Variables y modelos para la identificación y predicción del fracaso empresarialrevisión de la investigación empírica reciente

  1. Tascón Fernández, María Teresa
  2. Castaño Gutiérrez, Francisco Javier
Revista:
Revista de contabilidad = Spanish accounting review: [RC-SAR]

ISSN: 1138-4891

Año de publicación: 2012

Volumen: 15

Número: 1

Páginas: 7-58

Tipo: Artículo

DOI: 10.1016/S1138-4891(12)70037-7 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Revista de contabilidad = Spanish accounting review: [RC-SAR]

Referencias bibliográficas

  • Abad, C., Arquero, J.L. y Jiménez, S.M. (2004). Procesos de fracaso empresarial. Identificación y contrastación empírica. XI Encuentro de Profesores Universitarios de Contabilidad.
  • Acosta, E. y Fernández, F. (2007). Predicción del fracaso empresarial mediante el uso de algoritmos genéticos. X Encuentro de Economía Aplicada, Logroño, 14-15-16 de junio http://www.unirioja. es/dptos/dee"DEPARTAMENTO DE ECONOMÍA Y EMPRESA.
  • Ahn, B.S., Cho, S.S. y Kim, C.Y. (2000). The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Business Failure Prediction. Expert Systems with Applications, 18, pp. 65-74.
  • Altman, E.I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), pp. 568-609.
  • Altman, E.I. (1981). Financial Handbook. New York: John Wiley & Sons.
  • Altman, E.I. (1983). Corporate Financial Distress. Chichester: John Wiley & Sons.
  • Altman, E.I. (1993). Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting and Avoiding Distress and Profiting from Bankruptcy. New York: John Wiley & Sons.
  • Altman, E.I., Haldeman, R. y Narayanan, P. (1977). Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporations. Journal of Banking and Finance, 1(1), June, pp. 29-54.
  • Altman, E.I., Marco, G. y Varetto, F. (1994). Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (The Italian Experience). Journal of Banking and Finance, 18(3), pp. 505-529.
  • Altman, E.I. y Saunders, A. (1998). Credit Risk Measurement: Developments over the Last 20 Years. Journal of Banking and Finance, 21(11-12), December, pp. 1721-1742.
  • Altman, E.I. y Sabato, G. (2005). Effects of the New Basel Capital Accord on Bank Capital Requirements for SMEs. Journal of Financial Services Research, 28(1-3), pp. 15-42.
  • Altman, E.I. y Sabato, G. (2007). Modeling Credit Risk for SMEs: Evidence from the U.S. Market. Abacus, 43(3), pp. 332-357.
  • Altman, E.I., Sabato, G. y Wilson, N. (2008). The Value of Qualitative Information in SME Risk Management. Working Paper. Leonard N. Stern School of Business, New York University.
  • Argenti, J. (1976). Corporate Collapse: The Causes and Symptoms. New York: John Wiley & Sons.
  • Arquero, J.L., Abad, M.C. y Jiménez, S.M. (2008). Procesos de fracaso empresarial en PYMES, Identificación y contrastación empírica. Revista Internacional de la Pequeña y Mediana Empresa, 1(2), pp. 64-77.
  • Atiya, A.F. (2001). Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results. IEEE Transactions on Neural Networks, 12(4), pp. 929-935.
  • Balcaen, S. y Ooghe, H. (2006). 35 Years of Studies on Business Failure: An Overview of the Classic Statistical Methodologies and their Related Problems, British Accounting Review, 38(1), pp. 63-93.
  • Barniv, R., Anurag, A. y Leach, R. (1997). Predicting the Out Come Following Bankruptcy Filing: A Three State Classification Using NN, International Journal of Intelligent Systems in Accounting, Finance and Management, 6, pp. 177-194.
  • Beaver, W.H. (1966). Financial Ratios as Predictors of Failure, Journal of Accounting Research, Supplement, 4, January, pp. 71-111.
  • Beaver, W.H. (1968). Alternative accounting measures and predictors of failure. The Accounting Review, January, pp. 113-122.
  • Beaver, W.H., Correia, M. y McNichols, M. (2009). Have Changes in Financial Reporting Attributes Impaired Informativeness? Evidence from the Ability of Financial Ratios to Predict Bankruptcy. Rock Center for Corporate Governance Working Paper No. 13, Stanford University, December. Available at SSRN: http://ssrn.com/abstract=1341305.
  • Beaver, W.H., McNichols, M. y Rhie, J. (2005). Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy. Review of Accounting Studies, 10(1), pp. 93-122.
  • Bechetti, L. y Sierra, J. (2003). Bankruptcy risk and productive efficiency in manufacturing firms. Journal of Banking and Finance, 27(11), pp. 2099-2120.
  • Bell, T.B. (1997). Neural Nets or the Logit Model? A Comparison of Each Model's Ability to Predict Commercial Bank Failures. International Journal of Intelligent Systems in Accounting, Finance and Management, 6, pp. 249-264.
  • Bell, T.B., Ribar, G.S. y Verchio, J. (1990). Neural Nets Versus Logistic Regression: A Comparison of Each Model's Ability to Predict Commercial Bank Failures. En Srivastava, R.P. (ed) Auditing Symposium on Auditing Problems, pp. 29-53.
