IDEAS home Printed from https://ideas.repec.org/a/ers/journl/vxxivy2021i1p99-116.html
   My bibliography  Save this article

Corporate Failure Prediction of Construction Companies in Poland: Evidence from Logit Model

Author

Listed:
  • Andrzej Geise
  • Magdalena Kuczmarska
  • Jarosław Pawlowski

Abstract

Purpose: This paper aims to develop a corporate failure prediction model for construction companies in Poland that allow assessing their financial situation and credit risk. Design/Methodology/Approach: For this purpose, the following research methods have been used, descriptive and comparative analysis, subject literature review, and logit anal-ysis. The Polish construction companies' financial data in this research come from the Emerging Markets Information Service (EMIS). To achieve the main goal of the research, the logit model was built. The significance test, error matrix, and ROC curve were used to assess the quality of the estimated binary logit model. Findings: Based on the research, we identify seven financial indicators that significantly impact the probability of poor financial condition. The following variables are current assets turnover, debt to assets ratio, operating profit to assets, gross profit to assets, oper-ating profit plus amortization to short-term liabilities, current assets to assets ratio, and equity to assets ratio. The research results show that corporate failure prediction models are interesting and important tools to assess the financial situation. Based on the devel-oped model, it has been found that the growth of debts increases the credit risk of construc-tion companies. Moreover, the increase in the share of current assets in the total assets harms the financial condition. Also, the risk of insolvency decreases with growing profita-bility measured by the rate of return on assets. Practical Implications: The built logit model can be beneficial for investment loan provid-ers, insurance companies, and entities selecting contractors in construction projects due to the possibility of the credit risk assessment. Originality/Value: The use of logit models to identify statistically significant corporate failure prediction factors for construction companies in Poland.

Suggested Citation

  • Andrzej Geise & Magdalena Kuczmarska & Jarosław Pawlowski, 2021. "Corporate Failure Prediction of Construction Companies in Poland: Evidence from Logit Model," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 99-116.
  • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:1:p:99-116
    as

