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Bank failure: a multidimensional scaling approach

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  • Cecilio Mar-Molinero
  • Carlos Serrano-Cinca

Abstract

Mathematical models for the prediction of company failure are by now well established. Most of the work on multivariate modelling of distress prediction attempts to obtain a score that gives the failure probability of a company. A data set of 66 Spanish banks, 29 of which failed, is used to show that multidimensional scaling (MDS) techniques can be of use to produce simple tools for the analysis of financial health. MDS has the advantage of producing pictorial representations that are easy to interpret and use. This is done without loss of statistical rigour given the very close links between MDS and other multivariate statistical techniques that are normally used in the analysis of failure. As an example, the technique is used to trace the financial path of an ailing bank.

Suggested Citation

  • Cecilio Mar-Molinero & Carlos Serrano-Cinca, 2001. "Bank failure: a multidimensional scaling approach," The European Journal of Finance, Taylor & Francis Journals, vol. 7(2), pages 165-183.
    • Handle: RePEc:taf:eurjfi:v:7:y:2001:i:2:p:165-183
    DOI: 10.1080/13518470122202
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    Cited by:

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    2. Mar Molinero, C. & Apellaniz Gomez, P. & Serrano Cinca, C., 1996. "A multivariate study of spanish bond ratings," Omega, Elsevier, vol. 24(4), pages 451-462, August.
    3. Ali Emrouznejad & Emilyn Cabanda, 2010. "An aggregate measure of financial ratios using a multiplicative DEA model," International Journal of Financial Services Management, Inderscience Enterprises Ltd, vol. 4(2), pages 114-126.
    4. M. José Charlo, 2010. "The Most Relevant Variables To Support Risk Analysts For Loan Decisions: An Empirical Study," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 10(1).
    5. Henrik Andersen, 2008. "Failure prediction of Norwegian banks: A Logit approach," Working Paper 2008/02, Norges Bank.
    6. Carlos Serrano-Cinca, 1997. "Feedforward neural networks in the classification of financial information," The European Journal of Finance, Taylor & Francis Journals, vol. 3(3), pages 183-202.
    7. Róbert Štefko & Jarmila Horváthová & Martina Mokrišová, 2021. "The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy," JRFM, MDPI, vol. 14(5), pages 1-19, May.
    8. Yu-Chiang Hu & Jake Ansell, 2009. "Retail default prediction by using sequential minimal optimization technique," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(8), pages 651-666.
    9. Serrano Cinca, C. & Mar Molinero, C. & Gallizo Larraz, J.L., 2005. "Country and size effects in financial ratios: A European perspective," Global Finance Journal, Elsevier, vol. 16(1), pages 26-47, August.
    10. 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.
    11. Layla Khoja & Maxwell Chipulu & Ranadeva Jayasekera, 2016. "Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling," Review of Quantitative Finance and Accounting, Springer, vol. 46(3), pages 483-518, April.

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