  • Bellovary, J.L, Giacomino, D.E. y Akers, M.D. (2007). A Review of Bankruptcy Prediction Studies: 1930 to Present. Journal of Financial Education, 33(Winter), pp. 1-43.
  • Bhargava, M., Dubelaar, C. y Scott, T. (1998). Predicting bankruptcy in the retail sector: an examination of the validity of key measures of performance. Journal of Retailing and Consumer Services, 5(2), pp. 105-117.
  • Blum, M. (1974). Failing Company Discriminant Analysis. Journal of Accounting Research, 12(1), Spring, pp. 1-25.
  • Bonsón Ponte, E., Escobar Rodríguez, T., Martín Zamora, M.P. (1997a). Decision Tree Induction Systems. Applications in Accounting and Finance. En E. Bonsón Ponte y G. Sierra Molina (ed): Intelligent Technologies in Accounting and Business. Proceedings of the III International Meeting on Artificial Intelligence in Accounting, Finance and Tax. Huelva.
  • Bonsón Ponte, E., Escobar Rodríguez, T. y Martín Zamora, M.P. (1997b). Sistemas de inducción de árboles de decisión: Utilidad en el análisis de crisis bancarias. Ciberconta. Revista electrónica de Contabilidad. Universidad de Zaragoza, Departamento de Contabilidad y Finanzas. (Disponible en http://ciberconta. unizar.es/Biblioteca/0007/árboles.html).
  • Calvo-Flores, A., García, D. y Madrid, A. (2006). Tamaño, Antigüedad y Fracaso Empresarial. Working Paper. Universidad Politécnica de Cartagena.
  • Canbas, S., Cabuk, A. y Kilic, S.B. (2005). Prediction of Commercial Bank Failure via Multivariate Statistical Analysis of Financial Structure: The Turkish Case, European Journal of Operational Research, 166, pp.528-546.
  • Casey, C. y Bartczak, N. (1985). Using Operating Cash Flow Data to Predict Financial Distress- Some Extensions. Journal of Accounting Research, 23(1), pp. 384-401.
  • Cielen, A., Peeters, L., Vanhoof, K. (2004). Bankruptcy Prediction Using a Data Envelopment Analysis. European Journal of Operational Research, 154(2), April, pp. 526-532.
  • Collins, R.A. y Green, R.D. (1982). Statistical Methods for Bankruptcy Forecasting. Journal of Economics and Business, 34(4), pp. 349-354.
  • Correa, A., Acosta, M. y González, A.L. (2003). La insolvencia empresarial: un análisis empírico para la pequeña y mediana empresa. Revista de Contabilidad, 6(12), pp. 47-79.
  • Crespo Domínguez, M.A. (2000). Una aproximación a la predicción del fracaso empresarial mediante redes neuronales. IX Encuentro de Profesores Universitarios de Contabilidad, Las Palmas de Gran Canaria, pp. 591-607.
  • Dambolena, I.G. y Khoury, S.J. (1980). Ratio Stability and Corporate Failure. Journal of Finance, 35(4), September, pp. 1017-1026.
  • Daubie, M. y Meskens, N. (2002). Business Failure Prediction: A Review and Analysis of the Literature, en Zopounidis, C. (Ed.) New Trends in Banking Management, Physica-Verlag, pp. 71-86.
  • Davydenko, S.A. (2007). When do firms default? A study of the default boundary. AFA 2009 San Francisco Meetings Paper; EFA 2005 Moscow Meetings Paper; WFA 2006 Keystone Meetings Paper, August. Available at SSRN: http://ssrn.com/abstract=672343.
  • De Andrés Suárez, J. (2000). Técnicas de Inteligencia Artificial aplicadas al análisis de la solvencia empresarial. Documento de Trabajo núm. 206, Universidad de Oviedo, Facultad de Ciencias Económicas.
  • De Andrés Suárez, J. (2001). Statistical Techniques vs. SEE5 Algorithm. An Application to a Small Business Environment. The International Journal of Digital Accounting Research, 1(2), July, pp. 153-179.
  • De Andrés Sánchez, J. (2005). Comparativa de métodos de predicción de la quiebra: Redes neuronales artificiales vs. métodos estadísticos multivariantes. Partida Doble, 168, julioagosto, pp. 105-113.
  • De la Torre, J.M., Gómez, M.E. y Román, I. (2005). Análisis de sensibilidad temporal de los modelos de predicción de solvencia: una aplicación a las pymes industriales. XIII Congreso AECA, Armonización y gobierno de la diversidad, 22 a 24 de septiembre, Oviedo (recurso electrónico).
  • De Miguel, L.J., Revilla, E., Rodríguez, J.M. y Cano, J.M. (1993). A Comparison between Statistical and Neural Network Based Methods for Predicting Bank Failures. Proceedings of the IIIth International Workshop on Artificial Intelligence in Economics and Management, Portland (USA).
  • Deakin, E.B. (1972). A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, 10(1), Spring, pp. 167-179.
  • Deakin, E.B. (1976). Distributions of Financial Accounting Ratios: Some Empirical Evidence. The Accounting Review, 51(1), January, pp. 90-96.