    Download full text from publisher

    File URL: https://ersj.eu/journal/1952/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Clive S. Lennox, 1999. "The Accuracy and Incremental Information Content of Audit Reports in Predicting Bankruptcy," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 26(5&6), pages 757-778.
    2. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    3. Charles L. Merwin, 1942. "Financing Small Corporations in Five Manufacturing Industries, 1926–36," NBER Books, National Bureau of Economic Research, Inc, number merw42-1, March.
    4. Franklin Allen & Patrick Bolton, 2004. "Liquidity and Financial Instability: An Introduction," Journal of the European Economic Association, MIT Press, vol. 2(6), pages 925-928, December.
    5. Loredana Cultrera & Xavier Brédart, 2016. "Bankruptcy prediction: the case of Belgian SMEs," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 15(1), pages 101-119, February.
    6. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    7. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    8. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    9. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    10. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    11. Coleen C. Pantalone & Marjorie B. Platt, 1987. "Predicting Failure of Savings & Loan Associations," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 15(2), pages 46-64, June.
    12. Sebastian Klaudiusz Tomczak & Edward Radosiński, 2017. "The effectiveness of discriminant models based on the example of the manufacturing sector," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(3), pages 81-97.
    13. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    14. William F. Messier, Jr. & James V. Hansen, 1988. "Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data," Management Science, INFORMS, vol. 34(12), pages 1403-1415, December.
    15. Ann Gaeremynck & Marleen Willekens, 2003. "The endogenous relationship between audit-report type and business termination: evidence on private firms in a non-litigious environment," Accounting and Business Research, Taylor & Francis Journals, vol. 33(1), pages 65-79.
    16. Clive S. Lennox, 1999. "The Accuracy and Incremental Information Content of Audit Reports in Predicting Bankruptcy," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 26(5‐6), pages 757-778, June.
    17. Samir Trabelsi & Roc He & Lawrence He & Martin Kusy, 2015. "A comparison of Bayesian, Hazard, and Mixed Logit model of bankruptcy prediction," Computational Management Science, Springer, vol. 12(1), pages 81-97, January.
    18. Blum, M, 1974. "Failing Company Discriminant-Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 12(1), pages 1-25.
    19. Abdul Aziz & David C. Emanuel & Gerald H. Lawson, 1988. "Bankruptcy Prediction ‐ An Investigation Of Cash Flow Based Models[1]," Journal of Management Studies, Wiley Blackwell, vol. 25(5), pages 419-437, September.
    20. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    21. Cielen, Anja & Peeters, Ludo & Vanhoof, Koen, 2004. "Bankruptcy prediction using a data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 154(2), pages 526-532, April.
    22. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    23. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    24. Marek Sieminski & Ewa Wedrowska & Krzysztof Krukowski, 2020. "Cultural Aspect of Social Responsibility Implementation in SMEs," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 68-84.
    25. Durica Marek & Valaskova Katarina & Janoskova Katarina, 2019. "Logit business failure prediction in V4 countries," Engineering Management in Production and Services, Sciendo, vol. 11(4), pages 54-64, December.
    26. Tomczak, Sebastian, 2014. "Comparative analysis of liquidity ratios of bankrupt manufacturing companies," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 10(3), pages 1-14.
    27. Gentry, Ja & Newbold, P & Whitford, Dt, 1985. "Classifying Bankrupt Firms With Funds Flow Components," Journal of Accounting Research, Wiley Blackwell, vol. 23(1), pages 146-160.
    28. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    29. Irena Kropsz, 2010. "Financial Liquidity Of The Horticultural Enterprise Ppo Siechnice In Poland," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 5(2), pages 243-252, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Artur Pazdzior & Marta Sokol & Aleksandra Styk, 2021. "The Impact of the COVID-19 Pandemic on the Economic and Financial Situation of the Micro and Small Enterprises from the Construction and Development Industry in Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 751-762.
    2. Michal Pavlicko & Jaroslav Mazanec, 2022. "Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group," Mathematics, MDPI, vol. 10(8), pages 1-22, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    2. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    3. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    4. Bhanu Pratap Singh & Alok Kumar Mishra, 2016. "Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-28, December.
    5. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    6. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    7. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    8. Layla Khoja & Maxwell Chipulu & Ranadeva Jayasekera, 2016. "Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling," Review of Quantitative Finance and Accounting, Springer, vol. 46(3), pages 483-518, April.
    9. Kerstin Lopatta & Mario Albert Gloger & Reemda Jaeschke, 2017. "Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings," Accounting Perspectives, John Wiley & Sons, vol. 16(4), pages 315-343, December.
    10. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    11. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    12. Jackson, Richard H.G. & Wood, Anthony, 2013. "The performance of insolvency prediction and credit risk models in the UK: A comparative study," The British Accounting Review, Elsevier, vol. 45(3), pages 183-202.
    13. Amin Jan & Maran Marimuthu & Muhammad Kashif Shad & Haseeb ur-Rehman & Muhammad Zahid & Ahmad Ali Jan, 2019. "Bankruptcy profile of the Islamic and conventional banks in Malaysia: a post-crisis period analysis," Economic Change and Restructuring, Springer, vol. 52(1), pages 67-87, February.
    14. Hu, Yu-Chiang & Ansell, Jake, 2007. "Measuring retail company performance using credit scoring techniques," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1595-1606, December.
    15. García-Gallego, Ana & Mures-Quintana, María-Jesús, 2013. "La muestra de empresas en los modelos de predicción del fracaso: influencia en los resultados de clasificación || The Sample of Firms in Business Failure Prediction Models: Influence on Classification," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 133-150, June.
    16. repec:ctc:sdimse:dime21_01 is not listed on IDEAS
    17. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    18. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
    19. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    20. Nisansala Wijekoon & A. Abdul Azeez, 2015. "An Integrated Model to Predict Corporate Failure of Listed Companies in Sri Lanka," International Journal of Business and Social Research, LAR Center Press, vol. 5(7), pages 1-14, July.
    21. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.

    More about this item

    Keywords

    Bankruptcy; bankruptcy prediction; construction company; logit analysis; discriminant analysis.;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ers:journl:v:xxiv:y:2021:i:1:p:99-116. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marios Agiomavritis (email available below). General contact details of provider: https://ersj.eu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.