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Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises. (2022). Papik, Mario ; Papikova, Lenka.
In: Intelligent Systems in Accounting, Finance and Management.
RePEc:wly:isacfm:v:29:y:2022:i:4:p:254-281.

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  1. Alfaro, E., Garcia, N., Gamez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45, 110–122. https://doi.org/10.1016/j.dss.2007.12.002.
    Paper not yet in RePEc: Add citation now
  2. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23, 589–609. https://doi.org/10.2307/2978933.

  3. Altman, E., Drozdowska, M. I., Laitinen, E., & Suvas, A. (2015). Financial and nonfinancial variables as long‐horizon predictors of bankruptcy. Journal of Credit Risk, 12(4), 49–78. https://doi.org/10.2139/ssrn.2669668.
    Paper not yet in RePEc: Add citation now
  4. Anagnostopoulos, I., & Rizeq, A. (2021). Conventional and neural network target‐matching methods dynamics: The information technology mergers and acquisitions market in the USA. Intelligent Systems in Accounting, Finance and Management, 28(2), 97–118. https://doi.org/10.1002/isaf.1492.
    Paper not yet in RePEc: Add citation now
  5. Angenent, M. N., Barata, A. P., & Takes, F. W. (2020). Large‐scale machine learning for business sector prediction. In SAC '20: Proceedings of the 35th annual ACM symposium on applied computing (pp. 1143–1146). Association for Computing Machinery.
    Paper not yet in RePEc: Add citation now
  6. Antunes, F., Ribeiro, B., & Pereira, F. (2017). Probabilistic modeling and visualization for bankruptcy prediction. Applied Soft Computing, 60, 831–843. https://doi.org/10.1016/j.asoc.2017.06.043.
    Paper not yet in RePEc: Add citation now
  7. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171.

  8. Bermingham, M. L., Pong‐Wong, R., Spiliopoulou, A., Hayward, C., Rudan, I., Campbell, H., Wright, A. F., Wilson, J. F., Agakov, F., Navarro, P., & Haley, C. S. (2015). Application of high‐dimensional feature selection: evaluation for genomic prediction in man. Scientific Reports, 5, 10312. https://doi.org/10.1038/srep10312.
    Paper not yet in RePEc: Add citation now
  9. Boughaci, D., & Alkhawaldeh, A. A. (2020). Appropriate machine learning techniques for credit scoring and bankruptcy prediction in banking and finance: A comparative study. Risk Decision Analysis, 8, 15–24. https://doi.org/10.3233/RDA-180051.
    Paper not yet in RePEc: Add citation now
  10. Brédart, X., Séverin, E., & Veganzones, D. (2021). Human resources and corporate failure prediction modeling: Evidence from Belgium. Journal of Forecasting, 40, 1325–1341. https://doi.org/10.1002/for.2770.

  11. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Wadsworth International Group.
    Paper not yet in RePEc: Add citation now
  12. Callejón, A. M., Casado, A. M., Fernández, M. A., & Paláez, J. I. (2013). A system of insolvency prediction for industrial companies using a financial alternative model with neural networks. International Journal of Computational Intelligence Systems, 6(1), 29–37. https://doi.org/10.1080/18756891.2013.754167.
    Paper not yet in RePEc: Add citation now
  13. Canbas, S., Cabuk, A., & Kilic, S. B. (2005). Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case. European Journal of Operational Research, 166(2), 528–546. https://doi.org/10.1016/j.ejor.2004.03.023.

  14. Cenciarelli, V. G., Greco, G., & Allegrini, M. (2018). Does intellectual capital help predict bankruptcy? Journal of Intellectual Capital, 19, 321–337. https://doi.org/10.1108/JIC-03-2017-0047.
    Paper not yet in RePEc: Add citation now
  15. Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004). Editorial: Special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter, 6(1), 1–6. https://doi.org/10.1145/1007730.1007733.
    Paper not yet in RePEc: Add citation now
  16. Ciampi, F., Giannozzi, A., Marzi, G., & Altman, E. I. (2021). Rethinking SME default prediction: A systematic literature review and future perspectives. Scientometrics, 126, 2141–2188. https://doi.org/10.1007/s11192-020-03856-0.

