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Az első hazai csődmodell újraszámítása neurális hálók segítségével
[Recalculation of the first Hungarian bankruptcy-prediction model using neural networks]

Author

Listed:
  • Virág, Miklós

    ()

  • Kristóf, Tamás

Abstract

A tanulmány arra a kérdésre keresi a választ, hogy Magyarországon is megbízha tóbbnak bizonyulnak-e a legkorszerűbb csődelőrejelzési módszerek a hagyományos matematikai-statisztikai eljárásoknál. Az első hazai csődmodell adatbázisán végre hajtott szimulációs kísérletek egyértelműen azt bizonyítják, hogy a mesterséges neurális hálókkal elkészített csődmodellek magasabb besorolási pontossággal ren delkeznek, mint azok a modellek, amelyeket az 1990-es években diszkriminanciaana lízis és logisztikus regresszió alapján dolgoztak ki. A tanulmány az eredmények be mutatásán kívül elemzi az eltérések okait, és konstruktív javaslatokat fogalmaz meg a hazai csődelőrejelzési gyakorlat fejlesztésére.* Journal of Economic Literature (JEL) kód: C45, C53, G33.

Suggested Citation

  • 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.
  • Handle: RePEc:ksa:szemle:744
    as

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    References listed on IDEAS

    as
    1. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    2. Steven Gonzalez, "undated". "Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models," Working Papers-Department of Finance Canada 2000-07, Department of Finance Canada.
    3. 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.
    4. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
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    6. repec:bla:joares:v:22:y:1984:i::p:59-82 is not listed on IDEAS
    7. Yochanan Shachmurove, 2002. "Applying Artificial Neural Networks to Business, Economics and Finance," Penn CARESS Working Papers 5ecbb5c20d3d547f357aa1306, Penn Economics Department.
    8. 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.
    9. Olmeda, Ignacio & Fernandez, Eugenio, 1997. "Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 10(4), pages 317-335, November.
    10. Megyeri, Krisztina, 2001. "A pénz mint általános csereeszköz modellezése
      [Modelling money as a general medium of exchange]
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(4), pages 307-319.
    11. Selwyn Piramuthu & Harish Ragavan & Michael J. Shaw, 1998. "Using Feature Construction to Improve the Performance of Neural Networks," Management Science, INFORMS, vol. 44(3), pages 416-430, March.
    12. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. " Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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    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.

    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • 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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