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Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)

Abstract

Bibliometrics are important evaluation tools to the scientific production. A bibliometric study was used to evaluate researches about credit risk and bankruptcy. Credit support technique studies on economic and social orders are relevant in this knowledge field. Therefore, the aim of the current study is to identify and describe the application of multivariate data analysis techniques to credit risk and bankruptcy scenarios. The herein presented data were collected in publications indexed to Thomson Reuters’ Web of Science database between 1968 and 2014. The results corroborate information in the literature and in previous bibliometric reviews, as well as highlight other indications regarding the construction and development of research fields. Since the 1990’s, the neural networks became relevant due to their increased use as study object in publications. However, both the discriminant analysis (J Finance 23(4):589–609, 1968. doi:10.2307/2978933) and the logistic regression (J Account Res 18(1):109–131, 1980. doi:10.2307/2490395) are still often used in researches, fact that shows the tendency to find articles using more than one technique or hybrid models, artificial intelligence techniques and complex computer systems. This field appears to be multidisciplinary in journals and Web Science databases involving the business and economy, operational research, management, mathematics, data processing, engineering and statistics fields. Another relevant discovery was the increased number of publications about this subject launched right after the 2008 crisis.

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References

  1. Akkoç, S. (2012). An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data. European Journal of Operational Research, 222(1), 168–178. doi:10.1016/j.ejor.2012.04.009.

    Article  Google Scholar 

  2. Altman, E. I. (1968). Financial ratios, discriminant analysis and prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609. doi:10.2307/2978933.

    Article  Google Scholar 

  3. Anderson, T. W. (1958). An introduction to multivariate statistical analysis (Vol. 2, pp. 3–5). New York: Wiley.

    MATH  Google Scholar 

  4. Arslan, O., & Karan, M. B. (2010). Consumer credit risk characteristics: Understanding income and expense differentials. Emerging Markets Finance and Trade, 46(2), 20–37. doi:10.2753/ree1540-496x460202.

    Article  Google Scholar 

  5. Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627–635. doi:10.1057/palgrave.jors.2601545.

    Article  MATH  Google Scholar 

  6. Barbosa, J. S. K., & Reinert, M. (2014). Open Innovation: Uma Análise Bibliométrica do Período de 2003 a 2013. In Enanpad (Ed.), Anais do Encontro Nacional da Associação Nacional de Pós-Graduação e Pesquisa em Administração (p. 38). RJ, Brasil: Rio de Janeiro.

    Google Scholar 

  7. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research,. doi:10.2307/2490171.

    Google Scholar 

  8. Bellovary, J. L., Giacomino, D. E., & Akers, M. D. (2007). A review of bankruptcy prediction studies: 1930 to present. Journal of Financial Education, 33, 1–42.

    Google Scholar 

  9. Bittar, M., da Silva, M. R., & Hayashi, M. C. P. I. (2011). Produção científica em dois periódicos da área de educação. Avaliação: Revista da Avaliação da Educação Superior, 16(3), 655–674.

    Article  Google Scholar 

  10. Bojović, S., Matić, R., Popović, Z., Smiljanić, M., Stefanović, M., & Vidaković, V. (2014). An overview of forestry journals in the period 2006–2010 as basis for ascertaining research trends. Scientometrics, 98(2), 1331–1346. doi:10.1007/s11192-013-1171-9.

    Article  Google Scholar 

  11. Botelho, L. L. R., Cunha, C. C. D. A., & Macedo, M. (2011). O método da revisão integrativa nos estudos organizacionais. Gestão e Sociedade, 5(11), 121–136.

    Google Scholar 

  12. Bourdieu, P. (1994). O campo científico. In R. Ortiz (Ed.), Pierre Bourdieu: Sociologia (2nd ed., pp. 122–155). São Paulo, SP: Ática.

    Google Scholar 

  13. Brito, G. A. S., Assaf Neto, A., & Corrar, L. J. (2009). Sistema de classificação de risco de crédito: Uma aplicação a companhias abertas no Brasil. Revista Contabilidade & Finanças, 20(51), 28–43. doi:10.1590/s1519-70772009000300003.

    Article  Google Scholar 

  14. Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences, 101(suppl 1), 5303–5310. doi:10.1073/pnas.0307513100.

    Article  Google Scholar 

  15. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. doi:10.1002/asi.20317.

