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The stability of bankruptcy predictors in the construction and manufacturing industries at various times before bankruptcy

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F17%3APU123990" target="_blank" >RIV/00216305:26510/17:PU123990 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.ekonomie-management.cz/archiv/vyhledavani/detail/1396-the-stability-of-bankruptcy-predictors-in-the-construction-and-manufacturing-industries-at-various-times-before-bankruptcy/" target="_blank" >http://www.ekonomie-management.cz/archiv/vyhledavani/detail/1396-the-stability-of-bankruptcy-predictors-in-the-construction-and-manufacturing-industries-at-various-times-before-bankruptcy/</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.15240/tul/001/2017-2-009" target="_blank" >10.15240/tul/001/2017-2-009</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    The stability of bankruptcy predictors in the construction and manufacturing industries at various times before bankruptcy

  • Original language description

    This article focuses on the design of bankruptcy models, specifi cally the selection of suitable predictors. Previous research has drawn mainly on data concerning manufacturing companies one year before bankruptcy. Our research examines fi nancial ratios that are suitable bankruptcy indicators in two different industries (the construction and manufacturing industries) over a period of fi ve years prior to bankruptcy. Our main objective is to verify whether bankruptcy predictors are industry-specifi c. Another objective was to determine which indicators can detect signs of bankruptcy earlier than one period before bankruptcy. We presume that the application of industryspecifi c indicators can help increase the predictive accuracy of bankruptcy models when applied to a particular industry. Per analogiam, we assume that the inclusion of indicators capable of detecting signs of bankruptcy more than a year before its occurrence will increase their predictive capacity. Signifi cant predictors were fi rst identifi ed on a linear basis using the parametric t-test or F-test; for the sake of comparison, a non-linear non-parametric Boosted Trees method was also applied. Data for a total of 34,229 active companies and 304 companies that went bankrupt during the relevant period was analyzed. The research confi rmed our presumption that bankruptcy predictors are both industry and time specifi c. Four years before bankruptcy, the indicators return on assets, inventory turnover and asset structure are important predictors in both the manufacturing and construction industries. The net working capital to total assets ratio is a specifi c predictor for manufacturing companies in the third year before bankruptcy, as is the short-term indebtedness indicator. In the construction industry, specifi c predictors are the net working capital to sales ratio in the third and fi rst years before bankruptcy, and the interest coverage indicator in all four years preceding bankruptcy. Were these in

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    50202 - Applied Economics, Econometrics

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    E a M: Ekonomie a Management

  • ISSN

    1212-3609

  • e-ISSN

  • Volume of the periodical

    20

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    18

  • Pages from-to

    116-133

  • UT code for WoS article

    000405644600009

  • EID of the result in the Scopus database

    2-s2.0-85021256562