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Miklós Virág

Personal Details

First Name:Miklos
Middle Name:
Last Name:Virag
Suffix:
RePEc Short-ID:pvi529
[This author has chosen not to make the email address public]
Terminal Degree: Corvinus Institute for Advanced Studies; Budapesti Corvinus Egyetem (from RePEc Genealogy)

Affiliation

Vállalkozás és Innováció Intézet
Budapesti Corvinus Egyetem

Budapest, Hungary
https://www.uni-corvinus.hu/fooldal/egyetemunkrol/intezetek/vallalkozas-es-innovacio-intezet/
RePEc:edi:vicorhu (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. Tamás Kristóf & Attila Virág & Miklós Virág, 2024. "Sectoral Performance Trends and Differences in the Balkan and Eastern European Region," Economies, MDPI, vol. 12(4), pages 1-16, April.
  2. Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
  3. Tamás Kristóf & Miklós Virág, 2020. "A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary," JRFM, MDPI, vol. 13(2), pages 1-20, February.
  4. Nyitrai, Tamás & Virág, Miklós, 2019. "The effects of handling outliers on the performance of bankruptcy prediction models," Socio-Economic Planning Sciences, Elsevier, vol. 67(C), pages 34-42.
  5. 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.
  6. Miklós Virág & Tamás Nyitrai, 2014. "Is there a trade-off between the predictive power and the interpretability of bankruptcy models? The case of the first Hungarian bankruptcy prediction model," Acta Oeconomica, Akadémiai Kiadó, Hungary, vol. 64(4), pages 419-440, December.
  7. Miklós Virág & Tamás Nyitrai, 2014. "The application of ensemble methods in forecasting bankruptcy," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 13(4), pages 178-193.
  8. Miklós Virag & Tamás Nyitrai, 2013. "Application of support vector machines on the basis of the first Hungarian bankruptcy model," Society and Economy, Akadémiai Kiadó, Hungary, vol. 35(2), pages 227-248, August.
  9. Tamás Kristóf & Miklós Virág, 2012. "Data reduction and univariate splitting — Do they together provide better corporate bankruptcy prediction?," Acta Oeconomica, Akadémiai Kiadó, Hungary, vol. 62(2), pages 205-228, June.
  10. 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.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Tamás Kristóf & Miklós Virág, 2020. "A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary," JRFM, MDPI, vol. 13(2), pages 1-20, February.

    Cited by:

    1. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.

  2. Nyitrai, Tamás & Virág, Miklós, 2019. "The effects of handling outliers on the performance of bankruptcy prediction models," Socio-Economic Planning Sciences, Elsevier, vol. 67(C), pages 34-42.

    Cited by:

    1. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.
    2. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    3. Ghafariasl, Parviz & Mahmoudan, Alireza & Mohammadi, Mahmoud & Nazarparvar, Aria & Hoseinzadeh, Siamak & Fathali, Mani & Chang, Shing & Zeinalnezhad, Masoomeh & Garcia, Davide Astiaso, 2024. "Neural network-based surrogate modeling and optimization of a multigeneration system," Applied Energy, Elsevier, vol. 364(C).
    4. Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    5. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    6. Fedorova, Elena & Ledyaeva, Svetlana & Drogovoz, Pavel & Nevredinov, Alexandr, 2022. "Economic policy uncertainty and bankruptcy filings," International Review of Financial Analysis, Elsevier, vol. 82(C).
    7. Kristóf, Tamás & Márton, András & Fiáth, Attila, 2023. "Állami energiavállalatok pénzügyi teljesítménye Magyarországon a koronavírus-járvány előtt és alatt [Financial performance of publicly owned energy companies in Hungary before and during the COVID ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(10), pages 1057-1076.
    8. Dong, Weijia & Dong, Xinyang & Lv, Xin, 2022. "How does ownership structure affect corporate environmental responsibility? Evidence from the manufacturing sector in China," Energy Economics, Elsevier, vol. 112(C).
    9. Lidiya Guryanova & Olena Bolotova & Vitalii Gvozdytskyi & Sergienko Olena, 2020. "Long-term financial sustainability: An evaluation methodology with threats considerations," RIVISTA DI STUDI SULLA SOSTENIBILITA', FrancoAngeli Editore, vol. 0(1), pages 47-69.
    10. Mário S. Céu & Raquel M. Gaspar, 2023. "Financial Distress in European Vineyards and Olive Groves," Working Papers REM 2023/0266, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    11. Vadlamani Ravi & Vadlamani Madhav, 2020. "Optimizing the reliability of a bank with Logistic Regression and Particle Swarm Optimization," Papers 2004.11122, arXiv.org.

  3. Miklós Virág & Tamás Nyitrai, 2014. "Is there a trade-off between the predictive power and the interpretability of bankruptcy models? The case of the first Hungarian bankruptcy prediction model," Acta Oeconomica, Akadémiai Kiadó, Hungary, vol. 64(4), pages 419-440, December.

    Cited by:

    1. Sylvia Jenčová & Róbert Štefko & Petra Vašaničová, 2020. "Scoring Model of the Financial Health of the Electrical Engineering Industry’s Non-Financial Corporations," Energies, MDPI, vol. 13(17), pages 1-17, August.
    2. Misankova Maria & Zvarikova Katarina & Kliestikova Jana, 2017. "Bankruptcy Practice in Countries of Visegrad Four," Economics and Culture, Sciendo, vol. 14(1), pages 108-118, June.
    3. Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).

  4. Miklós Virág & Tamás Nyitrai, 2014. "The application of ensemble methods in forecasting bankruptcy," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 13(4), pages 178-193.

    Cited by:

    1. Mohammad Shamsu Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib & Kunpeng Yuan, 2022. "Modeling credit risk with a multi‐stage hybrid model: An alternative statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1386-1415, November.
    2. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.

  5. Miklós Virag & Tamás Nyitrai, 2013. "Application of support vector machines on the basis of the first Hungarian bankruptcy model," Society and Economy, Akadémiai Kiadó, Hungary, vol. 35(2), pages 227-248, August.

    Cited by:

    1. Tamás Kristóf & Miklós Virág, 2020. "A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary," JRFM, MDPI, vol. 13(2), pages 1-20, February.
    2. 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.
    3. Botond Benedek & Balint Zsolt Nagy, 2023. "Traditional versus AI-Based Fraud Detection: Cost Efficiency in the Field of Automobile Insurance," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 22(2), pages 77-98.
    4. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.

  6. Tamás Kristóf & Miklós Virág, 2012. "Data reduction and univariate splitting — Do they together provide better corporate bankruptcy prediction?," Acta Oeconomica, Akadémiai Kiadó, Hungary, vol. 62(2), pages 205-228, June.

    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.

  7. 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.

    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.

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