    Article  Google Scholar 

  16. Chen, M.-Y. (2012). Comparing traditional statistics, decision tree classification and support vector machine techniques for financial bankruptcy prediction. Intelligent Automation and Soft Computing, 18(1), 65–73. doi:10.1080/10798587.2012.10643227.

    Article  Google Scholar 

  17. Chen, Y.-S., & Cheng, C.-H. (2013). Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry. Knowledge-Based Systems, 39, 224–239. doi:10.1016/j.knosys.2012.11.004.

    Article  Google Scholar 

  18. Chi, B.-W., & Hsu, C.-C. (2012). A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model. Expert Systems with Applications, 39(3), 2650–2661. doi:10.1016/j.eswa.2011.08.120.

    Article  Google Scholar 

  19. Chen, X., Qi, H., & Li, W. (2003). A neural network model for bankruptcy prediction. Dynamics of Continuous Discrete and Impulsive Systems-Series B-Applications & Algorithms, Suppl., 230–237.

    Google Scholar 

  20. Chuang, C.-L. (2013). Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction. Information Sciences, 236, 174–185. doi:10.1016/j.ins.2013.02.015.

    MathSciNet  Article  Google Scholar 

  21. Corrar, L. J., Paulo, E., & Dias, J. M. F. (2014). Análise multivariada: Para os cursos de administração, ciências contábeis e economia (pp. 280–323). São Paulo: Atlas.

    Google Scholar 

  22. Crone, S. F., & Finlay, S. (2012). Instance sampling in credit scoring: An empirical study of sample size and balancing. International Journal of Forecasting, 28(1), 224–238. doi:10.1016/j.ijforecast.2011.07.006.

    Article  Google Scholar 

  23. Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447–1465. doi:10.1016/j.ejor.2006.09.100.

    MathSciNet  Article  MATH  Google Scholar 

  24. Daubie, M., & Meskens, N. (2002). Business failure prediction: A review and analysis of the literature. In C. Zopounidis (Ed.), New trends in banking management (pp. 71–86). Physica-Verlag HD, doi:10.1007/978-3-642-57478-8_5.

    Chapter  Google Scholar 

  25. Demyanyk, Y., & Hasan, I. (2010). Financial crises and bank failures: A review of prediction methods. Omega, 38(5), 315–324. doi:10.1016/j.omega.2009.09.007.

    Article  Google Scholar 

  26. Desai, V. S., Crook, J. N., & Overstreet, G. A. (1996). A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95(1), 24–37. doi:10.1016/0377-2217(95)00246-4.

    Article  MATH  Google Scholar 

  27. Dimitras, A. I., Slowinski, R., Susmaga, R., & Zopounidis, C. (1999). Business failure prediction using rough sets. European Journal of Operational Research, 114(2), 263–280. doi:10.1016/s0377-2217(98)00255-0.

    Article  MATH  Google Scholar 

  28. Dimitras, A. I., Zanakis, S. H., & Zopounidis, C. (1996). A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90(3), 487–513. doi:10.1016/0377-2217(95)00070-4.

    Article  MATH  Google Scholar 

  29. Durand, D. (1941). Risk elements in consumer installment lending. In National Bureau of Economic Research (Ed.), Studies in consumer installment financing, 8. New York: National Bureau of Economic Research, Inc.

    Google Scholar 

  30. Encyclopedia of Quantitative Finance (EQF—Wiley). (2015). http://www.wiley.com//legacy/wileychi/eqf/. Accessed 22 Apr 2015.

  31. European Journal of Operational Research (EJOR). (2015). Author information pack. www.elsevier.com/locate/ejor. Accessed 22 Apr 2015.

  32. Expert Systems with Applications. (2015). Author information pack. www.elsevier.com/locate/eswa. Accessed 22 Apr 2015.

  33. Falbo, P. (1991). Credit-scoring by enlarged discriminant models. Omega-International Journal of Management Science, 19(4), 275–289. doi:10.1016/0305-0483(91)90045-u.

    Article  Google Scholar 

  34. Feinstein, A. R. (1996). Multivariable analysis: An introduction. New Haven, CT: Yale University Press.

    Google Scholar 

  35. Fernandez, E., & Olmeda, I. (1995). Bankruptcy prediction with artificial neural networks. In J. Mira & F. Sandoval (Eds.), From natural to artificial neural computation (Vol. 930, pp. 1142–1146). Berlin: Springer. doi:10.1007/3-540-59497-3_296.

  36. Finlay, S. (2011). Multiple classifier architectures and their application to credit risk assessment. European Journal of Operational Research, 210(2), 368–378. doi:10.1016/j.ejor.2010.09.029.

