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Predicting Insolvency of the Construction Companies in the Creditworthiness Assessment Process—Empirical Evidence from Poland

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  • Rafał Balina

    (Department of Finance, Institute of Economics and Finance, University of Life Sciences, 02-389 Warsaw, Poland)

  • Marta Idasz-Balina

    (Department of Strategy, Kozminski University, 03-301 Warsaw, Poland)

  • Noer Azam Achsani

    (School of Business, Bogor Agricultural University, Bogor 16680, Indonesia)

Abstract

Prediction insolvency is one of the most important issues during creditworthiness assessment, especially in the turmoil environment. That is why the problem of insolvency and bankruptcy prediction has been the subject of numerous studies focused on its causes, consequences, and prediction. The main goal of the study was to develop a prediction model that can be effectively used in practice to analyze and signal the risk of insolvency and bankruptcy of a construction firms. Also, the research must identify the key factors that would allow for early identification of the symptoms of the upcoming financial failure of companies from a construction sector. To reach the goal of the study discriminant analysis, logistic regression and classification trees were used. The final estimated models included nine variables related to the profitability; revenues; liquidity; asset’s structure; and dynamics of own and foreign capitals, some of which referred to the industry and market situation in a construct sector, which is a novelty compared to previous research. What is more, results show that the method chosen to estimate the insolvency prediction model could have an impact on both partial and general effectiveness in the process of creditworthiness assessment.

Suggested Citation

  • Rafał Balina & Marta Idasz-Balina & Noer Azam Achsani, 2021. "Predicting Insolvency of the Construction Companies in the Creditworthiness Assessment Process—Empirical Evidence from Poland," JRFM, MDPI, vol. 14(10), pages 1-16, September.
  • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:10:p:453-:d:640237
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    References listed on IDEAS

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    1. Rafał Balina & Sławomir Juszczyk, 2014. "Forecasting bankruptcy risk of international commercial road transport companies," International Journal of Management and Enterprise Development, Inderscience Enterprises Ltd, vol. 13(1), pages 1-20.
    2. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
    3. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    4. Harlan Platt & Marjorie Platt, 2002. "Predicting corporate financial distress: Reflections on choice-based sample bias," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 26(2), pages 184-199, June.
    5. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    6. Roger Hosein & Timothy Michael Lewis, 2005. "Quantifying the relationship between aggregate GDP and construction value added in a small petroleum rich economy - a case study of Trinidad and Tobago," Construction Management and Economics, Taylor & Francis Journals, vol. 23(2), pages 185-197.
    7. Rafal Balina, 2018. "Forecasting Bankruptcy Risk in The contexts of Credit Risk Management - A Case Study on Wholesale Food Industry in Poland," International Journal of Economic Sciences, International Institute of Social and Economic Sciences, vol. 7(1), pages 1-15, May.
    8. David Alaminos & Agustín del Castillo & Manuel Ángel Fernández, 2016. "A Global Model for Bankruptcy Prediction," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
    9. Katarina Valaskova & Pavol Durana & Peter Adamko & Jaroslav Jaros, 2020. "Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities," JRFM, MDPI, vol. 13(5), pages 1-16, May.
    10. Marianna Succurro, 2017. "Financial Bankruptcy across European Countries," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(7), pages 132-146, July.
    11. Boratyńska, Katarzyna & Grzegorzewska, Emilia, 2018. "Bankruptcy prediction in the agribusiness sector: Lessons from quantitative and qualitative approaches," Journal of Business Research, Elsevier, vol. 89(C), pages 175-181.
    12. Chris Charalambous & Andreas Charitou & Froso Kaourou, 2000. "Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction," Annals of Operations Research, Springer, vol. 99(1), pages 403-425, December.
    13. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    14. Embrechts, Paul, 2004. "Credit Risk. Pricing, Measurement, and Management. Princeton University Press, 2003, Darrell Duffie and Kenneth J. Singleton," ASTIN Bulletin, Cambridge University Press, vol. 34(1), pages 264-265, May.
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    Cited by:

    1. Jun Wang & Mao Li & Martin Skitmore & Jianli Chen, 2024. "Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    2. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.

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