  • Del Rey Martínez, E. (1996). Bankruptcy Prediction in Non-Finance Companies: An Application Based on Artificial Neural Network Models. En Sierra Molina, G. y Bonsón Ponte, E. (Eds.): Intelligent Systems in Accounting and Finance, Huelva, pp. 253-272.
  • Dewaelheyns, N. y Van Hulle, C. (2004). The Impact of Business Groups on Bankruptcy Prediction Modeling. Tijdschrift voor Economie en Management, 49(4), pp. 623-645.
  • Dewaelheyns, N. y Van Hulle, C. (2006). Corporate Failure Prediction Modeling: Distorted by Business Groups' Internal Capital Markets?. Journal of Business Finance & Accounting, 33(5-6), pp. 909-931.
  • Dimitras, A., Zanakis S. y Zopounidis C. (1996). A survey of Business Failures with an Emphasis on Failure Prediction Methods and Industrial Applications. European Journal of Operational Research, 90(3), pp. 487-513.
  • Dutta, S. y Shekhar, S. (1992). Bond rating: a non conservative application of neural networks. En Neural Networks in Finance and Investing. Chicago: Probus Publishing, pp. 443-450.
  • Edmister, R.O. (1972). An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction. Journal of Financial and Quantitative Analysis, 7(2), March, pp. 1477-1493.
  • Edmister, R.O. (1988). Combining Human Credit Analysis and Numerical Credit Scoring for Business Failure Prediction. Akron Business and Economic Review, 19(3), pp. 6-14.
  • Elam, R. (1975). The Effect of Lease Data on the Predictive Ability of Financial Ratios. The Accounting Review, 50(1), January, pp. 25-43.
  • Fernández, E. y Olmeda, I. (1995). Bankruptcy Prediction with Artificial Neural Networks. Lecture Notes on Computational Sciences, 930, pp. 1142-1146.
  • Ferrando, M. y Blanco, F. (1998). La previsión del fracaso empresarial en la comunidad valenciana: aplicación de los modelos discriminante y logit. Revista Española de Financiación y Contabilidad, 27(95), abril-junio, pp. 499-540.
  • Fletcher, D. y Goss, E. (1993). Application Forecasting with Neural Networks: An Application Using Bankruptcy Data. Information and Management, 24, pp. 159-167.
  • Frydman, H., Altman, E.I. y Kao, D. (1985). Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress. The Journal of Finance, 40(1), March, pp. 269-291.
  • Gabás Trigo, F. (1990). Técnicas actuales de análisis contable, evaluación de la solvencia empresarial. Madrid: Instituto de Contabilidad y Auditoría de Cuentas. Ministerio de Economía y Hacienda.
  • Gallego, A. M., Gómez, J. C. y Yáñez, L. (1997). Modelos de predicción de quiebras en empresas no financieras. Actualidad Financiera, 2(5), mayo, pp. 3-14.
  • Gandía, J.L., García, J.L. y Molina, R. (1995). Estudio Empírico de la Solvencia Empresarial en la Comunidad Valenciana. Valencia: Instituto Valenciano de Investigaciones Económicas, Junio.
  • García, D., Arqués, A. y Calvo-Flores, A. (1995). Un modelo discriminante para evaluar el riesgo bancario en los créditos a empresas. Revista Española de Financiación y Contabilidad, 24(82), enero-marzo, pp. 175-200.
  • García-Ayuso, M. (1995). La necesidad de llevar a cabo un replanteamiento de la investigación en materia de análisis de la información financiera. Análisis financiero, 66, pp. 36-61.
  • Gazengel, A. y Thomas, P. (1992). Les défaillances d'entreprises. Les Cahiers de Recherche, 105, 47 p., École Superieure de Commerce de Paris.
  • Gentry, J., Newbold, P. y Whitford, D. (1985). Classifying Bankrupt Firms with Funds Flow Components. Journal of Accounting Research, 23(1), Spring, pp. 146-159.
  • Gilbert, L.R., Menon, K. y Schwartx, K.B. (1990). Predicting Bankruptcy for Firms in Financial Distress. Journal of Business, Finance and Accounting, 17(1), pp. 161-171.
  • Gombola, M.J. y Ketz, J.E. (1983). A Note on Cash Flow and Classification Patterns of Financial Ratios. Accounting Research, 58(1), January, pp. 105-114.
  • Gómez, M.A., Torre, J.M., y Román, I. (2008). Análisis de sensibilidad temporal en los modelos de predicción de insolvencia: una aplicación a las PYMES industriales. Revista Española de Financiación y Contabilidad, 37(137), enero-marzo, pp. 85-111.
  • Graveline, J. y Kokalari, M. (2008). Credit risk. Working Paper, The Research Foundation of CFA Institute, November.
  • Greenstein, M.M. y Welsh, M.J. (1996). Bankruptcy prediction using ex-ante neural networks and reallistically proportioned testing sets. En Sierra Molina, G. y Bonsón Ponte, E. (Eds.): Intelligent Systems in Accounting and Finance, Huelva, pp. 187-212.
  • Grice, J.S. e Ingram, R.W. (2001). Tests of the Generalizability of Altman's Bankruptcy Prediction Model. Journal of Business Research, 54(1), pp. 53-61.
  • Grunert, J., Norden, L. y Weber, M. (2005). The Role of Non-Financial Factors in Internal Credit Ratings. Journal of Banking and Finance, 29(2), pp. 509-531.