    Article  Google Scholar 

  37. Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. doi:10.1111/j.1469-1809.1936.tb02137.x.

    Article  Google Scholar 

  38. Fletcher, D., & Goss, E. (1993). Forecasting with neural networks—An application using bankruptcy data. Information & Management, 24(3), 159–167. doi:10.1016/0378-7206(93)90064-z.

    Article  Google Scholar 

  39. Francisco, E. D. R. (2011). RAE-eletrônica: Exploration of archive in the light of bibliometrics, geoanalysis and social network. Revista de Administração de Empresas, 51(3), 280–306. doi:10.1590/S0034-75902011000300008.

    MathSciNet  Article  Google Scholar 

  40. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall.

    Google Scholar 

  41. Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society: Series A (Statistics in Society), 160(3), 523–541. doi:10.1111/j.1467-985X.1997.00078.x.

    Article  Google Scholar 

  42. Hassan, S. U., Haddawy, P., & Zhu, J. (2014). A bibliometric study of the world’s research activity in sustainable development and its sub-areas using scientific literature. Scientometrics, 99(2), 549–579. doi:10.1007/s11192-013-1193-3.

    Article  Google Scholar 

  43. Haykin, S. (2001). Redes Neurais: Princípios e Prática (2a ed.). Porto Alegre: Bookman.

    Google Scholar 

  44. Hillegeist, S. A., Keating, E. K., Cram, D. P., & Lundstedt, K. G. (2004). Assessing the probability of bankruptcy. Review of Accounting Studies, 9(1), 5–34. doi:10.1023/B:RAST.0000013627.90884.b7.

    Article  Google Scholar 

  45. Himmelstein, D. U., Thorne, D., Warren, E., & Woolhandler, S. (2009). Medical Bankruptcy in the United States, 2007: Results of a National Study. American Journal of Medicine, 122(8), 741–746. doi:10.1016/j.amjmed.2009.04.012.

    Article  Google Scholar 

  46. Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37(4), 543–558. doi:10.1016/s0167-9236(03)00086-1.

    Article  Google Scholar 

  47. Huang, C.-L., Chen, M.-C., & Wang, C.-J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications, 33(4), 847–856. doi:10.1016/j.eswa.2006.07.007.

    MathSciNet  Article  Google Scholar 

  48. Hung, K., Cheng, H. W., Chen, S.-S., & Huang, Y. C. (2013). Factors that affect credit rating: An application of ordered probit models. Romanian Journal of Economic Forecasting, 16(4), 94–108.

    Google Scholar 

  49. Jo, H., & Han, I. (1996). Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction. Expert Systems with Applications, 11(4), 415–422. doi:10.1016/s0957-4174(96)00056-5.

    Article  Google Scholar 

  50. Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis (Vol. 4). Englewood Cliffs, NJ: Prentice hall.

    MATH  Google Scholar 

  51. Kao, L.-J., Chiu, C.-C., & Chiu, F.-Y. (2012). A Bayesian latent variable model with classification and regression tree approach for behavior and credit scoring. Knowledge-Based Systems, 36, 245–252. doi:10.1016/j.knosys.2012.07.004.

    Article  Google Scholar 

  52. Keasey, K., & Watson, R. (1991). Financial distress prediction models: A review of their usefulness. British Journal of Management, 2(2), 89–102. doi:10.1111/j.1467-8551.1991.tb00019.x.

    Article  Google Scholar 

  53. Krampen, G., Weiland, P., & Wiesenhütter, J. (2015). Citation success of different publication types: A case study on all references in psychology publications from the German-speaking countries (D–A–CH–L–L) in 2009, 2010, and 2011. Scientometrics,. doi:10.1007/s11192-015-1573-y.

    Google Scholar 

  54. Kumar, P. R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques–A review. European Journal of Operational Research, 180(1), 1–28. doi:10.1016/j.ejor.2006.08.043.

    MathSciNet  Article  MATH  Google Scholar 

  55. Lee, T. S., Chiu, C. C., Lu, C. J., & Chen, I. F. (2002). Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications, 23(3), 245–254. doi:10.1016/s0957-4174(02)00044-1.

    Article  Google Scholar 

  56. Lee, K. C., Han, I. G., & Kwon, Y. S. (1996). Hybrid neural network models for bankruptcy predictions. Decision Support Systems, 18(1), 63–72. doi:10.1016/0167-9236(96)00018-8.