  17. Cinaroglu, S. (2020). Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods. Intelligent Systems in Accounting, Finance and Management, 27(4), 168–181. https://doi.org/10.1002/isaf.1483.
    Paper not yet in RePEc: Add citation now
  18. Davalos, S., Leng, F., Feroz, E. H., & Cao, Z. (2014). Designing an if–then rules‐based ensemble of heterogeneous bankruptcy classifiers: A genetic algorithm approach. Intelligent Systems in Accounting, Finance and Management, 21(3), 129–153. https://doi.org/10.1002/isaf.1354.
    Paper not yet in RePEc: Add citation now
  19. Divsalar, M., Roodsaz, H., Vahdatinia, F., Norouzzadeh, G., & Behrooz, A. H. (2012). A robust data‐mining approach to bankruptcy prediction: A robust data‐mining approach to bankruptcy prediction. Journal of Forecasting, 31(6), 504–523. https://doi.org/10.1002/for.1232.
    Paper not yet in RePEc: Add citation now
  20. Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. https://doi.org/10.48550/arXiv.1810.11363.
    Paper not yet in RePEc: Add citation now
  21. Euler Hermes Economic Research. (2019). The view: Economic research. Insolvency outlook. Retrieved from: https://www.allianz‐trade.com/content/dam/onemarketing/aztrade/allianz‐trade_com/en_gl/erd/publications/pdf/Global‐Insolvencies‐Jan19.pdf (accessed on 21 September 2021).
    Paper not yet in RePEc: Add citation now
  22. Farooq, U., & Qamar, M. A. J. (2019). Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria. Journal of Forecasting, 38(7), 632–648. https://doi.org/10.1002/for.2588.

  23. Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R (2nd ed.). SAGE Publications.
    Paper not yet in RePEc: Add citation now
  24. FinStat. (2021). Dataset of financial statements 2019. Retrieved from https://finstat.sk/datasety-na-stiahnutie February, 14, 2021.
    Paper not yet in RePEc: Add citation now
  25. Freund, Y., & Schapire, R. E. (1997). A decision‐theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. https://doi.org/10.1006/jcss.1997.1504.
    Paper not yet in RePEc: Add citation now
  26. Friedman, J. H. (1989). Regularized discriminant analysis. Journal of the American Statistical Association, 84(405), 165–175. https://doi.org/10.1080/01621459.1989.10478752.
    Paper not yet in RePEc: Add citation now
  27. García, V., Sánchez, J. S., Marqués, A. I., Florencia, R., & Rivera, G. (2020). Understanding the apparent superiority of over‐sampling through an analysis of local information for class‐imbalanced data. Expert Systems with Applications, 158, 113026. https://doi.org/10.1016/j.eswa.2019.113026.
    Paper not yet in RePEc: Add citation now
  28. Geng, R., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), 236–247. https://doi.org/10.1016/j.ejor.2014.08.016.

  29. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182. https://doi.org/10.1162/153244303322753616.
    Paper not yet in RePEc: Add citation now
  30. Hajek, P., Olej, V., & Myskova, R. (2014). Forecasting corporate financial performance using sentiment in annual reports stakeholder's decision‐making. Technological and Economic Development of Economy, 20(4), 721–738. https://doi.org/10.3846/20294913.2014.979456.
    Paper not yet in RePEc: Add citation now
  31. Hamal, S., & Serval, O. (2021). Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs. International Journal of Computational Intelligence Systems, 14(1), 769–782. https://doi.org/10.2991/ijcis.d.210203.007.
    Paper not yet in RePEc: Add citation now
  32. Han, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques (3rd ed.). Morgan Kaufmann. https://doi.org/10.1016/C2009-0-61819-5.
    Paper not yet in RePEc: Add citation now
  33. Jabeur, S. B., Gharib, C., Mefteh‐Wali, S., & Arfi, W. B. (2021). CatBoost model and artificial intelligence techniques for corporate failure prediction. Technological Forecasting and Social Change, 166, 120658. https://doi.org/10.1016/j.techfore.2021.120658.
    Paper not yet in RePEc: Add citation now
  34. Jabeur, S. B., Stef, N., & Carmona, P. (2022). Bankruptcy prediction using the XGBoost algorithm and variable importance feature engineering. Computational Economy. https://doi.org/10.1007/s10614-021-10227-1.
    Paper not yet in RePEc: Add citation now
  35. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (p. 204). Springer.
    Paper not yet in RePEc: Add citation now
  36. Kleinbaum, D., Kupper, L., Nizam, A., & Rosenberg, E. (2013). Applied regression analysis and other multivariable methods. Nelson Education.
    Paper not yet in RePEc: Add citation now
  37. Kou, G., Xu, Y., Peng, Y., Shen, F., Chen, Y., Chang, K., & Kou, S. (2021). Bankruptcy prediction for SMEs using transactional data and two‐stage multiobjective feature selection. Decision Support Systems, 140, 113429. https://doi.org/10.1016/j.dss.2020.113429.
    Paper not yet in RePEc: Add citation now
  38. Kovacova, M., & Kliestik, T. (2017). Logit and probit application for the prediction of bankruptcy in Slovak companies. Equilibrium, 12(4), 775–791. https://doi.org/10.24136/eq.v12i4.40.