  • Hair, J.F., Anderson, R.E., Tatham, R.L. y Black, W.C. (1999). Análisis multivariante, Madrid: Prentice-Hall.
  • Hayden, E. (2003). Are Credit Scoring Models Sensitive with Respect to Default Definitions? Evidence from the Australian Market, Dissertation Paper, Department of Business Administration, Univesity of Vienna, Austria, pp.1-43.
  • Hill, N.T., Perry, S.E. y Andes, S. (1996). Evaluating Firms in Financial Distress: An Event History Analysis. Journal of Applied Business Research, 13(13), pp. 60-71.
  • Hillegeist, S.A., Keating, E.K., Cram, D.P. y Lundstedt, K.G. (2004). Assessing the Probability of Bankruptcy. Review of Accounting Studies, 9(1), pp. 5-34.
  • Holder, M. (1984). Le score de l'enterprise. París: Nouvelles Editions Fiduciaires.
  • Jacobson, T., Kindell, R., Lindé, J. y Roszbach, K. (2008). Firm Default and Aggregate Fluctuations. Working Paper, Sveriges Riskbank, no. 226, September. Available at SSRN: http://ssrn.com/abstract=1471254.
  • Jones, F.L. (1987). Current Techniques in Bankruptcy Prediction. Journal Accounting Literature, 6, pp. 131-164.
  • Jones, S. and Hensher, D.A. (2004). Predicting Firm Financial Distress: A Mixed Logit Model. The Accounting Review, 79(4), pp. 1011-1038.
  • Jones, S. and Hensher, D.A. (2008). Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction. Cambridge University Press.
  • Kaski, S., Sinkkonen, J. y Peltonen, J. (2001). Bankruptcy Analysis with Self-Organizing Maps in Learning Metrics. IEEE Transactions on Neural Networks, 12 (4), pp. 936-947.
  • Keasey, K. y Watson, R. (1987). Non-financial symptoms and the prediction of small company failure: a test of Argenti's hypothesis. Journal of Business, Finance and Accounting, 14(3), Autumn, pp. 335-354.
  • Keasey, K. y Watson, R. (1988). The non-submission of accounts and small company financial failure prediction. Accounting and Business Research, 19(73), Winter, pp. 47-54.
  • Keasey, K. y Watson, R. (1991). Financial Distress Prediction Models: A Review of their Usefulness, British Journal of Management, 2(2), July, pp. 89-102.
  • Ketz, J.E. (1978). The Effect of General Price-Level Adjustments on the Predictability of Financial Ratios. Journal of Accounting Research, 16 supplement, pp. 273-284.
  • Kiviluoto, K. (1998) Predicting Bankruptcies with Self Organizing Map. Neurocomputing, 21 pp. 191-201.
  • Koh, H.C. (1991). Model Predictions and Auditor Assessment of Going Concern Status. Accounting and Business Research, 21(84), pp. 331-338.
  • Koh, H.C. y Tan, S.S. (1999). A neural network approach to the prediction of going concern status. Accounting and Business Research, 29(3), pp. 211-216.
  • Kuo, Y.C. (2007). The Data Envelopment Models for the Application of Two-Group Discrimininant Analysis. Tesis Doctoral.
  • Labatut, G.; Pozuelo, J. y Veres, E.J. (2009). Modelización temporal de los ratios contables en la detección del fracaso empresarial de la PYME española. Revista Española de Financiación y Contabilidad, 38(143), julio-septiembre, pp. 423-448.
  • Lacher, R.C., Coats, P.K., Sharma, S.C., y Fant, L.F. (1995). A Neural Network for Classifying The Financial Health of a Firm. European Journal of Operational Research, 85(1), pp. 53-65.
  • Laffarga, J., Martín, J.L. y Vázquez, M.J. (1985). El análisis de la solvencia de las instituciones bancarias: Propuesta de una metodología y aplicaciones a la Banca española. Esic-Market, 48 (2° trim.), pp. 51-73.
  • Laffarga J., Martín, J.L. y Vázquez, M.J. (1986a). El pronóstico a corto plazo del fracaso en las instituciones bancarias: metodología y aplicaciones a la Banca española. Esic-Market, 53, (3° trim.), pp. 59-116.
  • Laffarga J., Martín, J.L. y Vázquez, M.J. (1986b). El pronóstico a largo plazo del fracaso en las instituciones bancarias: metodología y aplicaciones al caso español. Esic-Market, 54, (4° trim.), pp. 113-167.
  • Laffarga J., Martín, J.L. y Vázquez, M.J. (1987). Predicción de la crisis bancaria española: La comparación entre el análisis logit y el análisis discriminante. Cuadernos de Investigación Contable, 1(1), otoño, pp. 103-110.
  • Laffarga J., Martín, J.L. y Vázquez, M.J. (1991). La predicción de la quiebra bancaria: el caso español. Revista Española de Financiación y Contabilidad, 20(66), enero-marzo, pp. 151- 163.
  • Laitinen, E.K. (1993). Financial Predictors for Different Phases of the Failure Process. Omega International Journal of Management Science, 21(2), pp. 215-228.
  • Laitinen, T. y Kankaanpää, M. (1999). Comparative Analysis of Failure Prediction Methods: the Finnish Case. The European Accounting Review, 8(1), pp.67-92.
