IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v106y2016i3d10.1007_s11192-015-1829-6.html
   My bibliography  Save this article

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

Author

Listed:
  • José Willer Prado

    (UFLA – Universidade Federal de Lavras)

  • Valderí Castro Alcântara

    (UFLA – Universidade Federal de Lavras)

  • Francisval Melo Carvalho

    (UFLA – Universidade Federal de Lavras)

  • Kelly Carvalho Vieira

    (UFLA – Universidade Federal de Lavras)

  • Luiz Kennedy Cruz Machado

    (UFLA – Universidade Federal de Lavras)

  • Dany Flávio Tonelli

    (UFLA – Universidade Federal de Lavras)

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.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:106:y:2016:i:3:d:10.1007_s11192-015-1829-6
    DOI: 10.1007/s11192-015-1829-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-015-1829-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-015-1829-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Özgür Arslan & Mehmet Baha Karan, 2010. "Consumer Credit Risk Characteristics: Understanding Income and Expense Differentials," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 46(2), pages 20-37, March.
    2. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
    3. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    4. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    5. Ligang Zhou & Kin Keung Lai & Jerome Yen, 2009. "Credit Scoring Models With Auc Maximization Based On Weighted Svm," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 677-696.
    6. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    7. Falbo, P, 1991. "Credit-scoring by enlarged discriminant models," Omega, Elsevier, vol. 19(4), pages 275-289.
    8. Akkoç, Soner, 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 cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    9. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
    10. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    11. Zehra Taşkın & Umut Al, 2014. "Standardization problem of author affiliations in citation indexes," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 347-368, January.
    12. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    13. Weishu Liu & Mengdi Gu & Guangyuan Hu & Chao Li & Huchang Liao & Li Tang & Philip Shapira, 2014. "Profile of developments in biomass-based bioenergy research: a 20-year perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 507-521, May.
    14. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    15. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    16. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
    17. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    18. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    19. Cláudia Frias Pinto & Fernando Ribeiro Serra & Manuel Portugal Ferreira, 2014. "A bibliometric study on culture research in International Business," Working Papers 107, globADVANTAGE, Polytechnic Institute of Leiria.
    20. Saeed-Ul Hassan & Peter Haddawy & Jia Zhu, 2014. "A bibliometric study of the world’s research activity in sustainable development and its sub-areas using scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 549-579, May.
    21. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    22. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    23. 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, Elsevier, vol. 90(3), pages 487-513, May.
    24. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    25. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    26. Antonio‐Rafael Ramos‐Rodríguez & José Ruíz‐Navarro, 2004. "Changes in the intellectual structure of strategic management research: a bibliometric study of the Strategic Management Journal, 1980–2000," Strategic Management Journal, Wiley Blackwell, vol. 25(10), pages 981-1004, October.
    27. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 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, Elsevier, vol. 195(3), pages 942-959, June.
    28. Ken Hung & Hui Wen Cheng & Shih-shen Chen & Ying-Chen Huang, 2013. "Factors that Affect Credit Rating: An Application of Ordered Probit Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 94-108, December.
    29. Guifeng Liu, 2013. "Visualization of patents and papers in terahertz technology: a comparative study," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1037-1056, March.
    30. Lo, Andrew W., 1986. "Logit versus discriminant analysis : A specification test and application to corporate bankruptcies," Journal of Econometrics, Elsevier, vol. 31(2), pages 151-178, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel Fonseca Costa & Francisval Carvalho & Bruno César Moreira & José Willer Prado, 2017. "Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1775-1799, June.
    2. Gisleine Carmo & Luiz Flávio Felizardo & Valderí Castro Alcântara & Cristiane Aparecida Silva & José Willer Prado, 2023. "The impact of Jürgen Habermas’s scientific production: a scientometric review," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1853-1875, March.