  39. Kuppili, V., Tripathi, D., & Reddy Edla, D. (2019). Credit score classification using spiking extreme learning machine. Computational Intelligence, 36, 402–426. https://doi.org/10.1111/coin.12242.
    Paper not yet in RePEc: Add citation now
  40. Lahmiri, S., Bekiros, S., Giakoumelou, A., & Bezzina, F. (2020). Performance assessment of ensemble learning systems in financial data classification. Intelligent Systems in Accounting, Finance and Management, 27(1), 3–9. https://doi.org/10.1002/isaf.1460.
    Paper not yet in RePEc: Add citation now
  41. Le, T. (2022). A comprehensive survey of imbalanced learning methods for bankruptcy prediction. IET Communications, 16, 433–441. https://doi.org/10.1049/cmu2.12268.
    Paper not yet in RePEc: Add citation now
  42. Le, T., Vo, B., Fujita, H., Nguyen, N.‐T., & Baik, S. W. (2019). A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU‐based extreme gradient boosting. Information Sciences, 494, 294–310. https://doi.org/10.1016/j.ins.2019.04.060.
    Paper not yet in RePEc: Add citation now
  43. Le, T., Vo, M. T., Vo, B., Lee, M. Y., & Baik, S. W. (2019). A hybrid approach using oversampling technique and cost‐sensitive learning for bankruptcy prediction. Complexity, 2019, 1–12. https://doi.org/10.1155/2019/8460934.

  44. Li, H., & Sun, J. (2009). Gaussian case‐based reasoning for business failure prediction with empirical data in China. Information Sciences, 179, 89–108. https://doi.org/10.1016/j.ins.2008.09.003.
    Paper not yet in RePEc: Add citation now
  45. Li, H., Li, C.‐J., Wu, X.‐J., & Sun, J. (2014). Statistics‐based wrapper for feature selection: An implementation on financial distress identification with support vector machine. Applied Soft Computing, 19, 57–67. https://doi.org/10.1016/j.asoc.2014.01.018.
    Paper not yet in RePEc: Add citation now
  46. Liang, D., Lu, C.‐C., Tsai, C.‐F., & Shih, G.‐A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561–572. https://doi.org/10.1016/j.ejor.2016.01.012.

  47. Liang, D., Tsai, C.‐F., & Wu, H.‐T. (2015). The effect of feature selection on financial distress prediction. Knowledge‐Based Systems, 73, 289–297. https://doi.org/10.1016/j.knosys.2014.10.010.
    Paper not yet in RePEc: Add citation now
  48. Lin, W. C., Lu, Y. H., & Tsai, C. F. (2019). Feature selection in single and ensemble learning‐based bankruptcy prediction models. Expert Systems, 36(1), e12335. https://doi.org/10.1111/exsy.12335.
    Paper not yet in RePEc: Add citation now
  49. Liu, Y., Zeng, Q., Li, B., Ma, L., & Ordieres‐Meré, J. (2022). Anticipating financial distress of high‐tech startups in the European Union: A machine learning approach for imbalanced samples. Journal of Forecasting, 41, 1131–1155. https://doi.org/10.1002/for.2852.

  50. Maung, E. T. W., & Aye, Z. M. (2020). Comparison of data mining classification Algorithms: C5.0 and CART for car evaluation and credit card information datasets. National Journal of Parallel and Software Computing, 1(1), 75–80.
    Paper not yet in RePEc: Add citation now
  51. Melo, F. (2013). Area under the ROC curve. In W. Dubitzky, O. Wolkenhauer, K. H. Cho, & H. Yokota (Eds.), Encyclopedia of Systems Biology. Springer.
    Paper not yet in RePEc: Add citation now
  52. Mendes, F., Duarte, J., Vieira, A., & Gaspar‐Cunha, A. (2010). Feature selection for bankruptcy prediction: A multi‐objective optimization approach. In X. Z. Gao, A. Gaspar‐Cunha, M. Köppen, G. Schaefer, & J. Wang (Eds.), Soft Computing in Industrial Applications. Advances in Intelligent and Soft Computing, vol. 75. Springer.
    Paper not yet in RePEc: Add citation now
  53. Molinaro, A. M., Simon, R., & Pfeiffer, R. M. (2005). Prediction error estimation: A comparison of resampling methods. Bioinformatics, 21(15), 3301–3307. https://doi.org/10.1093/bioinformatics/bti499.
    Paper not yet in RePEc: Add citation now
  54. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395.