  • Lee, K., Booth, D. y Alam, P. (2005). A Comparison of Supervised and Unsupervised Neural Networks in Predicting Bankruptcy of Korean Firms. Expert Systems with Applications, 29, pp. 1-16.
  • Lee, S.H. y Urrutia, J.L. (1996). Analysis and Prediction of Insolvency in the Property-Liability Insurance Industry: A Comparison of Logit and Hazard Models. The Journal of Risk and Insurance, 63(1), pp. 121-130.
  • Lennox, C. (1999). Identifying Failing Companies: A Re-evaluation of the Logit, Probit and DA Approaches. Journal of Economics and Business, 51(4), July, pp. 347-364.
  • Leshno, M. y Spector, Y. (1996) Neural Network Prediction Analysis: The Bankruptcy Case, Neurocomputing, 10, pp. 125-147.
  • Libby, R. (1975). Accounting ratios and the prediction of failure: Some behavioural evidence. Journal of Accounting Research, 13(1), Spring, pp. 150-161.
  • Lincoln, M. (1984). An empirical study of the usefulness of accounting ratios to describe levels of insolvency risk. Journal of Banking and Finance, 8(2), pp. 321-340.
  • Liou, D.K. y Smith, M. (2006). Macroeconomic Variables in the Identification of Financial Distress, Working Paper, May, Available at SSRN: http://ssrn.com/abstract=900284.
  • Lizarraga Dallo, F. (1997). Utilidad de la información contable en el proceso de fracaso: análisis del sector industrial de la mediana empresa. Revista Española de Financiación y Contabilidad, 26(93), octubre-diciembre, pp. 871-915.
  • Lizarraga Dallo, F. (1998). Modelos de predicción del fracaso empresarial: ¿tFunciona entre nuestras empresas el modelo de Altman de 1968?. Revista de Contabilidad,1(1), enerojunio, pp. 137-164.
  • Lo, A.W. (1986). Logic Versus Discriminant Analysis. Journal of econometrics, 31(2), pp. 151- 178.
  • López, E. y Flórez, R. (1999). El análisis de solvencia empresarial utilizando redes neuronales autoasociativas: el modelo Koh-León. Proceedings of the VI International Meeting on Advances in Computational Management, Reus.
  • López, E. y Flórez, R. (2000). Aplicación de dos modelos de redes neuronales artificiales para el análisis económico-financiero empresarial. Revista Europea de Dirección y Economía de la Empresa, 9(2), pp. 139-164.
  • López, J., Gandía, J.L. y Molina, R. (1998). La suspensión de pagos en las pymes: una aproximación empírica. Revista Española de Financiación y Contabilidad, 27(94), eneromarzo, pp. 71-97.
  • López, D., Moreno, J. y Rodríguez, P. (1994). Modelos de predicción del fracaso empresarial. Aplicación a entidades de seguros en España. Esic-Market, 84, pp. 83-125.
  • Madrid, A. y García, D. (2006). Factores que explican el fracaso empresarial en la pyme. Gestión: Revista de Economía, 36, marzo-junio, pp. 5-9.
  • Mar Molinero, C. y Ezzamel, M. (1991). Multidimensional Scaling Applied to Corporate Failure. Omega, 19(4), pp. 259-274.
  • Mar, C. y Serrano, C. (2001). Bank Failure: A Multidimensional Scaling Approach. The European Journal of Finance, 7(2), pp. 165-183.
  • Marais, M., Patell, J. y Wolfson, M. (1984). The Experimental Design of Classification Models: An Application of Recursive Partitioning and Bootstrapping to Commercial Bank Loan Classifications. Journal of Accounting Research, 22(1), pp. 87-118.
  • Marose, R.A. (1992). A Financial Neural Network Application. En Neural Networks in Finance and Investing. Chicago: Probus Publishing, pp. 50-53.
  • Martin, D. (1977). Early Warning of Bank Failure. Journal of Banking and Finance, 1(3), pp. 249-276.
  • Martínez, I. (1996). Forecasting Company Failure: Neural Approach versus Discriminant Analysis: An Application to Spanish Insurance Companies. En Sierra Molina, G. y Bonsón Ponte, E. (Eds.): Intelligent Systems in Accounting and Finance, Huelva, pp. 169-185.
  • Martínez, C., Navarro, M.V. y Sanz, F. (1989). Selección y explotación de los sistemas de alarma y prevención de quiebra. Investigaciones Económicas, (supl.), 13(3), pp. 135-141.
  • McDonald, B.D. y Morris, M.H. (1984). The Statistical Validity of the Ratio Method in Financial Analysis: An Empirical Examination. Journal of Business, Finance and Accounting, 11(1), Spring, pp. 89-97.
  • McGahan, A.M. y Porter, M.E. (1997). How Much Does Industry Matter, Really?. Strategic Management Journal, 18, Summer, pp 15-30.
  • McGurr, P.T. y DeVaney, S.A. (1998). Predicting Business Failure of Retail Firms: An Analysis Using Mixed Industry Models. Journal of Business Research, 43(3), pp. 169-176.
  • McKee, T.E. (1990). Evaluation of Enterprise Continuity Status Via Neural Networks. Abstracts of the Thirteenth Annual Congress of the European Accounting Association, 72.
