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Philippe du Jardin

Personal Details

First Name:Philippe
Middle Name:
Last Name:du Jardin
Suffix:
RePEc Short-ID:pdu253

Affiliation

Groupe EDHEC (École de Hautes Études Commerciales du Nord)

Lille/Paris, France
http://www.edhec.edu/
RePEc:edi:edhecfr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
  2. P. Du Jardin & E. Séverin, 2012. "Forecasting financial failure using a Kohonen map: a comparative study to improve bankruptcy model over time," Post-Print hal-00801853, HAL.
  3. du Jardin, Philippe & Severin, Eric, 2011. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," MPRA Paper 39935, University Library of Munich, Germany, revised 03 Apr 2012.
  4. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
  5. du Jardin, Philippe & Séverin, Eric, 2011. "Dividend policy," MPRA Paper 44382, University Library of Munich, Germany.
  6. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
  7. du Jardin, Philippe & Séverin, Eric, 2010. "Dynamic analysis of the business failure process: A study of bankruptcy trajectories," MPRA Paper 44379, University Library of Munich, Germany.
  8. du Jardin, Philippe, 2009. "Bankruptcy prediction models: How to choose the most relevant variables?," MPRA Paper 44380, University Library of Munich, Germany.
  9. du Jardin, Philippe, 2008. "Bankruptcy prediction and neural networks: The contribution of variable selection methods," MPRA Paper 44384, University Library of Munich, Germany.

Articles

  1. Philippe Jardin, 2021. "Forecasting bankruptcy using biclustering and neural network-based ensembles," Annals of Operations Research, Springer, vol. 299(1), pages 531-566, April.
  2. 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.
  3. Philippe Jardin & David Veganzones & Eric Séverin, 2019. "Forecasting Corporate Bankruptcy Using Accrual-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 7-43, June.
  4. du Jardin, Philippe, 2016. "A two-stage classification technique for bankruptcy prediction," European Journal of Operational Research, Elsevier, vol. 254(1), pages 236-252.
  5. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
  6. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.

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.

Working papers

  1. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.

    Cited by:

    1. Ilyes Abid & Rim Ayadi & Khaled Guesmi & Farid Mkaouar, 2022. "A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction," Annals of Operations Research, Springer, vol. 313(2), pages 605-623, June.
    2. Xavier Brédart & Eric Séverin & David Veganzones, 2021. "Human resources and corporate failure prediction modeling: Evidence from Belgium," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1325-1341, November.
    3. Yusuf Ali Al-Hroot, 2015. "The Influence Of Sample Size And Selection Of Financial Ratios In Bankruptcy Model Accuracy," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 13(1), pages 7-19, May.

  2. P. Du Jardin & E. Séverin, 2012. "Forecasting financial failure using a Kohonen map: a comparative study to improve bankruptcy model over time," Post-Print hal-00801853, HAL.

    Cited by:

    1. 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.
    2. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    3. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    4. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
    5. Kamesh Korangi & Christophe Mues & Cristi'an Bravo, 2021. "A transformer-based model for default prediction in mid-cap corporate markets," Papers 2111.09902, arXiv.org, revised Apr 2023.
    6. Tomasz Korol, 2018. "The Implementation of Fuzzy Logic in Forecasting Financial Ratios," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 12(2), June.
    7. 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.
    8. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    9. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    10. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    11. R. J. Kuo & Y. S. Tseng & Zhen-Yao Chen, 2016. "Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1191-1207, December.
    12. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 199-244, June.
    13. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    14. Sami Ben Jabeur & Youssef Fahmi, 2018. "Forecasting financial distress for French firms: a comparative study," Empirical Economics, Springer, vol. 54(3), pages 1173-1186, May.
    15. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    16. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
    17. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    18. Korangi, Kamesh & Mues, Christophe & Bravo, Cristián, 2023. "A transformer-based model for default prediction in mid-cap corporate markets," European Journal of Operational Research, Elsevier, vol. 308(1), pages 306-320.
    19. 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.
    20. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    21. Tomasz Korol, 2020. "Assessment of Trajectories of Non-bankrupt and Bankrupt Enterprises," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1113-1135.
    22. Man Ha & Christopher Gan & Cuong Nguyen & Patricia Anthony, 2021. "Self-Organising (Kohonen) Maps for the Vietnam Banking Industry," JRFM, MDPI, vol. 14(10), pages 1-18, October.
    23. Yehui Tong & Ramon Saladrigues, 2022. "An analysis of factors affecting the profits of new firms in Spain: Evidence from the food industry," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(1), pages 28-38.