  55. Papík, M., Papíková, L., & Kajanová, J. (2020). Bankruptcy prediction in chemical industry. Przemysl Chemiczny, 99(12), 1762–1769. https://doi.org/10.15199/62.2020.
    Paper not yet in RePEc: Add citation now
  56. Papík, M., Papíková, L., Kajanová, J., & Bečka, M. (2023). CatBoost: The case of bankruptcy prediction. In B. Alareeni & A. Hamdan (Eds.), Sustainable finance, digitalization and the role of technology. Lecture Notes in Networks and Systems, vol. 487. Springer.
    Paper not yet in RePEc: Add citation now
  57. Patil, N., Lathi, R., & Chitre, V. (2012). Comparison of C5.0 & CART classification. International Journal of Engineering Research & Technology, 1(4), 1–5.
    Paper not yet in RePEc: Add citation now
  58. Pech, M., Prazakova, J., & Pechova, L. (2020). The evaluation of the success rate of corporate failure prediction in a five‐year period. Journal of Competitiveness, 12(1), 108–124. https://doi.org/10.7441/joc.2020.01.07.
    Paper not yet in RePEc: Add citation now
  59. Phuong, T. M., Lin, Z., & Altman, R. B. (2005). Choosing SNPs using feature selection. In 2005 IEEE Computational Systems Bioinformatics Conference (CSB’05), pp. 301–309. https://doi.org/10.1109/CSB.2005.22.
    Paper not yet in RePEc: Add citation now
  60. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Neural Information Processing Systems, 31, 6638–6648.
    Paper not yet in RePEc: Add citation now
  61. Quinlan, J. R. (1994). C4.5: Programs for machine learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. Machine Learning, 16, 235–240.
    Paper not yet in RePEc: Add citation now
  62. Ragab, Y. M., & Saleh, M. A. (2022). Non‐financial variables related to governance and financial distress prediction in SMEs—Evidence from Egypt. Journal of Applied Accounting Research, 23(3), 604–627. https://doi.org/10.1108/JAAR-02-2021-0025.
    Paper not yet in RePEc: Add citation now
  63. Roumani, Y. F., Nwankpa, J. K., & Tanniru, M. (2020). Predicting firm failure in the software industry. Artificial Intelligence Review, 53(6), 4161–4182. https://doi.org/10.1007/s10462-019-09789-2.
    Paper not yet in RePEc: Add citation now
  64. Shin, K.‐S., Lee, T. S., & Kim, H. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), 127–135. https://doi.org/10.1016/j.eswa.2004.08.009.
    Paper not yet in RePEc: Add citation now
  65. Son, H., Hyun, C., Phan, D., & Hwang, H. J. (2019). Data analytic approach for bankruptcy prediction. Expert Systems with Applications, 138, 112816. https://doi.org/10.1016/j.eswa.2019.07.033.
    Paper not yet in RePEc: Add citation now
  66. Soui, M., Smiti, S., Mkaouer, M. W., & Ejbali, R. (2020). Bankruptcy prediction using stacked auto‐encoders. Applied Artificial Intelligence, 34(1), 80–100. https://doi.org/10.1080/08839514.2019.1691849.
    Paper not yet in RePEc: Add citation now
  67. Taffler, R. J. (1984). Empirical models for the monitoring of UK corporations. Journal of Banking & Finance, 8(2), 199–227. https://doi.org/10.1016/0378-4266(84)90004-9.

  68. Tang, X., Li, S., Tan, M., & Shi, W. (2020). Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods. Journal of Forecasting, 39, 769–787. https://doi.org/10.1002/for.2661.

  69. Tian, S., Yu, Y., & Guo, H. (2015). Variable selection and corporate bankruptcy forecasts. Journal of Banking & Finance, 52, 89–100. https://doi.org/10.1016/j.jbankfin.2014.12.003.

  70. Tsai, C.‐F., Sue, K.‐L., Hu, Y.‐H., & Chiu, A. (2021). Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction. Journal of Business Research, 130, 200–209. https://doi.org/10.1016/j.jbusres.2021.03.018.

