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Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review

Citations

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Cited by:

  1. Marco Bisogno, 2012. "The Accessibility Of The Italian Bankruptcy Procedures: An Empirical Analysis," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 2(2), pages 1-24, December.
  2. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
  3. Jim Sánchez & Diego Restrepo & Andres Ramírez, 2018. "Inefficiency and Bank Failures: A Joint Bayesian Esti-mationof a Stochastic Frontier Model and a Hazards Model," Documentos de Trabajo de Valor Público 16977, Universidad EAFIT.
  4. Wauters, Mathieu & Vanhoucke, Mario, 2017. "A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1097-1111.
  5. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
  6. Anna Stelzer, 2019. "Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions," Papers 1907.12996, arXiv.org.
  7. 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.
  8. Mattia Iotti & Giuseppe Bonazzi, 2018. "Analysis of the Risk of Bankruptcy of Tomato Processing Companies Operating in the Inter-Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”," Sustainability, MDPI, vol. 10(4), pages 1-23, March.
  9. Wikil Kwak & Yong Shi & Gang Kou, 2012. "Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach," Review of Quantitative Finance and Accounting, Springer, vol. 38(4), pages 441-453, May.
  10. van der Heijden, Hans, 2022. "Predicting industry sectors from financial statements: An illustration of machine learning in accounting research," The British Accounting Review, Elsevier, vol. 54(5).
  11. Buckmann, Marcus & Gallego Marquez, Paula & Gimpelewicz, Mariana & Kapadia, Sujit & Rismanchi, Katie, 2023. "The more the merrier? Evidence on the value of multiple requirements in bank regulation," Journal of Banking & Finance, Elsevier, vol. 149(C).
  12. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
  13. Zoričák, Martin & Gnip, Peter & Drotár, Peter & Gazda, Vladimír, 2020. "Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets," Economic Modelling, Elsevier, vol. 84(C), pages 165-176.
  14. Jaizah Othman & Mehmet Asutay, 2018. "Integrated early warning prediction model for Islamic banks: the Malaysian case," Journal of Banking Regulation, Palgrave Macmillan, vol. 19(2), pages 118-130, April.
  15. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
  16. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
  17. Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, C.E.P.R. Discussion Papers.
  18. 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.
  19. Isik, Ihsan & Uygur, Ozge, 2021. "Financial crises, bank efficiency and survival: Theory, literature and emerging market evidence," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 952-987.
  20. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
  21. Boratyńska, Katarzyna & Grzegorzewska, Emilia, 2018. "Bankruptcy prediction in the agribusiness sector: Lessons from quantitative and qualitative approaches," Journal of Business Research, Elsevier, vol. 89(C), pages 175-181.
  22. Silvana Secinaro & Valerio Brescia & Davide Calandra & Buerhan Saiti, 2020. "Impact of climate change mitigation policies on corporate financial performance: Evidence‐based on European publicly listed firms," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 27(6), pages 2491-2501, November.
  23. Agata Gniadkowska-Szymańska, 2022. "The liquidity of shares and the risk of bankruptcy," Bank i Kredyt, Narodowy Bank Polski, vol. 53(6), pages 565-586.
  24. Michael Filletti & Aaron Grech, 2020. "Using News Articles and Financial Data to predict the likelihood of bankruptcy," Papers 2003.13414, arXiv.org.
  25. 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.
  26. Meng-Meng Tan & Dong-Ling Xu & Jian-Bo Yang, 2022. "Corporate Failure Risk Assessment for Knowledge-Intensive Services Using the Evidential Reasoning Approach," JRFM, MDPI, vol. 15(3), pages 1-29, March.
  27. Baumöhl, Eduard & Iwasaki, Ichiro & Kočenda, Evžen, 2019. "Institutions and determinants of firm survival in European emerging markets," Journal of Corporate Finance, Elsevier, vol. 58(C), pages 431-453.
  28. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
  29. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "Methodological comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the perspective of bankruptcy assessment," European Journal of Operational Research, Elsevier, vol. 199(2), pages 561-575, December.
  30. Veres Ferrer, Ernesto Jesús & Labatut Serer, Gregorio & Pozuelo Campillo, Jose, 2009. "Hacia una ordenación de las pequeñas empresas atendiendo a su posible situación de fracaso/Towards a Ranking of Smaller Companies According to Their Failure Risk," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 27, pages 775(18á)-77, Diciembre.
  31. Davaasukh Damdinjav & Dulamzaya Batjargal & Ninjin Batmunkh, 2024. "Distress Prediction and Stress Testing of Nonfinancial Firms: Case of Mongolia," IHEID Working Papers 16-2024, Economics Section, The Graduate Institute of International Studies.
