IDEAS home Printed from https://ideas.repec.org/a/ksa/szemle/1684.html
   My bibliography  Save this article

Magyar vállalkozások felszámolásának előrejelzése pénzügyi mutatóik idősorai alapján
[Predicting the liquidation of Hungarian firms using a time series of their financial ratios]

Author

Listed:
  • Virág, Miklós
  • Nyitrai, Tamás

Abstract

A vállalatok felszámolásának előrejelzésében általános gyakorlat a számviteli adatokból kapott hányados típusú pénzügyi mutatók használata. E mutatókat általában csak az utolsó lezárt üzleti év adatai alapján kalkulálják. Az így felépített modellek azonban statikus jellegűek, s nem veszik figyelembe a vállalati gazdálkodás folyamatjellegét. E hiányosság kiküszöbölésére korábban Nyitrai [2014a] tett kísérletet a statikus pénzügyi mutatószámok idősoraiból képzett, úgynevezett dinamikus pénzügyi mutatók használatával - azonban számos, önkényesnek tűnő feltételezéssel élt, amelyek közül tanulmányunkban kettőt feloldunk. Az idézett cikk csak döntési fák segítségével vizsgálta a pénzügyi mutatók időbeli trendjét kifejező változó hatékonyságát. Most e megközelítés hatását a modellek előrejelző képességére a - szakirodalomban általánosan elterjedt - logisztikus regresszió keretei között vizsgáljuk meg. Nyitrai [2014a] a pénzügyi mutatók teljes idősorait felhasználta, ennek szükségessége kérdéses lehet, ezért megnézzük a csődmodellek előrejelző képességét annak függvényében, hogy hány évre visszamenően vesszük figyelembe a pénzügyi mutatók értékeit.* Journal of Economic Literature (JEL) kód: C52, C53, G33.

Suggested Citation

  • Virág, Miklós & Nyitrai, Tamás, 2017. "Magyar vállalkozások felszámolásának előrejelzése pénzügyi mutatóik idősorai alapján [Predicting the liquidation of Hungarian firms using a time series of their financial ratios]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(3), pages 305-324.
    • Handle: RePEc:ksa:szemle:1684
    DOI: 10.18414/KSZ.2017.3.305
    as

    Download full text from publisher

    File URL: http://www.kszemle.hu/tartalom/letoltes.php?id=1684
    Download Restriction: Registration and subscription. 3-month embargo period to non-subscribers.
    File URL: https://libkey.io/10.18414/KSZ.2017.3.305?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
    ---><---

    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. 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.
    2. Lili Sun, 2007. "A re-evaluation of auditors’ opinions versus statistical models in bankruptcy prediction," Review of Quantitative Finance and Accounting, Springer, vol. 28(1), pages 55-78, January.
    3. 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.
    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. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    6. Emel, Ahmet Burak & Oral, Muhittin & Reisman, Arnold & Yolalan, Reha, 2003. "A credit scoring approach for the commercial banking sector," Socio-Economic Planning Sciences, Elsevier, vol. 37(2), pages 103-123, June.
    7. Péter Bauer & Marianna Endrész, 2016. "Modelling Bankruptcy Using Hungarian Firm-Level Data," MNB Occasional Papers 2016/122, Magyar Nemzeti Bank (Central Bank of Hungary).
    8. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    9. 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.
    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. Tamara Ayœs, Armando Lenin & Villegas, Gladis Cecilia & Leones Castro, María Cristina & Salazar Bocanegra, Juan Antonio, 2018. "Modelaci—n del riesgo de insolvencia en empresas del sector salud empleando modelos logit || Modeling of Insolvency Risk in Health Sector Companies Using Logit Models," 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 128-145, Diciembre.
    2. Ilyes Abid & Rim Ayadi & Khaled Guesmi & Farid Mkaouar, 2022. "A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction," Annals of Operations Research, Springer, vol. 313(2), pages 605-623, June.
    3. Nyitrai, Tamás & Virág, Miklós, 2019. "The effects of handling outliers on the performance of bankruptcy prediction models," Socio-Economic Planning Sciences, Elsevier, vol. 67(C), pages 34-42.
    4. Luca Sensini, 2016. "An Empirical Analysis of Financially Distressed Italian Companies," International Business Research, Canadian Center of Science and Education, vol. 9(10), pages 75-85, October.
    5. Tamás Kristóf & Miklós Virág, 2020. "A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary," JRFM, MDPI, vol. 13(2), pages 1-20, February.
    6. 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.
    7. Velia Gabriella Cenciarelli & Marco Maria Mattei & Giulio Greco, 2020. "Pressione competitiva e previsione dell?insolvenza," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2020(3), pages 35-58.
    8. 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.
    9. 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.
    10. 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.
    11. Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179, arXiv.org.
    12. Sumani Sumani, 2019. "The Structure of Good Corporate Governance and Financial Indicators as Predictor of Financial Distress in Mining Sector Company in Indonesia," Research in Business and Management, Macrothink Institute, vol. 6(1), pages 1-12, February.
    13. Chih‐Chun Chen & Chun‐Da Chen & Donald Lien, 2020. "Financial distress prediction model: The effects of corporate governance indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1238-1252, December.
    14. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    15. Eric Séverin & David Veganzones, 2021. "Can earnings management information improve bankruptcy prediction models?," Annals of Operations Research, Springer, vol. 306(1), pages 247-272, November.
    16. 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).
    17. Pavol Durana & Lucia Michalkova & Andrej Privara & Josef Marousek & Milos Tumpach, 2021. "Does the life cycle affect earnings management and bankruptcy?," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 425-461, June.
    18. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    19. E. Fedorova A. & M. Chukhlantseva A. & D. Chekrizov V. & ЕЛЕНА Федорова АНАТОЛЬЕВНА & МАРИЯ Чухланцева АЛЕКСАНДРОВНА & ДМИТРИЙ Чекризов ВАСИЛЬЕВИЧ, 2017. "Нормативные значения коэффициентов финансовой устойчивости: особенности видов экономической деятельности // Normative Values of Financial Stability Ratios: Industry-Specific Features," Управленческие науки // Management Science, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 7(2), pages 44-55.
    20. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.

    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
    • 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:1684. 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.