  3. du Jardin, Philippe & Severin, Eric, 2011. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," MPRA Paper 39935, University Library of Munich, Germany, revised 03 Apr 2012.

    Cited by:

    1. 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.
    2. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    3. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    4. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
    5. Kamesh Korangi & Christophe Mues & Cristi'an Bravo, 2021. "A transformer-based model for default prediction in mid-cap corporate markets," Papers 2111.09902, arXiv.org, revised Apr 2023.
    6. Tomasz Korol, 2018. "The Implementation of Fuzzy Logic in Forecasting Financial Ratios," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 12(2), June.
    7. 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.
    8. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    9. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    10. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    11. R. J. Kuo & Y. S. Tseng & Zhen-Yao Chen, 2016. "Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1191-1207, December.
    12. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 199-244, June.
    13. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    14. Sami Ben Jabeur & Youssef Fahmi, 2018. "Forecasting financial distress for French firms: a comparative study," Empirical Economics, Springer, vol. 54(3), pages 1173-1186, May.
    15. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    16. Eva Kalinová, 2021. "Artificial Intelligence for Cluster Analysis: Case Study of Transport Companies in Czech Republic," JRFM, MDPI, vol. 14(9), pages 1-36, September.
    17. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
    18. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    19. Korangi, Kamesh & Mues, Christophe & Bravo, Cristián, 2023. "A transformer-based model for default prediction in mid-cap corporate markets," European Journal of Operational Research, Elsevier, vol. 308(1), pages 306-320.
    20. 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.
    21. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    22. Tomasz Korol, 2020. "Assessment of Trajectories of Non-bankrupt and Bankrupt Enterprises," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1113-1135.
    23. Man Ha & Christopher Gan & Cuong Nguyen & Patricia Anthony, 2021. "Self-Organising (Kohonen) Maps for the Vietnam Banking Industry," JRFM, MDPI, vol. 14(10), pages 1-18, October.
    24. Yehui Tong & Ramon Saladrigues, 2022. "An analysis of factors affecting the profits of new firms in Spain: Evidence from the food industry," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(1), pages 28-38.

  4. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.

    Cited by:

