IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v10y2020i4p2158244020965250.html
   My bibliography  Save this article

Diagnosis of the Domino Effect in Bankruptcy Situations Through Positioning Maps and Their Evolution 10 Years Later

Author

Listed:
  • Josep Patau
  • Antonio Somoza
  • Salvador Torra

Abstract

This work aims to describe how the insolvency of a firm affects other business creditors. Through a sample of small and medium entities in Catalonia for the biennium 2004–2005, the situation of creditors before and after the legal event (insolvency proceedings) has been studied. The results confirm the existence of a substantial change in the composition of the financial structure of these firms (substitution of long-term debt by short-term debt; decrease in solvency and profitability, increase in financial charge, among others) and, therefore, the contagion of liquidity problems. The study covers 2 years before and after the insolvency and is dynamic in the sense that it highlights how the creditors change their source of finance and the ending situation. The main contributions of the object are the effect of insolvency on other companies closely related and also the technique used—multidimensional scaling.

Suggested Citation

  • Josep Patau & Antonio Somoza & Salvador Torra, 2020. "Diagnosis of the Domino Effect in Bankruptcy Situations Through Positioning Maps and Their Evolution 10 Years Later," SAGE Open, , vol. 10(4), pages 21582440209, December.
    • Handle: RePEc:sae:sagope:v:10:y:2020:i:4:p:2158244020965250
    DOI: 10.1177/2158244020965250
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2158244020965250
    Download Restriction: no
    File URL: https://libkey.io/10.1177/2158244020965250?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Johan Eklund & Nadine Levratto & Giovanni B. Ramello, 2020. "Entrepreneurship and failure: two sides of the same coin?," Small Business Economics, Springer, vol. 54(2), pages 373-382, February.
    2. Evi Neophytou & Cecilio Mar Molinero, 2004. "Predicting Corporate Failure in the UK: A Multidimensional Scaling Approach," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5‐6), pages 677-710, June.
    3. Gabriel Jiménez & Jesús Saurina, 2006. "Credit Cycles, Credit Risk, and Prudential Regulation," International Journal of Central Banking, International Journal of Central Banking, vol. 2(2), May.
    4. Beata Gavurova & Miroslava Packova & Maria Misankova & Lubos Smrcka, 2017. "Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(6), pages 1156-1173, November.
    5. Marti Sagarra & Cecilio Mar-Molinero & Miguel García-Cestona, 2015. "Spanish savings banks in the credit crunch: could distress have been predicted before the crisis? A multivariate statistical analysis," The European Journal of Finance, Taylor & Francis Journals, vol. 21(3), pages 195-214, February.
    6. Becchetti, Leonardo & Sierra, Jaime, 2003. "Bankruptcy risk and productive efficiency in manufacturing firms," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2099-2120, November.
    7. Marco Lamieri & Ilaria Sangalli, 2019. "The propagation of liquidity imbalances in manufacturing supply chains: evidence from a spatial auto-regressive approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1377-1401, October.
    8. Blum, M, 1974. "Failing Company Discriminant-Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 12(1), pages 1-25.
    9. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    10. J. Carroll & Jih-Jie Chang, 1970. "Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition," Psychometrika, Springer;The Psychometric Society, vol. 35(3), pages 283-319, September.
    11. 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.
    12. 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.
    13. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    14. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    15. Tor Jacobson & Erik Schedvin, 2015. "Trade Credit and the Propagation of Corporate Failure: An Empirical Analysis," Econometrica, Econometric Society, vol. 83(4), pages 1315-1371, July.
    16. Altman, Edward I., 1977. "Predicting performance in the savings and loan association industry," Journal of Monetary Economics, Elsevier, vol. 3(4), pages 443-466, October.
    17. Libby, R, 1979. "Bankers And Auditors Perceptions Of The Message Communicated By The Audit Report," Journal of Accounting Research, Wiley Blackwell, vol. 17(1), pages 99-122.
    18. Jacobson, Tor & von Schedvin, Erik, 2012. "Trade Credit and the Propagation of Corporate Failure: An Empirical Analysis," Working Paper Series 263, Sveriges Riksbank (Central Bank of Sweden).
    19. Joseph Kruskal, 1976. "More factors than subjects, tests and treatments: An indeterminacy theorem for canonical decomposition and individual differences scaling," Psychometrika, Springer;The Psychometric Society, vol. 41(3), pages 281-293, September.
    20. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    21. Evi Neophytou & Cecilio Mar Molinero, 2004. "Predicting Corporate Failure in the UK: A Multidimensional Scaling Approach," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5-6), pages 677-710.
    22. Green, Paul E & Maheshwari, Arun, 1969. "Common Stock Perception and Preference: An Application of Multidimensional Scaling," The Journal of Business, University of Chicago Press, vol. 42(4), pages 439-457, October.
    23. Pratt, J, 1982. "Post-Cognitive Structure - Its Determinants And Relationship To Perceived Information Use And Predictive Accuracy," Journal of Accounting Research, Wiley Blackwell, vol. 20(1), pages 189-209.
    Full references (including those not matched with items on IDEAS)

    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. Juan García Lara & Beatriz Osma & Evi Neophytou, 2009. "Earnings quality in ex‐post failed firms," Accounting and Business Research, Taylor & Francis Journals, vol. 39(2), pages 119-138.
    2. 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.
    3. Khoja, Layla & Chipulu, Maxwell & Jayasekera, Ranadeva, 2019. "Analysis of financial distress cross countries: Using macroeconomic, industrial indicators and accounting data," International Review of Financial Analysis, Elsevier, vol. 66(C).
    4. 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.
    5. Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.
    6. 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.
    7. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
    8. 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.
    9. 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.
    10. Fayçal Mraihi & Inane Kanzari & Mohamed Tahar Rajhi, 2015. "Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(3), pages 184-205.
    11. 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.
    12. Hsu, Hwa-Hsien & Wu, Chloe Yu-Hsuan, 2014. "Board composition, grey directors and corporate failure in the UK," The British Accounting Review, Elsevier, vol. 46(3), pages 215-227.
    13. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    14. Juraini Zainol Abidin & Nur Adiana Hiau Abdullah & Karren Lee-Hwei Khaw, 2020. "Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models," Capital Markets Review, Malaysian Finance Association, vol. 28(2), pages 29-41.
    15. Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Applicability of O-score model for Pakistani firms," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(2), pages 389-401, April.
    16. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," 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. 26(1), pages 294-314, Diciembre.
    17. Yung-Ho Chiu & Yu-Chuan Chen & Yu Han Hung, 2009. "Basel II and bank bankruptcy analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 16(18), pages 1843-1847.
    18. repec:ctc:sdimse:dime19_03 is not listed on IDEAS
    19. 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.
    20. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris Nanterre, EconomiX.
    21. repec:ctc:sdimse:dime21_01 is not listed on IDEAS
    22. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..

    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:sae:sagope:v:10:y:2020:i:4:p:2158244020965250. 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: SAGE Publications (email available below). General contact details of provider: .

    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.