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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.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

Volume (Year): 7 (2001)
Issue (Month): 2 ()
Pages: 165-183

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Handle: RePEc:taf:eurjfi:v:7:y:2001:i:2:p:165-183

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Related research

Keywords: Bankruptcy Prediction Financial Ratios Multidimensional Scaling Box And Whiskers Diagrams Spanish Banking System;

References

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  1. Davidson, Russell & MacKinnon, James G., 1984. "Convenient specification tests for logit and probit models," Journal of Econometrics, Elsevier, vol. 25(3), pages 241-262, July.
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  3. Eisenbeis, Robert A, 1977. "Pitfalls in the Application of Discriminant Analysis in Business, Finance, and Economics," Journal of Finance, American Finance Association, vol. 32(3), pages 875-900, June.
  4. 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.
  5. Haggstrom, Gus W, 1983. "Logistic Regression and Discriminant Analysis by Ordinary Least Squares," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(3), pages 229-38, July.
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  8. James Lingoes, 1971. "Some boundary conditions for a monotone analysis of symmetric matrices," Psychometrika, Springer, vol. 36(2), pages 195-203, June.
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Citations

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Cited by:
  1. 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.
  2. S. Balcaen & H. Ooghe, 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/249, Ghent University, Faculty of Economics and Business Administration.
  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. 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.

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