    MathSciNet  Article  Google Scholar 

  57. Leshno, M., & Spector, Y. (1996). Neural network prediction analysis: The bankruptcy case. Neurocomputing, 10(2), 125–147. doi:10.1016/0925-2312(94)00060-3.

    Article  Google Scholar 

  58. Li, J. P., Xu, W. X., & Shi, Y. (2005). Credit scoring via PCALWM. In V. S. Sunderam, G. D. VanAlbada, P. M. A. Sloot, & J. J. Dongarra (Eds.), Computational scienceIccs 2005, Pt 3 (Vol. 3516, pp. 531–538, Lecture Notes in Computer Science). Berlin: Springer. doi:10.1007/11428862_73.

  59. Liu, G. (2013). Visualization of patents and papers in terahertz technology: A comparative study. Scientometrics, 94(3), 1037–1056. doi:10.1007/s11192-012-0782-x.

    Article  Google Scholar 

  60. Liu, W., Gu, M., Hu, G., Li, C., Liao, H., Tang, L., & Shapira, P. (2014). Profile of developments in biomass-based bioenergy research: A 20-year perspective. Scientometrics, 99(2), 507–521. doi:10.1007/s11192-013-1152-z.

    Article  Google Scholar 

  61. Liu, X. D., & Liu, W. Q. (2005). Credit rating analysis with AFS fuzzy logic. In L. Wang, K. Chen, & Y. S. Ong (Eds.), Advances in natural computation, Pt 3, proceedings (Vol. 3612, pp. 1198–1204, Lecture Notes in Computer Science). doi:10.1007/11539902_152.

  62. Lo, A. W. (1986). Logit versus discriminant-analysis—A specification test and application to corporate bankruptcies. Journal of Econometrics, 31(2), 151–178. doi:10.1016/0304-4076(86)90046-1.

    Article  Google Scholar 

  63. Macias-Chapula, C. A. (1998). O papel da informetria e da cienciometria e sua perspectiva nacional e internacional. Ciência da Informação, 27(2), 134–140.

    Article  Google Scholar 

  64. Mardia, K. V., Kent, J. T., & Bibby, J. M. (1979). Multivariate analysis. London: Academic Press.

    MATH  Google Scholar 

  65. Min, J. H., & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603–614. doi:10.1016/j.eswa.2004.12.008.

    Article  Google Scholar 

  66. Min, S. H., Lee, J., & Han, I. (2006). Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, 31(3), 652–660. doi:10.1016/j.eswa.2005.09.070.

    Article  Google Scholar 

  67. Murcia, F. C. S., Murcia, F. D., & Borba, J. A. (2014). Rating de crédito corporativo: Revisão da literatura e oportunidades para pesquisa no cenário brasileiro. Revista de Economia e Administração, 13(1), 54–96. doi:10.11132/rea.2013.773.

    Article  Google Scholar 

  68. Nikolic, N., Zarkic-Joksimovic, N., Stojanovski, D., & Joksimovic, I. (2013). The application of brute force logistic regression to corporate credit scoring models: Evidence from Serbian financial statements. Expert Systems with Applications, 40(15), 5932–5944. doi:10.1016/j.eswa.2013.05.022.

    Article  Google Scholar 

  69. Odom, M. D., & Sharda, R. (1990). A neural network model for bankruptcy prediction. In Neural networks, 1990., 1990 IJCNN international joint conference on (pp. 163–168), San Diego, CA, doi:10.1109/IJCNN.1990.137710.

  70. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. doi:10.2307/2490395.

    MathSciNet  Article  Google Scholar 

  71. Pinto, C. F., Serra, F. R., & Ferreira, M. P. (2014). A bibliometric study on culture research in International Business. BAR-Brazilian Administration Review, 11(3), 340–363. doi:10.1590/1807-7692bar2014309.

    Article  Google Scholar 

  72. Ramos-Rodríguez, A. R., & Ruíz-Navarro, J. (2004). Changes in the intellectual structure of strategic management research: A bibliometric study of the Strategic Management Journal, 1980–2000. Strategic Management Journal, 25(10), 981–1004. doi:10.1002/smj.397.

    Article  Google Scholar 

  73. Sabato, G. (2009). Modelos de Scoring de Risco de Crédito. Revista Tecnologia de Crédito., 68(2), 29–47.

    Google Scholar 

  74. Shin, K. S., Lee, T. S., & Kim, H. J. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), 127–135. doi:10.1016/j.eswa.2004.08.009.