  32. Douglas, Ella & Lont, David & Scott, Tom, 2014. "Finance company failure in New Zealand during 2006–2009: Predictable failures?," Journal of Contemporary Accounting and Economics, Elsevier, vol. 10(3), pages 277-295.
  33. Ali Taghi-Molla & Masoud Rabbani & Mohammad Hosein Karimi Gavareshki & Ehsan Dehghani, 2020. "Safety improvement in a gas refinery based on resilience engineering and macro-ergonomics indicators: a Bayesian network–artificial neural network approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 641-654, June.
  34. Mare, Davide Salvatore & Moreira, Fernando & Rossi, Roberto, 2017. "Nonstationary Z-Score measures," European Journal of Operational Research, Elsevier, vol. 260(1), pages 348-358.
  35. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
  36. 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.
  37. repec:ctc:sdimse:dime19_03 is not listed on IDEAS
  38. Yan Dong & Moonwon Chung & Chen Zhou & Sriram Venkataraman, 2019. "Banking on “Mobile Money”: The Implications of Mobile Money Services on the Value Chain," Manufacturing & Service Operations Management, INFORMS, vol. 21(2), pages 290-307, May.
  39. Colvin, Christopher L. & de Jong, Abe & Fliers, Philip T., 2015. "Predicting the past: Understanding the causes of bank distress in the Netherlands in the 1920s," Explorations in Economic History, Elsevier, vol. 55(C), pages 97-121.
  40. Lucia Svabova & Lucia Michalkova & Marek Durica & Elvira Nica, 2020. "Business Failure Prediction for Slovak Small and Medium-Sized Companies," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
  41. Giudici, Paolo & Gramegna, Alex & Raffinetti, Emanuela, 2023. "Machine Learning Classification Model Comparison," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
  42. Andrea C. Hupman, 2022. "Cutoff Threshold Decisions for Classification Algorithms with Risk Aversion," Decision Analysis, INFORMS, vol. 19(1), pages 63-78, March.
  43. Doering, Jana & Kizys, Renatas & Juan, Angel A. & Fitó, Àngels & Polat, Onur, 2019. "Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends," Operations Research Perspectives, Elsevier, vol. 6(C).
  44. 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.
  45. Pham, Xuan T.T. & Ho, Tin H., 2021. "Using boosting algorithms to predict bank failure: An untold story," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 40-54.
  46. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
  47. Lin, Fengyi & Yeh, Ching Chiang & Lee, Meng Yuan, 2013. "A Hybrid Business Failure Prediction Model Using Locally Linear Embedding And Support Vector Machines," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 82-97, March.
  48. Oliver Lukason & Art Andresson, 2019. "Tax Arrears Versus Financial Ratios in Bankruptcy Prediction," JRFM, MDPI, vol. 12(4), pages 1-13, December.
  49. Taghizadeh-Hesary, Farhad & Phoumin, Han & Rasoulinezhad, Ehsan, 2022. "COVID-19 and regional solutions for mitigating the risk of SME finance in selected ASEAN member states," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 506-525.
  50. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
  51. Buchholst, Birgitte Vølund & Rangvid, Jesper, 2013. "Leading indicators of distress in Danish banks in the period 2008-12," Nationaløkonomisk tidsskrift, Nationaløkonomisk Forening, vol. 2013(2), pages 176-206.
  52. Dmitriy Borzykh & Henry Penikas, 2021. "IRB PD model accuracy validation in the presence of default correlation: a twin confidence interval approach," Risk Management, Palgrave Macmillan, vol. 23(4), pages 282-300, December.
  53. Marianna Succurro, 2017. "Financial Bankruptcy across European Countries," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(7), pages 132-146, July.
  54. 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.
  55. Huei-Wen Teng & Michael Lee, 2019. "Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-27, September.
  56. 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.
  57. Cheng Few Lee, 2020. "Financial econometrics, mathematics, statistics, and financial technology: an overall view," Review of Quantitative Finance and Accounting, Springer, vol. 54(4), pages 1529-1578, May.
  58. Xinlin Wang & Zs'ofia Kraussl & Mats Brorsson, 2024. "Datasets for Advanced Bankruptcy Prediction: A survey and Taxonomy," Papers 2411.01928, arXiv.org.
  59. Ana GARCÍA-GALLEGO & María-Jesús MURES-QUINTANA, 2016. "Principal Components And Canonical Correlation Analyses As Complementary Tools. Application To The Processing Of Financial Information," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 249-266.
