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The Problem of Bankruptcy in Listed Companies

Author

Listed:
  • Waldemar Tarczynski
  • Malgorzata Tarczynska-Luniewska
  • Kinga Flaga-Gieruszynska

Abstract

Purpose: The paper presents an investigation of the bankruptcy of companies listed on the Warsaw Stock Exchange using the Fundamental Power Index in dynamic terms (FPI). Design/Methodology/Approach: The methodology of the Fundamental Power Index (FPI) was used to assess bankruptcy. In general, the essence of the indicator is a synthetic assessment of the company's fundamental strength. The indicator can take high and low values. The appearance of low levels of the ratio for the company is not favourable and indicates a problem in the financial standing area. As a consequence, the level of the ratio may signal a risk of bankruptcy. The article also discusses the legal grounds for bankruptcy of companies in Poland and selected EU countries. Findings: The results of the conducted research indicate that FPI may be a useful tool of early warning against bankruptcy. The dynamic approach to the index allowed for the assessment of the fundamental strength of the companies in the period of five years. At the same time, the level of the index indicated the risk of bankruptcy. The basis for the construction of the ratio was the financial data from the financial statements of the examined entities. In particular, information on financial ratios from the following groups was used: liquidity, profitability, debt and operational efficiency. Practical Implications: The implementation of the indicator concerns many areas, including investing, assessment of companies or the stock market. In the event of bankruptcy, information about the level of the ratio may support the management process of the company and early response of managers to avoid bankruptcy (e.g. by introducing recovery or restructuring programs). For the investor, the information about the low level of the ratio is a signal for actions related to risk management. Originality/Value: The results of the study reflect the applicability and effectiveness of the proposed indicator. Consequently, the fundamental strength index may constitute an alternative to the existing methods of assessing the bankruptcy process in enterprises.

Suggested Citation

  • Waldemar Tarczynski & Malgorzata Tarczynska-Luniewska & Kinga Flaga-Gieruszynska, 2020. "The Problem of Bankruptcy in Listed Companies," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 3-15.
  • Handle: RePEc:ers:journl:v:xxiii:y:2020:i:special2:p:3-15
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    References listed on IDEAS

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    1. 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.
    2. Halpern, Paul & Kieschnick, Robert & Rotenberg, Wendy, 2009. "Determinants of financial distress and bankruptcy in highly levered transactions," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 772-783, August.
    3. Panayiotis G. Curtis & Michael Hanias & Eleftherios Kourtis & Mixalis Kourtis, 2020. "Data Envelopment Analysis (DEA) and Financial Ratios: Α Pro-Stakeholders’ View of Performance Measurement for Sustainable Value Creation of the Wind Energy," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(2), pages 326-350.
    4. Tian, Shaonan & Yu, Yan, 2017. "Financial ratios and bankruptcy predictions: An international evidence," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 510-526.
    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. Erginbay Ugurlu & Eleftherios Thalassinos & Yusuf Muratoglu, 2014. "Modeling Volatility in the Stock Markets using GARCH Models: European Emerging Economies and Turkey," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 72-87.
    7. Dorota Witkowska, 2006. "Discrete Choice Model Application to the Credit Risk Evaluation," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 12(1), pages 33-42, February.
    8. Thomas Eger, 2001. "Bankruptcy Regulations and the New German Insolvency Law from an Economic Point of View," European Journal of Law and Economics, Springer, vol. 11(1), pages 29-46, January.
    9. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    10. Closset, Frédéric & Urban, Daniel, 2019. "The balance of power between creditors and the firm: Evidence from German insolvency law," Journal of Corporate Finance, Elsevier, vol. 58(C), pages 454-477.
    11. Eleftherios Thalassinos & Theodore V. Stamatopoulos, 2015. "The Trilemma and the Eurozone: A Pre-announced Tragedy of the Hellenic Debt Crisis," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 27-40.
    12. 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.
    13. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
    14. repec:kap:iaecre:v:12:y:2006:i:1:p:33-42 is not listed on IDEAS
    15. Eleftherios I. Thalassinos & Theodoros Stamatopoulos & Pantelis E. Thalassinos, 2015. "The European Sovereign Debt Crisis and the Role of Credit Swaps," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 20, pages 605-639, World Scientific Publishing Co. Pte. Ltd..
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    More about this item

    Keywords

    Bankruptcy; FPI index; economic and law aspects of bankruptcy.;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • K22 - Law and Economics - - Regulation and Business Law - - - Business and Securities Law

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