    Article  Google Scholar 

  75. Tam, K. Y. (1991). Neural network models and the prediction of bank bankruptcy. Omega-International Journal of Management Science, 19(5), 429–445. doi:10.1016/0305-0483(91)90060-7.

    Article  Google Scholar 

  76. Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38(7), 926–947. doi:10.1287/mnsc.38.7.926.

    Article  MATH  Google Scholar 

  77. Tang, T. C., & Chi, L. C. (2005a). Neural networks analysis in business failure prediction of Chinese importers: A between-countries approach. Expert Systems with Applications, 29(2), 244–255. doi:10.1016/j.eswa.2005.03.003.

    Article  Google Scholar 

  78. Tang, T. C., & Chi, L. C. (2005b). Predicting. multilateral trade credit risks: Comparisons of Logit and Fuzzy Logic models using ROC curve analysis. Expert Systems with Applications, 28(3), 547–556. doi:10.1016/j.eswa.2004.12.016.

    Article  Google Scholar 

  79. Taşkin, Z., & Al, U. (2014). Standardization problem of author affiliations in citation indexes. Scientometrics, 98(1), 347–368. doi:10.1007/s11192-013-1004-x.

    Article  Google Scholar 

  80. Terentyev, A. N., Bidyuk, P. I., Mironova, A. V., & Medin, N. Y. (2009). Comparison of data mining methods while credit rating of natural persons. Journal of Automation and Information Sciences, 41(10), 71–80. doi:10.1615/JAutomatInfScien.v41.i10.60.

    Article  Google Scholar 

  81. Udo, G. (1993). Neural-network performance on the bankruptcy classification problem. Computers & Industrial Engineering, 25(1–4), 377–380. doi:10.1016/0360-8352(93)90300-m.

    Article  Google Scholar 

  82. Vanti, N. A. P. (2002). Da bibliometria à webometria: Uma exploração conceitual dos mecanismos utilizados para medir o registro da informação e a difusão do conhecimento. Ciência da Informação, 31(2), 152–162.

    Article  Google Scholar 

  83. Virgillito, S. B., & Famá, R. (2008). A análise estatística multivariada na previsão de insolvência de empresas. Revista Administração em Diálogo (RAD), 4(1), 1–27.

    Google Scholar 

  84. West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27(11–12), 1131–1152. doi:10.1016/s0305-0548(99)00149-5.

    Article  MATH  Google Scholar 

  85. Wilson, R. L., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(5), 545–557. doi:10.1016/0167-9236(94)90024-8.

    Article  Google Scholar 

  86. Wu, C.-H., Tzeng, G.-H., Goo, Y.-J., & Fang, W.-C. (2007). A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Systems with Applications, 32(2), 397–408. doi:10.1016/j.eswa.2005.12.008.

    Article  Google Scholar 

  87. Yap, B. W., Ong, S. H., & Husain, N. H. M. (2011). Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Systems with Applications, 38(10), 13274–13283. doi:10.1016/j.eswa.2011.04.147.

    Article  Google Scholar 

  88. Yu, L., Wang, S., & Lai, K. K. (2009). An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring. European Journal of Operational Research, 195(3), 942–959. doi:10.1016/j.ejor.2007.11.025.

    MathSciNet  Article  MATH  Google Scholar 

  89. Yu, L., Yue, W., Wang, S., & Lai, K. K. (2010). Support vector machine based multiagent ensemble learning for credit risk evaluation. Expert Systems with Applications, 37(2), 1351–1360. doi:10.1016/j.eswa.2009.06.083.

    Article  Google Scholar 

  90. Zhang, Y., Wang, S., & Ji, G. (2013). A rule-based model for bankruptcy prediction based on an improved genetic ant colony algorithm. Mathematical Problems in Engineering, 2013, 1–10. doi:10.1155/2013/753251.

    Google Scholar 

  91. Zhou, L., Lai, K. K., & Yen, J. (2009). Credit scoring models with AUC maximization based on weighted SVM. International Journal of Information Technology & Decision Making, 8(4), 677–696. doi:10.1142/s0219622009003582.

    Article  MATH  Google Scholar 

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do Prado, J.W., de Castro Alcântara, V., de Melo Carvalho, F. et al. Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014). Scientometrics 106, 1007–1029 (2016). https://doi.org/10.1007/s11192-015-1829-6

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Keywords

  • Bankruptcy
  • Credit risk
  • Multivariate data analysis
  • Bibliometrics