  71. Tumpach, M., Surovičová, A., Juhászová, Z., Marci, A., & Kubičková, V. (2020). Prediction of the bankruptcy of Slovak companies using neural networks with SMOTE. Ekonomický časopis, 68(10), 1021–1039. https://doi.org/10.31577/ekoncas.2020.10.03.
    Paper not yet in RePEc: Add citation now
  72. Uthayakumar, J., Vengattaraman, T., & Dhavachelvan, P. (2020). Swarm intelligence based classification rule induction (CRI) framework for qualitative and quantitative approach: An application of bankruptcy prediction and credit risk analysis. Journal of King Saud University–Computer and Information Sciences, 32, 647–657. https://doi.org/10.1016/j.jksuci.2017.10.007.
    Paper not yet in RePEc: Add citation now
  73. Wah, Y. B., Ibrahim, N., Hamid, H. A., Rahman, S. A., & Fong, S. (2018). Feature selection methods: Case of filter and wrapper approaches for maximising classification accuracy. Pertanika Journal of Science and Technology, 26(1), 329–340.
    Paper not yet in RePEc: Add citation now
  74. Wang, F., Li, Z., He, F., Wang, R., Yu, W., & Nie, F. (2019). Feature learning viewpoint of AdaBoost and a new algorithm. IEEE Access, 7, 149890–149899. https://doi.org/10.1109/ACCESS.2019.2947359.
    Paper not yet in RePEc: Add citation now
  75. Wang, G., Chen, G., & Chu, Y. (2018). A new random subspace method incorporating sentiment and textual information for financial distress prediction. Electronic Commerce Research and Applications, 29, 30–49. https://doi.org/10.1016/j.elerap.2018.03.004.
    Paper not yet in RePEc: Add citation now
  76. Wang, M., Chen, H., Li, H., Cai, Z., Zhao, X., Tong, C., Li, J., & Xu, X. (2017). Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction. Engineering Applications of Artificial Intelligence, 63, 54–68. https://doi.org/10.1016/j.engappai.2017.05.003.
    Paper not yet in RePEc: Add citation now
  77. Westland, J. C. (2020). Predicting credit card fraud with Sarbanes–Oxley assessments and Fama–French risk factors. Intelligent Systems in Accounting, Finance and Management, 27(2), 95–107. https://doi.org/10.1002/isaf.1472.
    Paper not yet in RePEc: Add citation now
  78. Xiao, Z., Yang, X., Pang, Y., & Dang, X. (2012). The prediction for listed companies' financial distress by using multiple prediction methods with rough set and Dempster–Shafer evidence theory. Knowledge‐Based Systems, 26, 196–206. https://doi.org/10.1016/j.knosys.2011.08.001.
    Paper not yet in RePEc: Add citation now
  79. Xie, C., Luo, C., & Yu, X. (2011). Financial distress prediction based on SVM and MDA methods: The case of Chinese listed companies. Quality & Quantity, 45(3), 671–686. https://doi.org/10.1007/s11135-010-9376-y.

  80. Yeh, C.‐C., Chi, D.‐J., & Lin, Y.‐R. (2014). Going‐concern prediction using hybrid random forests and rough set approach. Information Sciences, 254, 98–110. https://doi.org/10.1016/j.ins.2013.07.011.
    Paper not yet in RePEc: Add citation now
  81. Zelenkov, Y., Fedorova, E., & Chekrizov, D. (2017). Two‐step classification method based on genetic algorithm for bankruptcy forecasting. Expert Systems with Applications, 88, 393–401. https://doi.org/10.1016/j.eswa.2017.07.025.
    Paper not yet in RePEc: Add citation now
  82. Zhang, Y., Liu, R., Heidari, A. A., Wang, X., Chen, Y., Wang, M., & Chen, H. (2021). Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis. Neurocomputing, 430, 185–212. https://doi.org/10.1016/j.neucom.2020.10.038.
    Paper not yet in RePEc: Add citation now
  83. Zhou, L., Tam, K. P., & Fujita, H. (2016). Predicting the listing status of Chinese listed companies with multi‐class classification models. Information Sciences, 328, 222–236. https://doi.org/10.1016/j.ins.2015.08.036.
    Paper not yet in RePEc: Add citation now
  84. Zoričák, M., Gnip, P., Drotár, P., & Gazda, V. (2020). Bankruptcy prediction for small‑ and medium‐sized companies using severely imbalanced datasets. Economic Modelling, 84, 165–176. https://doi.org/10.1016/j.econmod.2019.04.003.
    Paper not yet in RePEc: Add citation now

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    RePEc:sae:emffin:v:21:y:2022:i:1:p:92-115.

