IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-01578092.html
   My bibliography  Save this paper

Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics

Author

Listed:
  • Michalis Doumpos

    (Technical University of Crete [Chania])

  • Kostas Andriosopoulos

    (ESCP Europe - Ecole Supérieure de Commerce de Paris)

  • Emilios Galariotis

    (Audencia Business School)

  • Georgia Makridou
  • Constantin Zopounidis

    (Technical University of Crete [Chania])

Abstract

This study examines the development of corporate failure prediction models for European firms in the energy sector, using a large dataset from 18 countries. The construction of the models is based on a multiple criteria decision aid (MCDA) approach taking into account both ordinal criteria and nominal country-sector effects. The analysis is based on different modeling specifications. First, traditional financial variables are examined, which are then extended with additional country-level data related to the economic and business environment, as well as data about the energy efficiency policies of the countries and the characteristics of their energy markets and networks. The results indicate that energy-related attributes have high discriminating power and add valuable information compared to the other attributes.

Suggested Citation

  • Michalis Doumpos & Kostas Andriosopoulos & Emilios Galariotis & Georgia Makridou & Constantin Zopounidis, 2017. "Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics," Post-Print hal-01578092, HAL.
    • Handle: RePEc:hal:journl:hal-01578092
    DOI: 10.1016/j.ejor.2017.04.024
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wei Xu & Yuchen Pan & Wenting Chen & Hongyong Fu, 2019. "Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine," Energies, MDPI, vol. 12(12), pages 1-20, June.
    2. Sebastian Klaudiusz Tomczak, 2019. "Comparison of the Financial Standing of Companies Generating Electricity from Renewable Sources and Fossil Fuels: A New Hybrid Approach," Energies, MDPI, vol. 12(20), pages 1-20, October.
    3. du Jardin, Philippe, 2021. "Forecasting corporate failure using ensemble of self-organizing neural networks," European Journal of Operational Research, Elsevier, vol. 288(3), pages 869-885.
    4. Sebastian Klaudiusz Tomczak & Anna Skowrońska-Szmer & Jan Jakub Szczygielski, 2020. "Is Investing in Companies Manufacturing Solar Components a Lucrative Business? A Decision Tree Based Analysis," Energies, MDPI, vol. 13(2), pages 1-27, January.
    5. De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
    6. Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Manos & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2020. "Textual Information and IPO Underpricing: A Machine Learning Approach," MPRA Paper 103813, University Library of Munich, Germany.
    7. Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2024. "Machine learning in bank merger prediction: A text-based approach," European Journal of Operational Research, Elsevier, vol. 312(2), pages 783-797.
    8. Koen W. de Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.
    9. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    10. Makridou, Georgia & Doumpos, Michalis & Galariotis, Emilios, 2019. "The financial performance of firms participating in the EU emissions trading scheme," Energy Policy, Elsevier, vol. 129(C), pages 250-259.
    11. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.
    12. 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.
    13. Katsafados, Apostolos & Anastasiou, Dimitris, 2022. "Short-term Prediction of Bank Deposit Flows: Do Textual Features matter?," MPRA Paper 111418, University Library of Munich, Germany.
    14. Zoltán Csedő & József Magyari & Máté Zavarkó, 2022. "Dynamic Corporate Governance, Innovation, and Sustainability: Post-COVID Period," Sustainability, MDPI, vol. 14(6), pages 1-21, March.
    15. 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..
    16. Koen W. de Bock & Kristof Coussement & Stefan Lessmann, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," Post-Print hal-02863245, HAL.
    17. Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
    18. Silvia Angilella & Maria Rosaria Pappalardo, 2022. "Performance assessment of energy companies employing Hierarchy Stochastic Multi-Attribute Acceptability Analysis," Operational Research, Springer, vol. 22(1), pages 299-370, March.

    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:hal:journl:hal-01578092. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.