    1. Richard Chamboko & Jorge Miguel Bravo, 2020. "A Multi-State Approach to Modelling Intermediate Events and Multiple Mortgage Loan Outcomes," Risks, MDPI, vol. 8(2), pages 1-29, June.
    2. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    3. Abinzano, Isabel & Gonzalez-Urteaga, Ana & Muga, Luis & Sanchez, Santiago, 2020. "Performance of default-risk measures: the sample matters," Journal of Banking & Finance, Elsevier, vol. 120(C).
    4. Błażej Prusak, 2018. "Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries," IJFS, MDPI, vol. 6(3), pages 1-28, June.
    5. Marialuisa Restaino & Marco Bisogno, 2019. "A Business Failure Index Using Rank Transformation," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(1), pages 56-65, January.
    6. Miroslav Svatoš & Markéta Chovancová, 2013. "The influence of subsidies on the economic performance of Czech farms in the regions," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 61(4), pages 1137-1144.
    7. Ling‐Jing Kao & Chih‐Chou Chiu & Hung‐Jui Wang & Chang Yu Ko, 2021. "Prediction of remaining time on site for e‐commerce users: A SOM and long short‐term memory study," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1274-1290, November.
    8. Nurul Izzaty Hasanah Azhar & Norziana Lokman & Md. Mahmudul Alam & Jamaliah Said, 2021. "Factors determining Z-score and corporate failure in Malaysian companies," Post-Print hal-03520192, HAL.
    9. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    10. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    11. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    12. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto, 2011. "Partial Least Square Discriminant Analysis (PLS-DA) for bankruptcy prediction," Working Papers CEB 11-024, ULB -- Universite Libre de Bruxelles.
    13. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    14. Joon Hyung Cho & Jungpyo Lee & So Young Sohn, 2021. "Predicting future technological convergence patterns based on machine learning using link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5413-5429, July.
    15. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    16. Tomasz Korol, 2020. "Assessment of Trajectories of Non-bankrupt and Bankrupt Enterprises," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1113-1135.
    17. Laura Fabregat-Aibar & Maria-Teresa Sorrosal-Forradellas & Glòria Barberà-Mariné & Antonio Terceño, 2021. "Can Artificial Neural Networks Predict the Survival Capacity of Mutual Funds? Evidence from Spain," Mathematics, MDPI, vol. 9(6), pages 1-10, March.
    18. Man Ha & Christopher Gan & Cuong Nguyen & Patricia Anthony, 2021. "Self-Organising (Kohonen) Maps for the Vietnam Banking Industry," JRFM, MDPI, vol. 14(10), pages 1-18, October.

  5. du Jardin, Philippe & Séverin, Eric, 2011. "Dividend policy," MPRA Paper 44382, University Library of Munich, Germany.

    Cited by:

    1. Mohammad Mirbagherijam, 2014. "Asymmetric Effect of Inflation on Dividend Policy of Iran's Stocks Market," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 4(2), pages 337-350, February.
    2. Nicoleta BARBUTA-MISU, 2013. "Analysis of Dividend Policy of the Romanian Financial Investment Companies," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 23-32.
    3. Kartal Demirg ne, 2015. "Determinants of Target Dividend Payout Ratio: A Panel Autoregressive Distributed Lag Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 5(2), pages 418-426.
    4. Jeremiah Mugo Karagu & Bichanga Okibo, 2014. "Financial Factors Influencing Performance of Savings and Credit Co-Operative Organization in Kenya," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(2), pages 291-302, April.
    5. Lee, King Fuei, 2011. "Demographics and the Long-Horizon Returns of Dividend-Yield Strategies in the US," MPRA Paper 46350, University Library of Munich, Germany.
    6. Eliasu Nuhu & Abubakar Musah & Damankah Basil Senyo, 2014. "Determinants of Dividend Payout of Financial Firms and Non-Financial Firms in Ghana," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(3), pages 109-118, July.
    7. Lee, King Fuei, 2013. "Demographics and the long-horizon returns of dividend-yield strategies," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(2), pages 202-218.
    8. Boudry, Walter I. & Kallberg, Jarl G. & Liu, Crocker H., 2013. "Investment opportunities and share repurchases," Journal of Corporate Finance, Elsevier, vol. 23(C), pages 23-38.
    9. Jiatao Li & Carmen Ng, 2013. "The Normalization of Deviant Organizational Practices: The Non-performing Loans Problem in China," Journal of Business Ethics, Springer, vol. 114(4), pages 643-653, June.

  6. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.

