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Feedforward neural networks in the classification of financial information

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  • Carlos Serrano-Cinca

Abstract

Financial research has given rise to numerous studies in which, on the basis of the information provided by financial statements, companies are classified into different groups. An example is that of the classification of companies into those that are solvent and those that are insolvent. Linear discriminant analysis (LDA) and logistic regression have been the most commonly used statistical models in this type of work. One feedforward neural network, known as the multilayer perceptron (MLP), performs the same task as LDA and logistic regression which, a priori, makes it appropriate for the treatment of financial information. In this paper, a practical case based on data from Spanish companies, shows, in an empirical form, the strengths and weaknesses of feedforward neural networks. The desirability of carrying out an exploratory data analysis of the financial ratios in order to study their statistical properties, with the aim of achieving an appropriate model selection, is made clear.

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Bibliographic Info

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

Volume (Year): 3 (1997)
Issue (Month): 3 ()
Pages: 183-202

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Handle: RePEc:taf:eurjfi:v:3:y:1997:i:3:p:183-202

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Related research

Keywords: Neural Networks Multilayer Perceptron Bankruptcy Prediction Spanish Banking System;

References

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  1. 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.
  2. 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.
  3. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. " Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-91, March.
  4. 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.
  5. Molinero, C Mar & Ezzamel, M, 1991. "Multidimensional scaling applied to corporate failure," Omega, Elsevier, vol. 19(4), pages 259-274.
  6. Srinivasan, Venkat & Kim, Yong H, 1987. " Credit Granting: A Comparative Analysis of Classification Procedures," Journal of Finance, American Finance Association, vol. 42(3), pages 665-81, July.
  7. 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.
  8. K. Feldman & J. Kingdon, 1995. "Neural networks and some applications to finance," Applied Mathematical Finance, Taylor & Francis Journals, vol. 2(1), pages 17-42.
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Cited by:
  1. Alessandra Amendola & Marialuisa Restaino & Luca Sensini, 2010. "Variable Selection In Forecasting Models For Corporate Bankruptcy," Working Papers 3_216, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
  2. 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.
  3. Korol, Tomasz, 2013. "Early warning models against bankruptcy risk for Central European and Latin American enterprises," Economic Modelling, Elsevier, vol. 31(C), pages 22-30.
  4. Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Society for Computational Economics, vol. 28(1), pages 71-88, August.
  5. du Jardin, Philippe, 2008. "Bankruptcy prediction and neural networks: The contribution of variable selection methods," MPRA Paper 44384, University Library of Munich, Germany.

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