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  2. Modelling cross-sectional tabular data using convolutional neural networks: Prediction of corporate bankruptcy in Poland. (2021). Maciej, Odziemczyk ; Aneta, Dzik-Walczak.
    In: Central European Economic Journal.
    RePEc:vrs:ceuecj:v:8:y:2021:i:55:p:352-377:n:22.

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  3. Discriminant Analysis and Firms’ Bankruptcy: Evidence from European SMEs. (2021). Cuoccio, Mariateresa ; Giannotti, Claudio ; Bussoli, Candida.
    In: International Journal of Business and Management.
    RePEc:ibn:ijbmjn:v:14:y:2021:i:12:p:164.

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  4. Explanatory Factors of Business Failure: Literature Review and Global Trends. (2021). Martin-Cervantes, Pedro Antonio ; del Carmen, Maria ; Farias, Fernando Zambrano.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:18:p:10154-:d:633391.

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  5. Research on financial early warning of mining listed companies based on BP neural network model. (2021). Lei, Yalin ; Sun, Xiaojun.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:73:y:2021:i:c:s0301420721002348.

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  6. A Study on Firms with Negative Book Value of Equity. (2021). Tripathy, Niranjan ; Liu, Ian ; Luo, Haowen.
    In: International Review of Finance.
    RePEc:bla:irvfin:v:21:y:2021:i:1:p:145-182.

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  7. CREWS: a CAMELS-based early warning system of systemic risk in the banking sector. (2021). Galan, Jorge.
    In: Occasional Papers.
    RePEc:bde:opaper:2132.

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  8. Measurement and Comparison of the Financial Soundness of Conventional and Islamic Commercial Banks in Bangladesh using the Altman€™s Z Score Model. (2021). Rana, Shohel ; Dr, Professor.
    In: International Journal of Science and Business.
    RePEc:aif:journl:v:5:y:2021:i:12:p:52-62.

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  9. The Analysis of Repayment of Default Bonds: Evidence from China. (2020). Li, LI.
    In: Journal of Applied Finance & Banking.
    RePEc:spt:apfiba:v:10:y:2020:i:2:f:10_2_5.

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  10. Evaluation of financial health of companies through data envelopment analysis: Selection of variables for the DEA model in R. (2020). Kavakova, Michaela ; Exenberger, Emil.
    In: Proceedings of Economics and Finance Conferences.
    RePEc:sek:iefpro:10913067.

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  11. State ownership and the structuring of lease arrangements. (2020). Zhang, Shanshan ; Liu, Chang.
    In: Journal of Corporate Finance.
    RePEc:eee:corfin:v:62:y:2020:i:c:s0929119920300419.

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  12. Using Data Envelopment Analysis in Credit Risk Evaluation of ICT Companies. (2020). Kavakova, Michaela ; Koiova, Kristina.
    In: AGRIS on-line Papers in Economics and Informatics.
    RePEc:ags:aolpei:309925.

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  13. Financial distress prevention in China: Does gender of board of directors matter?. (2019). Zhou, Guanping.
    In: Journal of Applied Finance & Banking.
    RePEc:spt:apfiba:v:9:y:2019:i:6:f:9_6_8.

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  14. Managerial Self-Attribution Bias and Banks’ Future Performance: Evidence from Emerging Economies. (2019). Iqbal, Javid.
    In: JRFM.
    RePEc:gam:jjrfmx:v:12:y:2019:i:2:p:73-:d:225828.

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  15. How well does management deliver? Creation of shareholder wealth by large public and private Brazilian firms in 2018. (2019). Sanvicente, Antonio.
    In: Textos para discussão.
    RePEc:fgv:eesptd:519.

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  16. A hybrid neural network model based on improved PSO and SA for bankruptcy prediction. (2019). Achchab, Said ; Azayite, Fatima Zahra.
    In: Papers.
    RePEc:arx:papers:1907.12179.

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  17. Modelaci—n del riesgo de insolvencia en empresas del sector salud empleando modelos logit || Modeling of Insolvency Risk in Health Sector Companies Using Logit Models. (2018). Támara Ayus, Armando.
    In: Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration.
    RePEc:pab:rmcpee:v:26:y:2018:i:1:p:128-145.

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  18. Using Altman’s Z score (Sales/Total Assets) Ratio Model in Assessing Likelihood of Bankruptcy for Sugar Companies in Kenya. (2018). Range, Maurice Mwita ; Waititu, Gichuhi A ; Njeru, Agnes.
    In: International Journal of Academic Research in Business and Social Sciences.
    RePEc:hur:ijarbs:v:8:y:2018:i:6:p:683-703.

