Authors:
Miklós Virág Department of Corporate Finance, School of Business Administration, Corvinus University of Budapest Fővám tér 8, E-350, H-1093 Budapest, Hungary

Search for other papers by Miklós Virág in
Current site
Google Scholar
PubMed
Close
and
Tamás Kristóf Futures Studies Department, Corvinus University of Budapest. Budapest, Hungary

Search for other papers by Tamás Kristóf in
Current site
Google Scholar
PubMed
Close
Restricted access

The article attempts to answer the question whether or not the latest bankruptcy prediction techniques are more reliable than traditional mathematical-statistical ones in Hungary. Simulation experiments carried out on the database of the first Hungarian bankruptcy prediction model clearly prove that bankruptcy models built using artificial neural networks have higher classification accuracy than models created in the 1990s based on discriminant analysis and logistic regression analysis. The article presents the main results, analyses the reasons for the differences and presents constructive proposals concerning the further development of Hungarian bankruptcy prediction.

  • Collapse
  • Expand

Senior editors

Editors-in-Chief: István P. Székely, Dóra Győrffy

Editor(s): Judit Kálmán

Associate Editors

  • Péter Benczúr, Joint Joint Research Center, European Commission
  • Dóra Benedek, International Monetary Fund
  • Balázs Égert, OECD
  • Dániel Prinz, World Bank
  • Rok Spruk, University of Ljubljana, School of Economics and Business, Slovenia

Editorial Board

  • Anders Åslund, Georgetown University and Advisory Council of CASE, USA
  • István Benczes, Corvinus University of Budapest, Hungary 
  • Agnieszka Chłoń-Domińczak, SGH Warsaw School of Economics, Poland
  • Fabrizio Coricelli, University of Siena, Italy
  • László Csaba, Corvinus University of Budapest, Hungary and Central European University, Austria
  • Beáta Farkas, Faculty of Economics and Business Administration, University of Szeged, Hungary
  • Péter Halmai, Budapest University of Technology and Economics, and National University of Public Service, Hungary
  • Martin Kahanec, Central European University, Austria
  • Michael Landesmann, The Vienna Institute for International Economic Studies (WIIW), Austria
  • Péter Mihályi, Corvinus University of Budapest, Hungary
  • Debora Revoltella, European Investment Bank

Corvinus University of Budapest
Department of Economics
Fővám tér 8 Budapest, H-1093, Hungary
E-mail: judit.kalman@uni-corvinus.hu

Indexing and Abstracting Services:

  • CABELLS Journalytics
  • EconLit
  • Elsevier GEO Abstracts
  • GEOBASE
  • Index Copernicus
  • International Bibliographies IBZ and IBR
  • JEL
  • Referativnyi Zhurnal
  • RePEc
  • SCOPUS
  • Social Science Citation Index

 

2023  
Web of Science  
Journal Impact Factor 0.7
Rank by Impact Factor Q3 (Economics)
Journal Citation Indicator 0.23
Scopus  
CiteScore 1.4
CiteScore rank Q3 (Economics and Econometrics)
SNIP 0.385
Scimago  
SJR index 0.218
SJR Q rank Q4

Acta Oeconomica
Publication Model Hybrid
Submission Fee none
Article Processing Charge 900 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2025 Online subsscription: 700 EUR / 768 USD
Print + online subscription: 820 EUR / 900 USD
Subscription Information Online subscribers are entitled access to all back issues published by Akadémiai Kiadó for each title for the duration of the subscription, as well as Online First content for the subscribed content.
Purchase per Title Individual articles are sold on the displayed price.

Acta Oeconomica
Language English
Size B5
Year of
Foundation
1966
Volumes
per Year
1
Issues
per Year
4
Founder Magyar Tudományos Akadémia
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 0001-6373 (Print)
ISSN 1588-2659 (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Aug 2024 36 0 0
Sep 2024 15 1 2
Oct 2024 72 0 0
Nov 2024 52 0 0
Dec 2024 39 0 0
Jan 2025 104 0 0
Feb 2025 33 0 0