  • McKee, T.E. (2000). Developing a Bankruptcy Prediction Model via Rough Sets Theory. International Journal of Intelligent Systems in Accounting, Finance & Management, 9(3), September, pp. 159-173.
  • Mensah, Y.M. (1984). An Examination of the Stationary of Multivariate Bankruptcy Prediction Models: A Methodological Study. Journal of Accounting Research, 22(1), pp. 380-395.
  • Messier, W.F.Jr. y Hansen, J.V. (1988). Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data, Management Science, 34(12), pp. 1403- 1415.
  • Meyer, P.A. y Pifer, H.W. (1970). Predictions of Bank Failures. The Journal of Finance, 25(4), September, pp. 853-868.
  • Min, S.H., Lee, J. y Han, I. (2006). Hybrid Genetic Algorithms and Support Vector Machines for Bankruptcy Prediction. Expert Systems with Applications, 31, pp. 652-660.
  • Mora Enguídanos, A. (1994a). Limitaciones metodológicas de los trabajos empíricos sobre la predicción del fracaso empresarial. Revista Española de Financiación y Contabilidad, 24(80), pp. 709-732.
  • Mora Enguídanos, A. (1994b). Los modelos de predicción del fracaso empresarial: una aplicación empírica del logit. Revista Española de Financiación y Contabilidad, 24(78), pp. 203-233.
  • Norton, C.L. (1976). A Comparison of the Abilities of General Price Level and Conventional Financial Ratios to Predict Bankruptcy, Arizona State University.
  • Norton, C. y Smith, R. (1979). A Comparison of General Price Level and Historical Cost Financial Statements in the Prediction of Bankruptcy. The Accounting Review, 54(1), January, pp. 72-87.
  • Odom, M.D. y Sharda, R. (1992). A Neural Network Model for Bankruptcy Prediction. En R.R. Trippi and E. Turban Eds. Neural networks in Finance and Investing. Chicago: Probus Publishing, pp.163-168.
  • Ohlson, J.A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), pp. 109-131.
  • Palepu, K.G. (1986). Predicting Takeover Targets: A Methodological and Empirical Analysis. Journal of Accounting and Economics, 8(1), March, pp. 3-35.
  • Paradi, J.C., Asmild, M. y Simak, P.C. (2004). Using DEA and Worst Practice DEA in Credit Risk Evaluation. Journal of Productivity Analysis, 21(2), March, pp. 153-165.
  • Park, C.S. y Han, I. (2002). A Case-Based Reasoning with the Feature Weights Derived by Analytic Hierarchy Process for Bankruptcy Prediction. Expert Systems with Applications, 23(3), pp. 255-264.
  • Peel, M.J. y Peel, D.A. (1987). Some Further Empirical Evidence on Predicting Private Company Failure. Accounting and Business Research, 18(69), pp. 57-66.
  • Peel, M.J., Peel, D.A. y Pope, P.F. (1986). Predicting Corporate Failure. Some Results for the UK Corporate Sector. Omega, 14(1), pp. 5-12.
  • Pina Martínez, V. (1989). Estudio empírico de la crisis bancaria. Revista Española de Financiación y contabilidad, 28(58), enero-marzo, pp. 309-338.
  • Piramuthu, S., Ragavan, H. y Shaw, M.J. (1998). Using Feature Construction to Improve the Performance of Neural Networks. Management Science, 44 (3), pp. 416-430.
  • Platt, H.D. y Platt, M.B. (1991). A Note on the Use of Industry Relative Ratios in Bankruptcy Prediction. Journal of Banking and Finance, 15(6), pp. 1183-1194.
  • Platt, H.D. y Platt, M.B. (2002). Predicting Corporate Financial Distress: Reflections on Choice-Based Sample Bias. Journal of Economics and Finance, 26(2), pp. 184-199.
  • Platt, H.D., Platt, M.B. y Pedersen, J.G. (1994). Bankruptcy Discrimination with Real Variables. Journal of Business, Finance and Accounting, 21(4), June, pp. 491-510.
  • Premachandra, I.M., Bhabra, G.S. y Sueyoshi, T. (2009). DEA as a Tool for Bankruptcy Assessment: A Comparative Study with Logistic Regression Technique. European Journal of Operational Research, 193(2), pp. 412-424.
  • Ramírez Comeig, I. (1996). La utilidad del análisis multivariante para evaluar la solvencia de las pequeñas empresas. X Congreso Nacional de AEDEM, Granada, junio, Ponencias y Comunicaciones, pp. 463-473.
  • Ravi Kumar, P. y Ravi, V. (2007). Bankruptcy Prediction in Banks and Firms Via Statistical and Intelligent Techniques - A Review. European Journal of Operational Research, 180(1), pp. 1-28.
  • Rodríguez Acebes, M.C. (1990). La Predicción de las Crisis Empresariales. Modelos para el Sector de Seguros, Valladolid: Secretariado de Publicaciones, Universidad de Valladolid.
  • Rodríguez Fernández, J.M. (1986). Crisis en los bancos privados españoles: un modelo logit. Investigaciones Económicas, (supl.), pp. 59-64.
  • Rodríguez Fernández, J.M., (1987). Crisis en los bancos privados españoles: un modelo logit. II Jornadas de Economía Industrial, Madrid.
