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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
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    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.
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    5. 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.
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 123-127.
    7. 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.
    8. Péter Bauer & Marianna Endrész, 2016. "Modelling Bankruptcy Using Hungarian Firm-Level Data," MNB Occasional Papers 2016/122, Magyar Nemzeti Bank (Central Bank of Hungary).
    9. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 71-111.
    Full references (including those not matched with items on IDEAS)

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    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

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