  60. Romero Martínez, Mariano & Carmona Ibáñez, Pedro & Pozuelo Campillo, José, 2021. "Utilidad del Deep Learning en la predicción del fracaso empresarial en el ámbito europeo || The usefulness of Deep Learning in the prediction of business failure at the European level," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 32(1), pages 392-414, December.
  61. Naoyuki Yoshino & Farhad Taghizadeh-Hesary, 2019. "The Role of SMEs in Asia and Their Difficulties in Accessing Finance," Working Papers id:12959, eSocialSciences.
  62. Kolari, James W. & López-Iturriaga, Félix J. & Sanz, Ivan Pastor, 2019. "Predicting European bank stress tests: Survival of the fittest," Global Finance Journal, Elsevier, vol. 39(C), pages 44-57.
  63. Li, Hui & Sun, Jie, 2009. "Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II," European Journal of Operational Research, Elsevier, vol. 197(1), pages 214-224, August.
  64. Rahimikia, Eghbal & Mohammadi, Shapour & Rahmani, Teymur & Ghazanfari, Mehdi, 2017. "Detecting corporate tax evasion using a hybrid intelligent system: A case study of Iran," International Journal of Accounting Information Systems, Elsevier, vol. 25(C), pages 1-17.
  65. 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.
  66. 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.
  67. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Post-Print halshs-01281948, HAL.
  68. Khaled Halteh & Kuldeep Kumar & Adrian Gepp, 2018. "Using Cutting-Edge Tree-Based Stochastic Models to Predict Credit Risk," Risks, MDPI, vol. 6(2), pages 1-13, May.
  69. 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.
  70. Shaddady, Ali & Moore, Tomoe, 2019. "Investigation of the effects of financial regulation and supervision on bank stability: The application of CAMELS-DEA to quantile regressions," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 58(C), pages 96-116.
  71. Amin Jan & Maran Marimuthu, 2016. "Bankruptcy Profile of Foreign versus Domestic Islamic Banks of Malaysia: A Post Crisis Period Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 332-346.
  72. Huang, Chao & Dai, Chong & Guo, Miao, 2015. "A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 431-441.
  73. Fatima Alali & Silvia Romero, 2013. "Characteristics of failed U.S. commercial banks: an exploratory study," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(4), pages 1149-1174, December.
  74. Sebastian Klaudiusz Tomczak & Piotr Staszkiewicz, 2020. "Cross-Country Application of Manufacturing Failure Models," JRFM, MDPI, vol. 13(2), pages 1-10, February.
  75. You-Shyang Chen & Ying-Hsun Hung & Yu-Sheng Lin, 2023. "A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation," Mathematics, MDPI, vol. 11(13), pages 1-34, July.
  76. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
  77. 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.
  78. 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.
  79. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
  80. Baumöhl, Eduard & Iwasaki, Ichiro & Kočenda, Evžen, 2020. "Firm survival in new EU member states," Economic Systems, Elsevier, vol. 44(1).
  81. 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.
  82. Pompella, Maurizio & Dicanio, Antonio, 2017. "Ratings based Inference and Credit Risk: Detecting likely-to-fail Banks with the PC-Mahalanobis Method," Economic Modelling, Elsevier, vol. 67(C), pages 34-44.
  83. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
  84. Yoshino, Naoyuki & Taghizadeh-Hesary, Farhad, 2019. "Optimal credit guarantee ratio for small and medium-sized enterprises’ financing: Evidence from Asia," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 342-356.
  85. Carlos Serrano-Cinca & Yolanda Fuertes-Call鮠 & Bego uti鲲ez-Nieto & Beatriz Cuellar-Fernᮤez, 2014. "Path modelling to bankruptcy: causes and symptoms of the banking crisis," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3798-3811, November.
  86. Salvador Marín Hernández & Ester Gras Gil & Marcos Antón Renart, 2011. "Financial information and restructuring of spanish savings banks in a context of crisis. Changes in the regulation; content and evolution of FROB," CIRIEC-España, revista de economía pública, social y cooperativa, CIRIEC-España, issue 73, pages 99-126, October.
  87. Greta Falavigna, 2011. "An artificial neural network approach for assigning rating judgements to Italian Small Firms," CERIS Working Paper 201104, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
  88. Khoja, Layla & Chipulu, Maxwell & Jayasekera, Ranadeva, 2019. "Analysis of financial distress cross countries: Using macroeconomic, industrial indicators and accounting data," International Review of Financial Analysis, Elsevier, vol. 66(C).
  89. Mohamed El Ghourabi & Amira Dridi & Mohamed Limam, 2015. "A new financial stress index model based on support vector regression and control chart," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 775-788, April.