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  19. Innovative Social Policies for the Romanian Agriculture. (2018). Pop, Ionut Daniel ; Radutu, Andrei.
    In: International Journal of Academic Research in Business and Social Sciences.
    RePEc:hur:ijarbs:v:8:y:2018:i:5:p:417-435.

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  20. Prediction of company failure: Past, present and promising directions for the future. (2018). Jayasekera, Ranadeva.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:55:y:2018:i:c:p:196-208.

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  21. Forewarning Bankruptcy: An Indigenous Model for Pakistan. (2017). Hunjra, Ahmed ; Azam, Rauf I ; Ijaz, Muhammad Shahzad.
    In: Business & Economic Review.
    RePEc:bec:imsber:v:9:y:2017:i:4:p:259-286.

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  22. MODELLING BANKRUPTCY USING HUNGARIAN FIRM-LEVEL DATA. (2016). Endresz, Marianna ; Bauer, Peter.
    In: MNB Occasional Papers.
    RePEc:mnb:opaper:2016/122.

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  23. A Comparison of Jordanian Bankruptcy Models: Multilayer Perceptron Neural Network and Discriminant Analysis. (2016). Al-Hroot, Yusuf Ali .
    In: International Business Research.
    RePEc:ibn:ibrjnl:v:9:y:2016:i:12:p:121-130.

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  24. The performance of hybrid models in the assessment of default risk. (2016). Zouari, Sami ; Levyne, Olivier ; Bellalah, Mondher.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:52:y:2016:i:pa:p:259-265.

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  25. The Control and Performance of Joint Ventures. (2016). Mantecon, Tomas ; Song, Kyojik ; Luo, Haowen.
    In: Financial Management.
    RePEc:bla:finmgt:v:45:y:2016:i:2:p:431-465.

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  26. Business Failure Prediction: An emperical study based on Survival Analysis and Generalized Linear Modelling (GLM) Techniques. (2015). Bunyaminu, Alhassan.
    In: International Journal of Financial Economics.
    RePEc:rss:jnljfe:v4i3p2.

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  27. Effect Ownership, Accountant Public Office, and Financial Distress to the Public Company Financial Fraudulent Reporting in Indonesia. (2015). Mardiana, Ana.
    In: Journal of Economics and Behavioral Studies.
    RePEc:rnd:arjebs:v:7:y:2015:i:2:p:109-115.

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  28. MODEL PREDIKSI FINANCIAL DISTRESS DENGAN BINARY LOGIT (STUDI KASUS EMITEN JAKARTA ISLAMIC INDEX). (2015). Iskandar, Azwar.
    In: MPRA Paper.
    RePEc:pra:mprapa:82694.

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  29. Accounting data and the credit spread: An empirical investigation. (2015). Tucker, Jon ; Guermat, Cherif ; Demirovic, Amer.
    In: Research in International Business and Finance.
    RePEc:eee:riibaf:v:34:y:2015:i:c:p:233-250.

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  30. An analysis of the determinants of financial distress in Italy: A competing risks approach. (2015). Restaino, Marialuisa ; Amendola, Alessandra ; Sensini, Luca.
    In: International Review of Economics & Finance.
    RePEc:eee:reveco:v:37:y:2015:i:c:p:33-41.

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  31. Soft Information and Default Prediction in Cooperative and Social Banks. (2014). CORNEE, Simon.
    In: Journal of Entrepreneurial and Organizational Diversity.
    RePEc:trn:csnjrn:v:3:i:1:p:89-109.

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  32. Evaluation of Financial Distress: A Case Study of UCHUMI Supermarket Ltd. (2014). , Waita ; Gichaiya, M ; Ishmail, D.
    In: International Journal of Financial Economics.
    RePEc:rss:jnljfe:v3i3p6.

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  33. Corporate Failure Prediction: A Fresh Technique for Dealing Effectively With Normality Based On Quantitative and Qualitative Approach. (2014). Bashiru, Shani ; Bunyaminu, Alhassan.
    In: International Journal of Financial Economics.
    RePEc:rss:jnljfe:v2i1p1.

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  34. Modeling firm specific internationalization risk: An application to banks’ risk assessment in lending to firms that do international business. (2014). Jonsson, Sara ; Eriksson, Kent ; Lindbergh, Jessica ; Lindstrand, Angelika.
    In: International Business Review.
    RePEc:eee:iburev:v:23:y:2014:i:6:p:1074-1085.

