IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v50y2016i1p385-398.html
   My bibliography  Save this article

Usage of artificial neural networks for optimal bankruptcy forecasting. Case study: Eastern European small manufacturing enterprises

Author

Listed:
  • T. Slavici
  • S. Maris
  • M. Pirtea

Abstract

Our study aims to present an optimisation method for the forecasting of bankruptcy. To this end, we elaborate and optimise an artificial neural network (ANN) which, based on the situation of real companies in Eastern Europe, can forecast bankruptcy state. After describing the network structure, the performance is evaluated. Using specific statistical methods, a statistical network optimisation is performed. The conclusion is that ANNs are extremely productive in predicting firm bankruptcy, with the forecast accuracy being higher than the accuracy obtained by traditional methods. The results are applicable at an international level, though the target group of this study contains mainly Eastern European Small Manufacturing Enterprises. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • T. Slavici & S. Maris & M. Pirtea, 2016. "Usage of artificial neural networks for optimal bankruptcy forecasting. Case study: Eastern European small manufacturing enterprises," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 385-398, January.
    • Handle: RePEc:spr:qualqt:v:50:y:2016:i:1:p:385-398
    DOI: 10.1007/s11135-014-0154-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-014-0154-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11135-014-0154-0?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. Rebecca Wu, 1997. "Neural network models: Foundations and applications to an audit decision problem," Annals of Operations Research, Springer, vol. 75(0), pages 291-301, January.
    2. Teodora Dogaru & Frank Van Oort & Mark Thissen, 2011. "Agglomeration Economies In European Regions: Perspectives For Objective 1 Regions," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 102(4), pages 486-494, September.
    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. Gintare Giriūniene & Lukas Giriūnas & Mangirdas Morkunas & Laura Brucaite, 2019. "A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania," Economies, MDPI, vol. 7(3), pages 1-20, August.

    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. Patrick Witte & Frank Van Oort & Bart Wiegmans & Tejo Spit, 2013. "Capitalising on Spatiality in European Transport Corridors," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 104(4), pages 510-517, September.
    2. K. Bruns & N. Bosma & M. Sanders & M. Schramm, 2017. "Searching for the existence of entrepreneurial ecosystems: a regional cross-section growth regression approach," Small Business Economics, Springer, vol. 49(1), pages 31-54, June.
    3. Olalere Isaac Opeyemi, 2022. "Predicting Trade Mispricing: A Gaussian Multivariate Anomaly Detection Model ," GATR Journals jber221, Global Academy of Training and Research (GATR) Enterprise.
    4. Dogaru, Teodora & Burger, Martijn & van Oort, Frank & Karreman, Bas, 2014. "The Geography of Multinational Corporations in CEE Countries: Perspectives for Second-Tier City Regions and European Cohesion Policy," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 29, pages 193-214.
    5. Teodora Dogaru & Martijn Burger & Bas Karreman & Frank Oort, 2015. "Functional and Sectoral Division of Labour within Central and Eastern European Countries: Evidence from Greenfield FDI," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 106(1), pages 120-129, February.
    6. Carlos León & José Fernando Moreno & Jorge Cely, 2016. "Whose Balance Sheet is this? Neural Networks for Banks’ Pattern Recognition," Borradores de Economia 959, Banco de la Republica de Colombia.
    7. Nicola Cortinovis & Frank Oort, 2015. "Variety, economic growth and knowledge intensity of European regions: a spatial panel analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 55(1), pages 7-32, October.
    8. Gianni Guastella & Frank G. van Oort, 2015. "Regional Heterogeneity and Interregional Research Spillovers in European Innovation: Modelling and Policy Implications," Regional Studies, Taylor & Francis Journals, vol. 49(11), pages 1772-1787, November.
    9. Frank Oort & Niels Bosma, 2013. "Agglomeration economies, inventors and entrepreneurs as engines of European regional economic development," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 51(1), pages 213-244, August.
    10. Frank van Oort & Stefan de Geus & Teodora Dogaru, 2015. "Related Variety and Regional Economic Growth in a Cross-Section of European Urban Regions," European Planning Studies, Taylor & Francis Journals, vol. 23(6), pages 1110-1127, June.
    11. León, Carlos & Barucca, Paolo & Acero, Oscar & Gage, Gerardo & Ortega, Fabio, 2020. "Pattern recognition of financial institutions’ payment behavior," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    12. Argentino Pessoa, 2014. "Smart Specialisation in the EU: Is it a Bridge between Innovation and Cohesion?," ERSA conference papers ersa14p989, European Regional Science Association.
    13. Dieudonné Tchuente & Serge Nyawa, 2022. "Real estate price estimation in French cities using geocoding and machine learning," Annals of Operations Research, Springer, vol. 308(1), pages 571-608, January.
    14. Niels Bosma & Frank van Oort, 2012. "Agglomeration Economies, Inventors and Entrepreneurs as Engines of European Regional Productivity," Working Papers 12-20, Utrecht School of Economics.

    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:spr:qualqt:v:50:y:2016:i:1:p:385-398. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.