IDEAS home Printed from https://ideas.repec.org/a/ksa/szemle/995.html
   My bibliography  Save this article

A csődelőrejelzés és a nem fizetési valószínűség számításának módszertani kérdéseiről
[Some methodological questions of bankruptcy prediction and probability of default estimation]

Author

Listed:
  • Kristóf, Tamás

Abstract

A Bázel-2 tőkeegyezmény magyarországi bevezetése új lendületet adott a sokváltozós csőd-előrejelzési módszerek alkalmazásnak és továbbfejlődésének. A cikk a nemzetközi szakirodalomban és pénzintézeti gyakorlatban leggyakrabban alkalmazott négy csőd-előrejelzési módszer becslőképességét hasonlítja össze. Empirikus vizsgálattal alátámasztva igyekszik választ találni arra a kérdésre, vajon a kevésbé szigorú alkalmazási feltételeket támasztó szimulációs eljárások megbízhatóbb csődelőrejelzést, valamint a nem fizetési valószínűségek jobb becslését tesznek-e lehetővé, mint a hagyományos matematikai-statisztikai alapú eljárások. Az empirikus vizsgálat eredményei arra is rávilágítanak, hogy a főkomponens-elemzéssel nem feltétlenül növekszik az előrejelző képesség. Journal of Economic Literature (JEL) kód: C52, C53, C45, G33.

Suggested Citation

  • Kristóf, Tamás, 2008. "A csődelőrejelzés és a nem fizetési valószínűség számításának módszertani kérdéseiről [Some methodological questions of bankruptcy prediction and probability of default estimation]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 441-461.
  • Handle: RePEc:ksa:szemle:995
    as

    Download full text from publisher

    File URL: http://www.kszemle.hu/tartalom/letoltes.php?id=995
    Download Restriction: Registration and subscription. 3-month embargo period to non-subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Benedek, Gábor, 2000. "Evolúciós alkalmazások előrejelzési modellekben I [Evolutionary applications in forecasting models, Part I]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(12), pages 988-1007.
    2. 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.
    3. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    4. Engelmann, Bernd & Hayden, Evelyn & Tasche, Dirk, 2003. "Measuring the Discriminative Power of Rating Systems," Discussion Paper Series 2: Banking and Financial Studies 2003,01, Deutsche Bundesbank.
    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. Nyitrai, Tamás, 2014. "Növelhető-e a csőd-előrejelző modellek előre jelző képessége az új klasszifikációs módszerek nélkül? [Can the predictive capacity of bankruptcy forecasting models be increased without new classific," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 566-585.

    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. 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.
    2. 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.
    3. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
    4. 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.
    5. 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.
    6. Laurentiu DROJ & Goran KARANOVIC & Ioan Gheorghe TARA, 2021. "The Impact Of The Covid-19 Pandemics Over The Financial Performance At The Level Of The Main Pharmaceutical Operating In Central And Eastern Europe," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 30(2), pages 283-290, December.
    7. 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.
    8. 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.
    9. Moro Russ A. & Härdle Wolfgang K. & Schäfer Dorothea, 2017. "Company rating with support vector machines," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 55-67, June.
    10. En-Der Su & Shih-Ming Huang, 2010. "Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(3), pages 209-239, September.
    11. 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.
    12. 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.
    13. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    14. Elisa Ughetto & Andrea Vezzulli, 2011. "What role can mutual guarantee consortia play for financing innovation? A firm-level study for Italy," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 3(4), pages 294-319.
    15. 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.
    16. 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..
    17. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
    18. Situm Mario, 2014. "Inability of Gearing-Ratio as Predictor for Early Warning Systems," Business Systems Research, Sciendo, vol. 5(2), pages 23-45, September.
    19. Virág, Miklós & Kristóf, Tamás, 2005. "Az első hazai csődmodell újraszámítása neurális hálók segítségével [Recalculation of the first Hungarian bankruptcy-prediction model using neural networks]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 144-162.
    20. Yi Cao & Xiaoquan Liu & Jia Zhai & Shan Hua, 2022. "A two‐stage Bayesian network model for corporate bankruptcy prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 455-472, January.

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • 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:ksa:szemle:995. 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: Odon Sok (email available below). General contact details of provider: http://www.kszemle.hu .

    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.