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  35. Creditor control rights, state of nature verification, and financial reporting conservatism. (2013). Tan, Liang.
    In: Journal of Accounting and Economics.
    RePEc:eee:jaecon:v:55:y:2013:i:1:p:1-22.

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  36. ENTREPRENEURIAL RISK AND PERFORMANCE: EMPIRICAL EVIDENCE OF ROMANIAN AGRICULTURAL HOLDINGS.. (2013). Burja, Camelia.
    In: Annales Universitatis Apulensis Series Oeconomica.
    RePEc:alu:journl:v:2:y:2013:i:15:p:21.

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  37. Retail Bankruptcy Prediction. (2013). Kogel, Mark ; Pang, Johnny.
    In: American Journal of Economics and Business Administration.
    RePEc:abk:jajeba:ajebasp.2013.29.46.

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  38. AN ALGORITHM FOR THE DETECTION OF REVENUE AND RETAINED EARNINGS MANIPULATION. (2012). Pustylnick, Igor .
    In: Accounting & Taxation.
    RePEc:ibf:acttax:v:4:y:2012:i:2:p:95-105.

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  39. Arbitrary truncated Levy flight: Asymmetrical truncation and high-order correlations. (2012). Vinogradov, Dmitry V..
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:391:y:2012:i:22:p:5584-5597.

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  40. Financing Decisions and Discretionary Accruals: Managerial Manipulation or Managerial Overoptimism. (2011). Szewczyk, Samuel H. ; Marciukaityte, Dalia.
    In: Review of Behavioral Finance.
    RePEc:eme:rbfpps:v:3:y:2011:i:2:p:91-114.

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  41. A Study on Performance Evaluation of Dinesh Mills Ltd. (2011). Bhayani, Sanjay J ; Ajmera, Butalal.
    In: Indian Journal of Commerce and Management Studies.
    RePEc:aii:ijcmss:v:2:y:2011:i:6:p:114-123.

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  42. A Risk-Based Debt Sustainability Framework: Incorporating Balance Sheets and Uncertainty. (2008). Malone, Samuel ; Loukoianova, Elena ; Gray, Dale F ; Lim, Cheng Hoon.
    In: IMF Working Papers.
    RePEc:imf:imfwpa:2008/040.

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  43. Corporate cash holdings: Evidence from a different institutional setting. (2006). Dobetz, Wolfgang ; Gruninger, Matthias C..
    In: Working papers.
    RePEc:bsl:wpaper:2006/06.

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  44. Small, US Air Carrier Financial Condition: A Back-Propagation Neural Network Approach to Forecasting Bankruptcy and Financial Stress. (2002). Davalos, Sergio ; Gritta, Richard D ; Wang, Marcus ; Chow, Garland.
    In: Journal of the Transportation Research Forum.
    RePEc:ags:ndjtrf:317621.

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  45. Estimation du risque de défaut par une modélisation stochastique du bilan : Application à des firmes industrielles françaises. (2000). Refait-Alexandre, Catherine.
    In: Post-Print.
    RePEc:hal:journl:halshs-03718527.

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  46. Estimation du risque de défaut par une modélisation stochastique du bilan : Application à des firmes industrielles françaises. (2000). Refait-Alexandre, Catherine.
    In: Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers).
    RePEc:hal:cesptp:halshs-03718527.

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  47. A non-parametric statistical model for the control of Italian insurance companies. (1994). Ottaviani, Riccardo ; Angelis, Paolo ; Gismondi, Fulvio.
    In: Decisions in Economics and Finance.
    RePEc:spr:decfin:v:17:y:1994:i:1:p:69-84.

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  48. OPTIMAL USE OF QUALITATIVE MODELS: AN APPLICATION TO COUNTRY GRAIN ELEVATOR BANKRUPTCIES. (1988). Kaylen, Michael S. ; Procter, Michael H. ; Devino, Gary T..
    In: Southern Journal of Agricultural Economics.
    RePEc:ags:sojoae:29262.

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  49. Business Failures, Managerial Competence, and Macroeconomic Variables. (1977). sawhney, bansi ; Dipietro, William.
    In: Entrepreneurship Theory and Practice.
    RePEc:sae:entthe:v:2:y:1977:i:2:p:4-15.

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  50. Power Parity and Lethal International Violence, 1969€“1973. (1976). .
    In: Journal of Conflict Resolution.
    RePEc:sae:jocore:v:20:y:1976:i:3:p:379-394.

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