    Cited by:

    1. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    2. Mogilat , Anastasia & Ipatova, Irina, 2016. "Technical efficiency as a factor of Russian industrial companies’ risks of financial distress," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 42, pages 05-29.
    3. Ali Asgary & Ali Sadeghi Naini, 2011. "Modelling The Adaptation Of Business Continuity Planning By Businesses Using Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 89-104, April.
    4. Ilyes Abid & Rim Ayadi & Khaled Guesmi & Farid Mkaouar, 2022. "A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction," Annals of Operations Research, Springer, vol. 313(2), pages 605-623, June.
    5. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Post-Print halshs-01281948, HAL.
    6. Luca Sensini, 2016. "An Empirical Analysis of Financially Distressed Italian Companies," International Business Research, Canadian Center of Science and Education, vol. 9(10), pages 75-85, October.
    7. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
    8. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
    9. Apostolos G. Christopoulos & Ioannis G. Dokas & Iraklis Kollias & John Leventides, 2019. "An implementation of Soft Set Theory in the Variables Selection Process for Corporate Failure Prediction Models. Evidence from NASDAQ Listed Firms," Bulletin of Applied Economics, Risk Market Journals, vol. 6(1), pages 1-20.
    10. Seyma Caliskan Cavdar & Alev Dilek Aydin, 2015. "An Empirical Analysis for the Prediction of a Financial Crisis in Turkey through the Use of Forecast Error Measures," JRFM, MDPI, vol. 8(3), pages 1-18, August.
    11. Angeliki Papana & Anastasia Spyridou, 2020. "Bankruptcy Prediction: The Case of the Greek Market," Forecasting, MDPI, vol. 2(4), pages 1-21, December.
    12. Piasecki Krzysztof & Wójcicka-Wójtowicz Aleksandra, 2017. "Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors," Folia Oeconomica Stetinensia, Sciendo, vol. 17(2), pages 129-143, December.
    13. James W. Kolari & Ivan Pastor Sanz, 2017. "Systemic risk measurement in banking using self-organizing maps," Journal of Banking Regulation, Palgrave Macmillan, vol. 18(4), pages 338-358, November.
    14. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01281948, HAL.
    15. Noora Alzayed & Rasol Eskandari & Hassan Yazdifar, 2023. "Bank failure prediction: corporate governance and financial indicators," Review of Quantitative Finance and Accounting, Springer, vol. 61(2), pages 601-631, August.
    16. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
    17. 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.
    18. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    19. Tamás Kristóf & Miklós Virág, 2020. "A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary," JRFM, MDPI, vol. 13(2), pages 1-20, February.
    20. Ching-Hsue Cheng & Ssu-Hsiang Wang, 2015. "A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress," Quantitative Finance, Taylor & Francis Journals, vol. 15(12), pages 1979-1994, December.
    21. Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179, arXiv.org.
    22. 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.
    23. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    24. 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.
    25. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    26. Santosh Kumar Shrivastav & P. Janaki Ramudu, 2020. "Bankruptcy Prediction and Stress Quantification Using Support Vector Machine: Evidence from Indian Banks," Risks, MDPI, vol. 8(2), pages 1-22, May.

  7. du Jardin, Philippe & Séverin, Eric, 2010. "Dynamic analysis of the business failure process: A study of bankruptcy trajectories," MPRA Paper 44379, University Library of Munich, Germany.

    Cited by:

    1. Błażej Prusak, 2018. "Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries," IJFS, MDPI, vol. 6(3), pages 1-28, June.
    2. Lebret, Rémi & Iovleff, Serge & Langrognet, Florent & Biernacki, Christophe & Celeux, Gilles & Govaert, Gérard, 2015. "Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i06).

  8. du Jardin, Philippe, 2009. "Bankruptcy prediction models: How to choose the most relevant variables?," MPRA Paper 44380, University Library of Munich, Germany.