    3. Fernando Zambrano Farias & María del Carmen Valls Martínez & Pedro Antonio Martín-Cervantes, 2021. "Explanatory Factors of Business Failure: Literature Review and Global Trends," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    4. Germ`a Coenders & N'uria Arimany Serrat, 2023. "Accounting statement analysis at industry level. A gentle introduction to the compositional approach," Papers 2305.16842, arXiv.org, revised Feb 2024.
    5. Néstor Montalván-Burbano & Andrés Velastegui-Montoya & Miguel Gurumendi-Noriega & Fernando Morante-Carballo & Marcos Adami, 2021. "Worldwide Research on Land Use and Land Cover in the Amazon Region," Sustainability, MDPI, vol. 13(11), pages 1-24, May.
    6. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    7. Ren, Yi-Shuai & Ma, Chao-Qun & Chen, Xun-Qi & Lei, Yu-Tian & Wang, Yi-Ran, 2023. "Sustainable finance and blockchain: A systematic review and research agenda," Research in International Business and Finance, Elsevier, vol. 64(C).
    8. Jingjing Long & Cuiqing Jiang & Stanko Dimitrov & Zhao Wang, 2022. "Clues from networks: quantifying relational risk for credit risk evaluation of SMEs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-41, December.
    9. Roberto Pico-Saltos & Paúl Carrión-Mero & Néstor Montalván-Burbano & Javier Garzás & Andrés Redchuk, 2021. "Research Trends in Career Success: A Bibliometric Review," Sustainability, MDPI, vol. 13(9), pages 1-24, April.
    10. Saulo Cardoso Maia & Gideon Carvalho Benedicto & José Willer Prado & David Alastair Robb & Oscar Neto Almeida Bispo & Mozar José Brito, 2019. "Mapping the literature on credit unions: a bibliometric investigation grounded in Scopus and Web of Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 929-960, September.
    11. Schwab, Leila & Gold, Stefan & Reiner, Gerald, 2019. "Exploring financial sustainability of SMEs during periods of production growth: A simulation study," International Journal of Production Economics, Elsevier, vol. 212(C), pages 8-18.
    12. Noluthando Mngadi & Hossana Twinomurinzi, 2023. "Quantifying Causality between Climate Change and Credit Risk: A Bibliometric Study and Research Agenda," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    13. Daniel Fonseca Costa & Brenda Melissa Fonseca & Lélis Pedro Andrade & Bruno César Melo Moreira, 2023. "Bibliometric and scientometric analysis of the scientific field in taxation," SN Business & Economics, Springer, vol. 3(1), pages 1-28, January.
    14. Yan Yan & Zhewen Liao & Xiaosong Chen, 2018. "Fixed-income securities: bibliometric review with network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1615-1640, September.
    15. Khan, Ashraf & Goodell, John W. & Hassan, M. Kabir & Paltrinieri, Andrea, 2022. "A bibliometric review of finance bibliometric papers," Finance Research Letters, Elsevier, vol. 47(PA).
    16. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    17. P. V. Thayyib & Rajesh Mamilla & Mohsin Khan & Humaira Fatima & Mohd Asim & Imran Anwar & M. K. Shamsudheen & Mohd Asif Khan, 2023. "State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary," Sustainability, MDPI, vol. 15(5), pages 1-38, February.
    18. Adnan Ali & Suresh Ramakrishnan & Faisal Faisal & Tooba Akram & Sidra Salam & Sami Ur Rahman, 2023. "Bibliometric analysis of finance and natural resources: past trend, current development, and future prospects," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 13035-13064, November.
    19. Yong Qin & Zeshui Xu & Xinxin Wang & Marinko Škare, 2021. "Are family firms in the eyes of economic policy?," International Entrepreneurship and Management Journal, Springer, vol. 17(3), pages 1233-1259, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    2. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    3. Fernando Zambrano Farias & María del Carmen Valls Martínez & Pedro Antonio Martín-Cervantes, 2021. "Explanatory Factors of Business Failure: Literature Review and Global Trends," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    4. 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.
    5. 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.
    6. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    13. 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.
    14. Thomas E. Mckee, 2000. "Developing a bankruptcy prediction model via rough sets theory," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(3), pages 159-173, September.
    15. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    16. 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.
    17. 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.
    18. Barniv, Ran & Mehrez, Abraham & Kline, Douglas M., 2000. "Confidence intervals for controlling the probability of bankruptcy," Omega, Elsevier, vol. 28(5), pages 555-565, October.
    19. 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.
    20. Akkoç, Soner, 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 cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:106:y:2016:i:3:d:10.1007_s11192-015-1829-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.