  • Rodríguez Fernández, J.M. (1989a). Análisis de las insolvencias bancarias en España: un modelo empírico. Moneda y Crédito, 189, pp. 187-227.
  • Rodríguez Fernández, J.M. (1989b). The Crisis in Spanish Private Banks: A Logit Analysis. Finance, 10(1), junio, pp. 69-88.
  • Rodríguez, M. y Díaz, F. (2005). La Teoría de los rough sets y la predicción del fracaso empresarial. Diseño de un modelo para las pymes. XIII Congreso AECA, Armonización y gobierno de la diversidad, 22 a 24 de septiembre, Oviedo (recurso electrónico).
  • Rodríguez López, M. (2001). Predicción del fracaso empresarial en compañías no financieras. Consideración de técnicas de análisis multivariante de corte paramétrico. Actualidad Financiera, 6(6), pp. 27-42.
  • Román, I., De La Torre, J.M., Castillo, P.A., y Merelo, J.J. (2002). Sectorial Bankruptcy Prediction Analysis Using Artificial Neural Networks. The Case of Spanish Companies. European Accounting Congress, Copenhagen.
  • Román, I., De la Torre, J.M. y Zafra, J.L. (2001). Análisis sectorial de la predicción del riesgo de insolvencia: un estudio empírico. XI Congreso AECA: Empresa, Euro y Nueva Economía, Madrid, 26-28 septiembre (recurso electrónico).
  • Rose, P.S., Andrews, W.T. y Giroux, G.A. (1982). Predicting Business Failure: A Macroeconomic Perspective. Journal of Accounting, Auditing and Finance, 6(1), Fall, pp. 20-31.
  • Rubio Misas, M. (2008). Análisis del fracaso empresarial en Andalucía. Especial referencia a la edad de la empresa. Cuadernos de CC.EE. Y EE., 54, pp. 35-56.
  • Rughupathi, W., Schkade, L. y Raju, B.S. (1993). A Neural Network to Bankruptcy Prediction. En Trippi, R. y Turban, E. (editors) Neural Network in Finance and Investing. Cambridge: Probus Publishing Company, pp. 159-176.
  • Rumelt, R.P. (1997). How Much Does Industry Matter? Strategic Management Journal, 12(3), pp. 167-185.
  • Santomero, A.M. y Vinso J.D. (1977). Estimating the Probability of Failure for Commercial Banks and the Banking System. Journal of Banking and Finance, 1(2), October, pp. 185- 205.
  • Sarle, W.S. (1994). Neural Networks and Statistical Models. Proceedings of the Nineteenth Annual SAS Users Group International Conference, April.
  • Scott, J. (1981). The Probability of Bankruptcy. Journal of Banking and Finance, 5, pp. 317- 344.
  • Serrano Cinca, C. (1994). Las redes neuronales artificiales en el análisis de la información contable. (Tesis doctoral). Zaragoza: Universidad de Zaragoza.
  • Serrano Cinca, C. (1996). Self Organizing Neural Networks for Financial Diagnosis. Decision Support Systems, 17(3), pp. 227-238.
  • Serrano Cinca, C. (1997). Feedforward Neural Networks in the Classification of Financial Information. European Journal of Finance, 3(3), septiembre, pp. 183-202.
  • Serrano, C. y Martín, B. (1993). Predicción de la crisis bancaria mediante el empleo de redes neuronales artificiales. Revista Española de Financiación y Contabilidad, 22(74), pp. 153- 176.
  • Shin, K.S. y Lee, Y.J. (2002). A Genetic Algorithm Application in Bankruptcy Prediction Modeling. Expert Systems with Applications, 23(3), pp. 321-328.
  • Shin, K.S., Shin, T.S. y Han, I. (1998). Intelligent Corporate Credit Rating System Using Bankruptcy Probability Matrix. Proceedings of the IV International Conference on Artificial Intelligence and Emerging Technologies in Accounting, Finance and Tax, Huelva.
  • Shrieves, R.E. y Stevens, D.L. (1979). Bankruptcy Avoidance as a Motive for Merger. Journal of Financial and Quantitative Analysis, 3, pp. 501-515.
  • Shumway, T. (2001). Forcasting Bankruptcy More Accurately: A Simple Hazard Model. Journal of business, 74(1), January, pp. 101-124.
  • Sinkey, J.F. (1975). A Multivariate Statistical Analysis of the Characteristics of Problem Banks. The Journal of Finance, 30(1), March, pp. 21-36.
  • Slowinski, R., y Zopounidis, C. (1995). Application of the Rough Set Approach to Evaluation of Bankruptcy Risk, International Journal of Intelligent Systems in Accounting Finance and Management, 4(1), pp. 27-41.
  • Somoza López, A. (2001). La consideración de factores cualitativos, macroeconómicos y sectoriales en los modelos de predicción de la solvencia empresarial. Papeles de Economía Española, 89/90, pp. 402-426.
  • Somoza López, A. (2002). Modelos de predicción de la insolvencia: la incorporación de otro tipo de variables. En Doldán, F. y Rodríguez, M. (Coord.) La gestión del riesgo de crédito, Madrid, AECA, pp. 139-173.