  90. Salehi Mahdi & Rostami Neda, 2013. "Bankruptcy Prediction By Using Support Vector Machines And Genetic Algorithms," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 8(1), pages 104-114, April.
  91. repec:ctc:sdimse:dime21_01 is not listed on IDEAS
  92. 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.
  93. Mousavi, Mohammad M. & Ouenniche, Jamal & Xu, Bing, 2015. "Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 64-75.
  94. 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.
  95. 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.
  96. Logan Ewanchuk & Christoph Frei, 2019. "Recent Regulation in Credit Risk Management: A Statistical Framework," Risks, MDPI, vol. 7(2), pages 1-19, April.
  97. Yoshino, Naoyuki & Taghizadeh-Hesary, Farhad, 2016. "Optimal Credit Guarantee Ratio for Asia," ADBI Working Papers 586, Asian Development Bank Institute.
  98. Tyler Pike & Horacio Sapriza & Tom Zimmermann, 2019. "Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning," Finance and Economics Discussion Series 2019-070, Board of Governors of the Federal Reserve System (U.S.).
  99. Wanke, Peter & Barros, Carlos P. & Faria, João R., 2015. "Financial distress drivers in Brazilian banks: A dynamic slacks approach," European Journal of Operational Research, Elsevier, vol. 240(1), pages 258-268.
  100. repec:erf:erfstu:78 is not listed on IDEAS
  101. Papanikolaou, Nikolaos I., 2018. "To be bailed out or to be left to fail? A dynamic competing risks hazard analysis," Journal of Financial Stability, Elsevier, vol. 34(C), pages 61-85.
  102. Amendola, Alessandra & Restaino, Marialuisa & Sensini, Luca, 2015. "An analysis of the determinants of financial distress in Italy: A competing risks approach," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 33-41.
  103. Ece Acar & Görkem Sarıyer & Vipul Jain & Bharti Ramtiyal, 2023. "Discovering Hidden Associations among Environmental Disclosure Themes Using Data Mining Approaches," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
  104. Javid Iqbal, 2019. "Managerial Self-Attribution Bias and Banks’ Future Performance: Evidence from Emerging Economies," JRFM, MDPI, vol. 12(2), pages 1-32, April.
  105. Angilella, Silvia & Mazzù, Sebastiano, 2015. "The financing of innovative SMEs: A multicriteria credit rating model," European Journal of Operational Research, Elsevier, vol. 244(2), pages 540-554.
  106. Õie Renata Siimon & Oliver Lukason, 2021. "A Decision Support System for Corporate Tax Arrears Prediction," Sustainability, MDPI, vol. 13(15), pages 1-23, July.
  107. Tomasz Pisula, 2020. "An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship," JRFM, MDPI, vol. 13(2), pages 1-35, February.
  108. Yoshino, Naoyuki & Taghizadeh-Hesary, Farhad & Nili, Farhad, 2015. "Estimating Dual Deposit Insurance Premium Rates and Forecasting Non-performing Loans: Two New Models," ADBI Working Papers 510, Asian Development Bank Institute.
  109. Yin Shi & Xiaoni Li, 2021. "Determinants of financial distress in the European air transport industry: The moderating effect of being a flag-carrier," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-17, November.
  110. Jun Wang & Mao Li & Martin Skitmore & Jianli Chen, 2024. "Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
  111. Hyunjung Nam & Won Gyun No & Youngsu Lee, 2017. "Are Commercial Financial Databases Reliable? New Evidence from Korea," Sustainability, MDPI, vol. 9(8), pages 1-23, August.
  112. José Willer Prado & Valderí Castro Alcântara & Francisval Melo Carvalho & Kelly Carvalho Vieira & Luiz Kennedy Cruz Machado & Dany Flávio Tonelli, 2016. "Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1007-1029, March.
  113. Li, Hui & Sun, Jie, 2012. "Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples – Evidence from the Chinese hotel industry," Tourism Management, Elsevier, vol. 33(3), pages 622-634.
  114. Ding, Shusheng & Cui, Tianxiang & Bellotti, Anthony Graham & Abedin, Mohammad Zoynul & Lucey, Brian, 2023. "The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 90(C).
  115. 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.
  116. Carlo Caserio & Delio Panaro & Sara Trucco, 2014. "A statistical analysis of reliability of audit opinions as bankruptcy predictors," Discussion Papers 2014/174, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
  117. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
  118. Li, Jieyi & Qian, Shuangyue & Li, Ling & Guo, Yuanxuan & Wu, Jun & Tang, Ling, 2024. "A novel secondary decomposition method for forecasting crude oil price with twitter sentiment," Energy, Elsevier, vol. 290(C).
  119. 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.
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