    Cited by:

    1. Ptak-Chmielewska Aneta, 2021. "Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods," Statistics in Transition New Series, Statistics Poland, vol. 22(1), pages 179-195, March.
    2. Duarte Trigueiros, 2019. "Improving the effectiveness of predictors in accounting-based models," Journal of Applied Accounting Research, Emerald Group Publishing Limited, vol. 20(2), pages 207-226, June.
    3. Situm Mario, 2014. "Inability of Gearing-Ratio as Predictor for Early Warning Systems," Business Systems Research, Sciendo, vol. 5(2), pages 23-45, September.
    4. Simone Poli & Marco Gatti, 2024. "The relevance of cash flow information in predicting corporate bankruptcy in Italian private companies," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2024(1), pages 179-202.
    5. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    6. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    7. Šlefendorfas Gediminas, 2016. "Bankruptcy Prediction Model for Private Limited Companies of Lithuania," Ekonomika (Economics), Sciendo, vol. 95(1), pages 134-152, January.

Articles

  1. Philippe Jardin, 2021. "Forecasting bankruptcy using biclustering and neural network-based ensembles," Annals of Operations Research, Springer, vol. 299(1), pages 531-566, April.

    Cited by:

    1. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    2. Philippe Jardin, 2023. "Designing topological data to forecast bankruptcy using convolutional neural networks," Annals of Operations Research, Springer, vol. 325(2), pages 1291-1332, June.
    3. 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.
    4. 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.

  2. 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.

    Cited by:

    1. Philippe Jardin, 2023. "Designing topological data to forecast bankruptcy using convolutional neural networks," Annals of Operations Research, Springer, vol. 325(2), pages 1291-1332, June.
    2. Hajirahimi, Zahra & Khashei, Mehdi, 2022. "Series Hybridization of Parallel (SHOP) models for time series forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    3. Dai, Yeming & Yang, Xinyu & Leng, Mingming, 2022. "Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

  3. Philippe Jardin & David Veganzones & Eric Séverin, 2019. "Forecasting Corporate Bankruptcy Using Accrual-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 7-43, June.

    Cited by:

    1. Asyrofa Rahmi & Hung-Yuan Lu & Deron Liang & Dinda Novitasari & Chih-Fong Tsai, 2023. "Role of Comprehensive Income in Predicting Bankruptcy," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 689-720, August.
    2. Pavol Durana & Lucia Michalkova & Andrej Privara & Josef Marousek & Milos Tumpach, 2021. "Does the life cycle affect earnings management and bankruptcy?," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 425-461, June.
    3. Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023. "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, vol. 89(C).
    4. 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.
    5. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2022. "Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1231-1249, March.
    6. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.

  4. du Jardin, Philippe, 2016. "A two-stage classification technique for bankruptcy prediction," European Journal of Operational Research, Elsevier, vol. 254(1), pages 236-252.

    Cited by:

    1. 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.
    2. Xiaobo Tang & Shixuan Li & Mingliang Tan & Wenxuan Shi, 2020. "Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 769-787, August.
    3. Doumpos, Michalis & Andriosopoulos, Kostas & Galariotis, Emilios & Makridou, Georgia & Zopounidis, Constantin, 2017. "Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics," European Journal of Operational Research, Elsevier, vol. 262(1), pages 347-360.
    4. Mattia Pellegrino & Gianfranco Lombardo & George Adosoglou & Stefano Cagnoni & Panos M. Pardalos & Agostino Poggi, 2024. "A Multi-Head LSTM Architecture for Bankruptcy Prediction with Time Series Accounting Data," Future Internet, MDPI, vol. 16(3), pages 1-20, February.
    5. Silvia Angilella & Maria Rosaria Pappalardo, 2021. "Assessment of a failure prediction model in the energy sector: a multicriteria discrimination approach with Promethee based classification," Papers 2102.07656, arXiv.org.
    6. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
    7. Sami Ben Jabeur & Rabi Belhaj Hassine & Salma Mefteh‐Wali, 2021. "Firm financial performance during the financial crisis: A French case study," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2800-2812, April.
    8. Ünal, Cemre & Ceasu, Ioana, 2019. "A Machine Learning Approach Towards Startup Success Prediction," IRTG 1792 Discussion Papers 2019-022, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    9. Liang, Deron & Tsai, Chih-Fong & Lu, Hung-Yuan (Richard) & Chang, Li-Shin, 2020. "Combining corporate governance indicators with stacking ensembles for financial distress prediction," Journal of Business Research, Elsevier, vol. 120(C), pages 137-146.
    10. Thomson, Mary E. & Pollock, Andrew C. & Önkal, Dilek & Gönül, M. Sinan, 2019. "Combining forecasts: Performance and coherence," International Journal of Forecasting, Elsevier, vol. 35(2), pages 474-484.
    11. Philippe Jardin, 2023. "Designing topological data to forecast bankruptcy using convolutional neural networks," Annals of Operations Research, Springer, vol. 325(2), pages 1291-1332, June.
    12. Carmona, Pedro & Dwekat, Aladdin & Mardawi, Zeena, 2022. "No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure," Research in International Business and Finance, Elsevier, vol. 61(C).
    13. Noora Alzayed & Rasol Eskandari & Hassan Yazdifar, 2023. "Bank failure prediction: corporate governance and financial indicators," Review of Quantitative Finance and Accounting, Springer, vol. 61(2), pages 601-631, August.
    14. 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.
    15. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    16. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    17. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    18. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    19. Canto, José Augusto & Silva, Amélia Cristina Ferreira & Leite, Gabriela & Machado-Santos, Carlos, 2019. "Insolvency prediction for Portuguese agro-industrial SME: Tree Bagging Methodology," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 0(Issue 2).
    20. Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).
    21. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 199-244, June.
    22. Gianfranco Lombardo & Mattia Pellegrino & George Adosoglou & Stefano Cagnoni & Panos M. Pardalos & Agostino Poggi, 2022. "Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks," Future Internet, MDPI, vol. 14(8), pages 1-23, August.
    23. Bai, Chunguang & Shi, Baofeng & Liu, Feng & Sarkis, Joseph, 2019. "Banking credit worthiness: Evaluating the complex relationships," Omega, Elsevier, vol. 83(C), pages 26-38.
    24. Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    25. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    26. Philippe Jardin & David Veganzones & Eric Séverin, 2019. "Forecasting Corporate Bankruptcy Using Accrual-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 7-43, June.
    27. Ronald Richman & Mario V. Wüthrich, 2020. "Nagging Predictors," Risks, MDPI, vol. 8(3), pages 1-26, August.
    28. Suss, Joel & Treitel, Henry, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.
    29. Yehui Tong & Ramon Saladrigues, 2022. "An analysis of factors affecting the profits of new firms in Spain: Evidence from the food industry," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(1), pages 28-38.

  5. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.

    Cited by:

    1. Lukason, Oliver & Laitinen, Erkki K., 2019. "Firm failure processes and components of failure risk: An analysis of European bankrupt firms," Journal of Business Research, Elsevier, vol. 98(C), pages 380-390.
    2. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    3. Fedorova, Elena & Ledyaeva, Svetlana & Drogovoz, Pavel & Nevredinov, Alexandr, 2022. "Economic policy uncertainty and bankruptcy filings," International Review of Financial Analysis, Elsevier, vol. 82(C).
    4. Xavier Brédart & Eric Séverin & David Veganzones, 2021. "Human resources and corporate failure prediction modeling: Evidence from Belgium," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1325-1341, November.
    5. Ben Jabeur, Sami, 2017. "Bankruptcy prediction using Partial Least Squares Logistic Regression," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 197-202.
    6. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    7. Nora Muñoz-Izquierdo & María-del-Mar Camacho-Miñano & María-Jesús Segovia-Vargas & David Pascual-Ezama, 2019. "Is the External Audit Report Useful for Bankruptcy Prediction? Evidence Using Artificial Intelligence," IJFS, MDPI, vol. 7(2), pages 1-23, April.
    8. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
    9. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
    10. Angeliki Papana & Anastasia Spyridou, 2020. "Bankruptcy Prediction: The Case of the Greek Market," Forecasting, MDPI, vol. 2(4), pages 1-21, December.
    11. Abinzano, Isabel & Gonzalez-Urteaga, Ana & Muga, Luis & Sanchez, Santiago, 2020. "Performance of default-risk measures: the sample matters," Journal of Banking & Finance, Elsevier, vol. 120(C).
    12. Kamesh Korangi & Christophe Mues & Cristi'an Bravo, 2021. "A transformer-based model for default prediction in mid-cap corporate markets," Papers 2111.09902, arXiv.org, revised Apr 2023.
    13. Theodore Metaxas & Athanasios Romanopoulos, 2023. "A Literature Review on the Financial Determinants of Hotel Default," JRFM, MDPI, vol. 16(7), pages 1-19, July.
    14. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    15. Jarmila Horváthová & Martina Mokrišová & Martin Bača, 2023. "Bankruptcy Prediction for Sustainability of Businesses: The Application of Graph Theoretical Modeling," Mathematics, MDPI, vol. 11(24), pages 1-20, December.
    16. 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.
    17. Korol Tomasz, 2017. "Evaluation of the factors influencing business bankruptcy risk in Poland," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 13(2), pages 22-35, December.
    18. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    19. 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.
    20. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    21. Oliver Lukason & María-del-Mar Camacho-Miñano, 2019. "Bankruptcy Risk, Its Financial Determinants and Reporting Delays: Do Managers Have Anything to Hide?," Risks, MDPI, vol. 7(3), pages 1-15, July.
    22. Manuel D. N. T. Oliveira & Fernando A. F. Ferreira & Guillermo O. Pérez-Bustamante Ilander & Marjan S. Jalali, 2017. "Integrating cognitive mapping and MCDA for bankruptcy prediction in small- and medium-sized enterprises," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 985-997, September.
    23. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    24. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 199-244, June.
    25. Surbhi Bhatia & Manish K. Singh, 2022. "Fifty years since Altman (1968): Performance of financial distress prediction models," Working Papers 12, xKDR.
    26. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    27. Sami Ben Jabeur & Youssef Fahmi, 2018. "Forecasting financial distress for French firms: a comparative study," Empirical Economics, Springer, vol. 54(3), pages 1173-1186, May.
    28. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
    29. Tanzina Haque, 2023. "“Impact of Bankruptcy Risk on Reporting Aelay: An Empirical Evidence from Engineering Industry in Bangladesh.â€," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(8), pages 1538-1552, August.
    30. 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.
    31. Korangi, Kamesh & Mues, Christophe & Bravo, Cristián, 2023. "A transformer-based model for default prediction in mid-cap corporate markets," European Journal of Operational Research, Elsevier, vol. 308(1), pages 306-320.
    32. Philippe Jardin & David Veganzones & Eric Séverin, 2019. "Forecasting Corporate Bankruptcy Using Accrual-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 7-43, June.
    33. Yi Cao & Xiaoquan Liu & Jia Zhai & Shan Hua, 2022. "A two‐stage Bayesian network model for corporate bankruptcy prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 455-472, January.
    34. Oliver Lukason & Art Andresson, 2019. "Tax Arrears Versus Financial Ratios in Bankruptcy Prediction," JRFM, MDPI, vol. 12(4), pages 1-13, December.
    35. Philippe Jardin, 2021. "Forecasting bankruptcy using biclustering and neural network-based ensembles," Annals of Operations Research, Springer, vol. 299(1), pages 531-566, April.
    36. Spyridou, Anastasia, 2019. "Evaluating Factors of Small and Medium Hospitality Enterprises Business Failure: a conceptual approach," MPRA Paper 93997, University Library of Munich, Germany.
    37. Martina Mokrišová & Jarmila Horváthová, 2023. "Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach," Risks, MDPI, vol. 11(11), pages 1-18, November.

  6. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.
    See citations under working paper version above.

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