  • Stein, J.H. y Ziegler, W. (1984). The Prognosis and Surveillance of Risks from Commercial Credit Borrowers. Journal of Banking and Finance, 8(2), June, pp. 249-268.
  • Sueyoshi, T. y Goto, M. (2009a). Can R&D Expenditure Avoid Corporate Bankruptcy? Comparison Between Japanese Machinery and Electric Equipment Industries Using DEA-Discriminant Analysis. European Journal of Operational Research, 196(1), pp. 289- 311.
  • Sueyoshi, T. y Goto, M. (2009b). DEA-DA for Bankruptcy-Based Performance Assessment: Misclassification Analysis of Japanese Construction Industry. European Journal of Operational Research, 199(2), pp. 576-594.
  • Sueyoshi, T. y Goto, M. (2009c). Methodological Comparison between DEA (Data Envelopment Analysis) and DEA-DA (Discriminant Analysis) from the Perspective of Bankruptcy Assessment. European Journal of Operational Research, 199(2), pp. 561-575.
  • Swicegood, P. y Clark, J.A. (2001). Off-Site Monitoring for Predicting Bank under Performance: A Comparison of Neural Networks, Discriminant Analysis and Professional Human Judgment. International Journal of Intelligent Systems in Accounting, Finance and Management, 10, pp. 169-186.
  • Surkan, A.J. y Singleton, J.C. (1992). Neural Networks for Bond Rating Improved by Multiple Hidden Layers. En R.R. Trippi y E. Turban, Neural networks in Finance and Investing, Chicago: Probus Publishing.
  • Taffler, R.J. (1982). Forecasting Company Failure in the UK using Discriminant Analysis and Finance Ratio Data. Journal of the Royal Statistical Society, Series A, 145(3), pp. 342-358.
  • Taffler, R.J. (1983). The Assessment of Company Solvency and Performance Using a Statistical Model. Accounting and Business Research, 15(52), Autumn, pp. 295-307.
  • Tam, K.Y. (1991). Neural Network Models and the Prediction of Bank Bankruptcy, Omega, 19(5), pp. 429-445.
  • Tam, K.Y. y Kiang, M.Y. (1992). Managerial Applications of Neural Networks: The Case of Bank Failure Predictions. Management Science, 38(7), July, pp. 926-947.
  • Troutt, M.D., Rai, A. y Zhang, A. (1996). The Potential Use of DEA for Credit Applicant Acceptance Systems. Computers & Operations Research, 23(4), April, pp. 405-408.
  • Tsukuda, J. y Baba, S.I. (1994). Predicting Japanese Corporate Bankruptcy in Terms of Finance Data Using Neural Network. Computers and Industrial Engineering, 27(1-4), pp. 445-448.
  • Westgaard, S. y Van Der Wijst, N. (2001). Default Probabilities in a Corporate Bank Portfolio: A Logistic Model Approach. European Journal of Operational Research, 135(2), December, pp. 338-349.
  • Whalen, G. (1991). A Proportional Hazard Model of Bank Failure: An Examination of its Usefulness as an Early Warning Model Tool. Federal Reserve Bank of Cleveland Economic Review, 27(1), pp. 21-31.
  • Wheelock, D.C. y Wilson, P.W. (2000). Why do Banks Disappear? The Determinants of U.S. Bank Failures and Acquisitions. The Review of Economics and Statistics, 82(1), February, pp. 127-138.
  • Wilson, R.L. y Sharda, R. (1994). Bankruptcy Prediction Using Neural Networks. Decision Support Systems, 11, pp. 545-557.
  • Whittred, G.P. y Zimmer, I. (1984). Timeliness of Financial Reporting and Financial Distress, The Accounting Review, 59(2), April, pp. 297-295.
  • Wilcox, J.W. (1971). A Gambler's Ruin Prediction of Business Failure Using Accounting Data, Sloan Management Review, 12(3), September, pp. 1-10.
  • Wilcox, J.W. (1976). The Gambler's Ruin Approach to Business Risk, Sloan Management Review, 18 (autumn), pp. 33-46.
  • Wilson, R.L. y Sharda, R. (1994). Bankruptcy Prediction using Neural Networks. Decision Support Systems, 11(5), pp. 545-557.
  • Xu, M. y Zhang, C. (2009). Bankruptcy Prediction: The Case of Japanese Listed Companies. Review of Accounting Studies, 14(4), December, pp. 534-558.
  • Zavgren, C.V. (1983). The prediction of corporate failure: the state of the art. Journal of Accounting Literature, 2(1), pp. 1-38.
  • Zavgren, C.V. (1985). Assessing the Vulnerability of Failure of American Industrial Firms: A Logistic Analysis. Journal of Banking and Finance. 12(1), Spring, pp. 19-45.
  • Zavgren, C.V. (1988). The Association between Probabilities of Bankrupcy and Market Responses- A Test of Market Anticipation. Journal of Business, Finance and Accounting. 15(1), pp. 27-45.
  • Zhang, G.P., Hu, M.Y., Patuwo, B.E. e Indro, D.C. (1999). Artificial Neural Networks in Bankruptcy Prediction: General Framework and Cross-Validation Analysis. European Journal of Operational Research, 116(1), July, pp. 16-32.
  • Zmijewski, M. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22 (supplement), pp. 59-86.