59Z
    date copyrightSeptember 2013
    date issued2013
    identifier other%28asce%29co%2E1943-7862%2E0000707.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/58858
    description abstractLoss 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.
    publisherAmerican Society of Civil Engineers
    titlePredicting Loss for Large Construction Companies
    typeJournal Paper
    journal volume139
    journal issue9
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/(ASCE)CO.1943-7862.0000696
    treeJournal of Construction Engineering and Management:;2013:;Volume ( 139 ):;issue: 009
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian