Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente

Variables and Models for the Identification and Prediction of Business Failure: Revision of Recent Empirical Research Advances

  • Maria T. T. Tascon
  • Francisco Javier Castaño Gutierrez
Palabras clave: fracaso empresarial, quiebra, análisis de variables, ratios financieros

Resumen

Este trabajo analiza la evolución en el tiempo de los estudios sobre fracaso empresarial. Con carácter general, partimos de la revisión crítica realizada en la literatura previa, y aportamos un análisis de la evidencia empírica adicional, con especial atención a la obtenida durante la última década. Pero además, para subsanar algunas deficiencias detectadas en las revisiones anteriores, nos ocupamos de tres aspectos, que pueden considerarse la principal contribución de este trabajo: primero, analizamos la evolución en las últimas décadas del concepto de fracaso empresarial o fallido, detectando cierta evolución desde la identificación hacia la predicción; segundo, analizamos las variables empleadas en los modelos, aportando un estudio de los rasgos empresariales que se representan con las variables (frente al tradicional análisis de frecuencia de las propias variables individuales), siendo los resultados más acordes con los planteamientos y desarrollos teóricos clásicos sobre el fracaso empresarial; y, finalmente, destacamos los puntos fuertes y débiles de las metodologías que, por su reciente aparición, no habían sido analizadas o muy poco por revisiones anteriores: las técnicas de inteligencia artificial y el análisis envolvente de datos (DEA). Adicionalmente, integramos en la revisión el numeroso grupo de trabajos empíricos publicados en España sobre la cuestión, y que no aparecían en ninguna de las revisiones previas analizadas.

Descargas

La descarga de datos todavía no está disponible.

Biografía del autor/a

Maria T. T. Tascon

AssistantProfessor

Facultad de CC. Económicas y Empresariales
Campus de vegazana, s/n
Campus de vegazana, s/n
León
24071
SPAIN

Francisco Javier Castaño Gutierrez

 

Alto del Pozo s/n
Pobladura del Bernesga
León
24121
Spain

Citas

[Abad et al., 2004] C. Abad, J.L. Arquero, S.M. Jiménez. Procesos de fracaso empresarial. Identificación y contrastación empírica. XI Encuentro de Profesores Universitarios de Contabilidad, (2004),

[Acosta and Fernández, 2007] E. Acosta, F. Fernández. Predicción del fracaso empresarial mediante el uso de algoritmos genéticos. X Encuentro de Economía Aplicada, Logroño,, (2007),

[Ahn et al., 2000] B.S. Ahn, S.S. Cho, C.Y. Kim. The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Business Failure Prediction. Expert Systems with Applications, 18 (2000), pp. 65-74

[Altman, 1968] E.I. Altman. Financial Ratios. Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23 (1968), pp. 568-609

[Altman, 1981] E.I. Altman. Financial Handbook. John Wiley & Sons, (1981)

[Altman, 1983] E.I. Altman. Corporate Financial Distress. John Wiley & Sons, (1983)

[Altman, 1993] E.I. Altman. Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting and Avoiding Distress and Profiting from Bankruptcy. John Wiley & Sons, (1993)

[Altman et al., 1977] E.I. Altman, R. Haldeman, P. Narayanan. Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporations. Journal of Banking and Finance, 1 (June 1977), pp. 29-54

[Altman et al., 1994] E.I. Altman, G. Marco, F. Varetto. Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (The Italian Experience). Journal of Banking and Finance, 18 (1994), pp. 505-529

[Altman and Saunders, 1998] E.I. Altman, A. Saunders. Credit Risk Measurement: Developments over the Last 20 Years. Journal of Banking and Finance, 21 (December 1998), pp. 1721-1742

[Altman and Sabato, 2005] E.I. Altman, G. Sabato. Effects of the New Basel Capital Accord on Bank Capital Requirements for SMEs. Journal of Financial Services Research, 28 (2005), pp. 15-42

[Altman and Sabato, 2007] E.I. Altman, G. Sabato. Modeling Credit Risk for SMEs: Evidence from the U.S. Market. Abacus, 43 (2007), pp. 332-357

[Altman et al., 2008] E.I. Altman, G. Sabato, N. Wilson. The Value of Qualitative Information in SME Risk Management. Working Paper. Leonard N. Stern School of Business. New York University, (2008),

[Argenti, 1976] J. Argenti. Corporate Collapse: The Causes and Symptoms. John Wiley & Sons, (1976)

[Arquero et al., 2008] J.L. Arquero, M.C. Abad, S.M. Jiménez. Procesos de fracaso empresarial en PYMES. Identificación y contrastación empírica. Revista Internacional de la Pequeña y Mediana Empresa, 1 (2008), pp. 64-77

[Atiya, 2001] A.F. Atiya. Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results. IEEE Transactions on Neural Networks, 12 (2001), pp. 929-935 http://dx.doi.org/10.1109/72.935101

[Balcaen and Ooghe, 2006] S. Balcaen, H. Ooghe. 35 Years of Studies on Business Failure: An Overview of the Classic Statistical Methodologies and their Related Problems. British Accounting Review, 38 (2006), pp. 63-93

[Barniv et al., 1997] R. Barniv, A. Anurag, R. Leach. Predicting the Out Come Following Bankruptcy Filing: A Three State Classification Using NN, International Journal of Intelligent Systems in Accounting. Finance and Management, 6 (1997), pp. 177-194

[Beaver, 1966] W.H. Beaver. Financial Ratios as Predictors of Failure. Journal of Accounting Research, Supplement, (4 January 1966), pp. 71-111

[Beaver, 1968] W.H. Beaver. Alternative accounting measures and predictors of failure. The Accounting Review, (January 1968), pp. 113-122

[Beaver et al., 2009] W.H. Beaver, M. Correia, M. McNichols. 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, (2009),

[Beaver et al., 2005] W.H. Beaver, M. McNichols, J. Rhie. Have Financial Statements Become Less Informative?. Evidence from the Ability of Financial Ratios to Predict Bankruptcy. Review of Accounting Studies, 10 (2005), pp. 93-122

[Bechetti and Sierra, 2003] L. Bechetti, J. Sierra. Bankruptcy risk and productive efficiency in manufacturing firms. Journal of Banking and Finance, 27 (2003), pp. 2099-2120

[Bell, 1997] T.B. Bell. Neural Nets or the Logit Model?. A Comparison of Each Models Ability to Predict Commercial Bank Failures. International Journal of Intelligent Systems in Accounting. Finance and Management, 6 (1997), pp. 249-264

[Bell et al., 1990] T.B. Bell, G.S. Ribar, J. Verchio. Neural Nets Versus Logistic Regression: A Comparison of Each Models Ability to Predict Commercial Bank Failures. Auditing Symposium on Auditing Problems, pp. 29-53

[Bellovary et al., 2007] J.L. Bellovary, D.E. Giacomino, M.D. Akers. A Review of Bankruptcy Prediction Studies: 1930 to Present. Journal of Financial Education, 33 (2007), pp. 1-43

[Bhargava et al., 1998] M. Bhargava, C. Dubelaar, T. Scott. Predicting bankruptcy in the retail sector: an examination of the validity of key measures of performance. Journal of Retailing and Consumer Services, 5 (1998), pp. 105-117

[Blum, 1974] M. Blum. Failing Company Discriminant Analysis. Journal of Accounting Research, 12 (1974), pp. 1-25

[Bonsón Ponte et al., 1997a] E. Bonsón Ponte, T. Escobar Rodríguez, M.P. Martín Zamora. Decision Tree Induction Systems. Applications in Accounting and Finance. Bonsón PonteE.Sierra MolinaG.. Proceedings of the III International Meeting on Artificial Intelligence in Accounting, Finance and Tax, (1997),

[Bonsón Ponte et al., 1997b] E. Bonsón Ponte, T. Escobar Rodríguez, M.P. Martín Zamora. 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., (1997),

[Calvo-Flores et al., 2006] A. Calvo-Flores, D. García, A. Madrid. Tamaño Antigüedad y Fracaso Empresarial. Working Paper. Universidad Politécnica de Cartagena, (2006),

[Canbas et al., 2005] S. Canbas, A. Cabuk, S.B. Kilic. Prediction of Commercial Bank Failure via Multivariate Statistical Analysis of Financial Structure: The Turkish Case. European Journal of Operational Research, 166 (2005), pp. p.528-p.546

[Casey and Bartczak, 1985] C. Casey, N. Bartczak. Using Operating Cash Flow Data to Predict Financial Distress- Some Extensions. Journal of Accounting Research, 23 (1985), pp. 384-401

[Cielen et al., 2004] A. Cielen, L. Peeters, K. Vanhoof. Bankruptcy Prediction Using a Data Envelopment Analysis. European Journal of Operational Research, 154 (April 2004), pp. 526-532

[Collins and Green, 1982] R.A. Collins, R.D. Green. Statistical Methods for Bankruptcy Forecasting. Journal of Economics and Business, 34 (1982), pp. 349-354

[Correa et al., 2003] A. Correa, M. Acosta, A.L. González. La insolvencia empresarial: un análisis empírico para la pequeña y mediana empresa. Revista de Contabilidad, 6 (2003), pp. 47-79

[Crespo Domínguez, 2000] M.A. Crespo Domínguez. Una aproximación a la predicción del fracaso empresarial mediante redes neuronales. IX Encuentro de Profesores Universitarios de Contabilidad, Las Palmas de Gran Canaria, (2000), pp. 591-607

[Dambolena and Khoury, 1980] I.G. Dambolena, S.J. Khoury. Ratio Stability and Corporate Failure. Journal of Finance, 35 (September 1980), pp. 1017-1026

[Daubie and Meskens, 2002] M. Daubie, N. Meskens. Business Failure Prediction: A Review and Analysis of the Literature. New Trends in Banking Management, pp. 71-86

[Davydenko, 2007] S.A. Davydenko. 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 2007),

[De Andrés Suárez, 2000] J. De Andrés Suárez. Técnicas de Inteligencia Artificial aplicadas al análisis de la solvencia empresarial. Documento de Trabajo núm. 206, Universidad de Oviedo, Facultad de Ciencias Económicas, (2000),

[De Andrés Suárez, 2001] J. De Andrés Suárez. Statistical Techniques vs. SEE5 Algorithm. An Application to a Small Business Environment. The International Journal of Digital Accounting Research, 1 (July 2001), pp. 153-179

[De Andrés Sánchez, 2005] J. De Andrés Sánchez. Comparativa de métodos de predicción de la quiebra: Redes neuronales artificiales vs. métodos estadísticos multivariantes. Partida Doble, 168, julioagosto, (2005), pp. 105-113

[De la Torre et al., 2005] J.M. De la Torre, M.E. Gómez, I. Román. 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., (2005),

[De Miguel et al., 1993] L.J. De Miguel, E. Revilla, J.M. Rodríguez, J.M. Cano. 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,

[Deakin, 1972] E.B. Deakin. A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, 10 (1972), pp. 167-179

[Deakin, 1976] E.B. Deakin. Distributions of Financial Accounting Ratios: Some Empirical Evidence. The Accounting Review, 51 (January 1976), pp. 90-96

[Del Rey Martínez, 1996] E. Del Rey Martínez. Bankruptcy Prediction in Non-Finance Companies: An Application Based on Artificial Neural Network Models. Intelligent Systems in Accounting and Finance, pp. 253-272

[Dewaelheyns and Van Hulle, 2004] N. Dewaelheyns, C. Van Hulle. The Impact of Business Groups on Bankruptcy Prediction Modeling. Tijdschrift voor Economie en Management, 49 (2004), pp. 623-645

[Dewaelheyns and Van Hulle, 2006] N. Dewaelheyns, C. Van Hulle. Corporate Failure Prediction Modeling: Distorted by Business Groups’ Internal Capital Markets?. Journal of Business Finance & Accounting, 33 (2006), pp. 909-931 http://dx.doi.org/10.1016/j.jebo.2014.01.017

[Dimitras et al., 1996] A. Dimitras, S. Zanakis, C. Zopounidis. A survey of Business Failures with an Emphasis on Failure Prediction Methods and Industrial Applications. European Journal of Operational Research, 90 (1996), pp. 487-513

[Dutta and Shekhar, 1992] S. Dutta, S. Shekhar. Bond rating: a non conservative application of neural networks. Neural Networks in Finance and Investing, Probus Publishing, (1992)pp. 443-450

[Edmister, 1972] R.O. Edmister. An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction. Journal of Financial and Quantitative Analysis, 7 (March 1972), pp. 1477-1493

[Edmister, 1988] R.O. Edmister. Combining Human Credit Analysis and Numerical Credit Scoring for Business Failure Prediction. Akron Business and Economic Review, 19 (1988), pp. 6-14

[Elam, 1975] R. Elam. The Effect of Lease Data on the Predictive Ability of Financial Ratios. The Accounting Review, 50 (January 1975), pp. 25-43

[Fernández and Olmeda, 1995] E. Fernández, I. Olmeda. Bankruptcy Prediction with Artificial Neural Networks. Lecture Notes on Computational Sciences, 930 (1995), pp. 1142-1146

[Ferrando and Blanco, 1998] M. Ferrando, F. Blanco. La previsión del fracaso empresarial en la comunidad valenciana: aplicación de los modelos discriminante y logit. Revista Española de Financiación y Contabilidad, 27 (abril-junio 1998), pp. 499-540

[Fletcher and Goss, 1993] D. Fletcher, E. Goss. Application Forecasting with Neural Networks: An Application Using Bankruptcy Data. Information and Management, 24 (1993), pp. 159-167

[Frydman et al., 1985] H. Frydman, E.I. Altman, D. Kao. Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress. The Journal of Finance, 40 (March 1985), pp. 269-291

[Gabás Trigo, 1990] F. Gabás Trigo. Técnicas actuales de análisis contable, evaluación de la solvencia empresarial. Madrid: Instituto de Contabilidad y Auditoría de Cuentas. Ministerio de Economía y Hacienda, (1990),

[Gallego et al., 1997] A.M. Gallego, J.C. Gómez, L. Yáñez. Modelos de predicción de quiebras en empresas no financieras. Actualidad Financiera, 2 (mayo 1997), pp. 3-14

[Gandía et al., 1995] J.L. Gandía, J.L. García, R. Molina. Estudio Empírico de la Solvencia Empresarial en la Comunidad Valenciana. Valencia: Instituto Valenciano de Investigaciones Económicas, (Junio 1995),

[García et al., 1995] D. García, A. Arqués, A. Calvo-Flores. Un modelo discriminante para evaluar el riesgo bancario en los créditos a empresas. Revista Española de Financiación y Contabilidad, 24 (enero-marzo 1995), pp. 175-200

[García-Ayuso, 1995] M. García-Ayuso. La necesidad de llevar a cabo un replanteamiento de la investigación en materia de análisis de la información financiera. Análisis financiero, 66 (1995), pp. 36-61

[Gazengel and Thomas, 1992] A. Gazengel, P. Thomas. Les défaillances d’entreprises. Les Cahiers de Recherche, 105, 47 p., École Superieure de Commerce de Paris, (1992),

[Gentry et al., 1985] J. Gentry, P. Newbold, D. Whitford. Classifying Bankrupt Firms with Funds Flow Components. Journal of Accounting Research, 23 (1985), pp. 146-159

[Gilbert et al., 1990] L.R. Gilbert, K. Menon, K.B. Schwartx. Predicting Bankruptcy for Firms in Financial Distress, Journal of Business. Finance and Accounting, 17 (1990), pp. 161-171

[Gombola and Ketz, 1983] M.J. Gombola, J.E. Ketz. A Note on Cash Flow and Classification Patterns of Financial Ratios. Accounting Research, 58 (January 1983), pp. 105-114

[Gómez et al., 2008] M.A. Gómez, J.M. Torre, I. Román. Análisis de sensibilidad temporal en los modelos de predicción de insolvencia: una aplicación a las PYMES industriales. Revista Española de Financiación y Contabilidad, 37 (enero-marzo 2008), pp. 85-111

[Graveline and Kokalari, 2008] J. Graveline, M. Kokalari. Credit risk. Working Paper, The Research Foundation of CFA Institute, (November 2008)

[Greenstein and Welsh, 1996] M.M. Greenstein, M.J. Welsh. . Bankruptcy prediction using ex-ante neural networks and reallistically proportioned testing sets., pp. 187-212

[Grice and e Ingram, 2001] J.S. Grice, R.W. e Ingram. Tests of the Generalizability of Altmans Bankruptcy Prediction Model. Journal of Business Research, 54 (2001), pp. 53-61

[Grunert et al., 2005] J. Grunert, L. Norden, M. Weber. The Role of Non-Financial Factors in Internal Credit Ratings. Journal of Banking and Finance, 29 (2005), pp. 509-531

[Hair et al., 1999] J.F. Hair, R.E. Anderson, R.L. Tatham, W.C. Black. Análisis multivariante. Prentice-Hall, (1999)

[Hayden, 2003] E. Hayden. Are Credit Scoring Models Sensitive with Respect to Default Definitions?. Evidence from the Australian Market, Dissertation Paper, Department of Business Administration, Univesity of Vienna, (2003)pp. p.1-p.43

[Hill et al., 1996] N.T. Hill, S.E. Perry, S. Andes. Evaluating Firms in Financial Distress: An Event History Analysis. Journal of Applied Business Research, 13 (1996), pp. 60-71

[Hillegeist et al., 2004] S.A. Hillegeist, E.K. Keating, D.P. Cram, K.G. Lundstedt. Assessing the Probability of Bankruptcy. Review of Accounting Studies, 9 (2004), pp. 5-34

[Holder, 1984] M. Holder. Le score de l’enterprise.. Nouvelles Editions Fiduciaires, (1984)

[Jacobson, 2008] T. Jacobson, R. Kindell, J. Lindé, K. Roszbach. Firm Default and Aggregate Fluctuations. Working Paper, Sveriges Riskbank, n°. 226, (September 2008),

[Jones, 1987] F.L. Jones. Current Techniques in Bankruptcy Prediction. Journal Accounting Literature, 6 (1987), pp. 131-164

[Jones and Hensher, 2004] S. Jones, D.A. Hensher. Predicting Firm Financial Distress: A Mixed Logit Model. The Accounting Review, 79 (2004), pp. 1011-1038

[Jones and Hensher, 2008] S. Jones, D.A. Hensher. Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction. Cambridge University Press, (2008)

[Kaski et al., 2001] S. Kaski, J. Sinkkonen, J. Peltonen. Bankruptcy Analysis with Self-Organizing Maps in Learning Metrics. IEEE Transactions on Neural Networks, 12 (2001), pp. 936-947 http://dx.doi.org/10.1109/72.935102

[Keasey and Watson, 1987] K. Keasey, R. Watson. Non-financial symptoms and the prediction of small company failure: a test of Argentis hypothesis, Journal of Business. Finance and Accounting, 14 (1987), pp. 335-354

[Keasey and Watson, 1988] K. Keasey, R. Watson. The non-submission of accounts and small company financial failure prediction. Accounting and Business Research, 19 (1988), pp. 47-54

[Keasey and Watson, 1991] K. Keasey, R. Watson. Financial Distress Prediction Models: A Review of their Usefulness. British Journal of Management, 2 (July 1991), pp. 89-102

[Ketz, 1978] J.E. Ketz. The Effect of General Price-Level Adjustments on the Predictability of Financial Ratios. Journal of Accounting Research, 16 supplement, (1978), pp. 273-284

[Kiviluoto, 1998] K. Kiviluoto. Predicting Bankruptcies with Self Organizing Map. Neurocomputing, 21 (1998), pp. 191-201

[Koh, 1991] H.C. Koh. Model Predictions and Auditor Assessment of Going Concern Status. Accounting and Business Research, 21 (1991), pp. 331-338

[Koh and Tan, 1999] H.C. Koh, S.S. Tan. A neural network approach to the prediction of going concern status. Accounting and Business Research, 29 (1999), pp. 211-216

[Kuo, 2007] Y.C. Kuo. The Data Envelopment Models for the Application of Two-Group Discrimininant Analysis. Tesis Doctoral, (2007),

[Labatut et al., 2009] G. Labatut, J. Pozuelo, E.J. Veres. Modelización temporal de los ratios contables en la detección del fracaso empresarial de la PYME española. Revista Española de Financiación y Contabilidad, 38 (julio-septiembre 2009), pp. 423-448

[Lacher et al., 1995] R.C. Lacher, P.K. Coats, S.C. Sharma, L.F. Fant. A Neural Network for Classifying The Financial Health of a Firm. European Journal of Operational Research, 85 (1995), pp. 53-65

[Laffarga et al., 1985] J. Laffarga, J.L. Martín, M.J. Vázquez. El análisis de la solvencia de las instituciones bancarias: Propuesta de una metodología y aplicaciones a la Banca española. Esic-Market, 48 (2° trim.), (1985), pp. 51-73

[Laffarga et al., 1986a] J. Laffarga, J.L. Martín, M.J. Vázquez. El pronóstico a corto plazo del fracaso en las instituciones bancarias: metodología y aplicaciones a la Banca española. Esic-Market, 53, (3° trim.), (1986), pp. 59-116

[Laffarga et al., 1986b] J. Laffarga, J.L. Martín, M.J. Vázquez. El pronóstico a largo plazo del fracaso en las instituciones bancarias: metodología y aplicaciones al caso español. Esic-Market, 54, (4° trim.), (1986), pp. 113-167

[Laffarga et al., 1987] J. Laffarga, J.L. Martín, M.J. Vázquez. Predicción de la crisis bancaria española: La comparación entre el análisis logit y el análisis discriminante. Cuadernos de Investigación Contable, 1 (otoño 1987), pp. 103-110

[Laffarga et al., 1991] J. Laffarga, J.L. Martín, M.J. Vázquez. La predicción de la quiebra bancaria: el caso español. Revista Española de Financiación y Contabilidad, 20 (enero-marzo 1991), pp. 151-163

[Laitinen, 1993] E.K. Laitinen. Financial Predictors for Different Phases of the Failure Process. Omega International Journal of Management Science, 21 (1993), pp. 215-228

[Laitinen and Kankaanpää, 1999] T. Laitinen, M. Kankaanpää. Comparative Analysis of Failure Prediction Methods: the Finnish Case. The European Accounting Review, 8 (1999), pp. p.67-p.92

[Lee et al., 2005] K. Lee, D. Booth, P. Alam. A Comparison of Supervised and Unsupervised Neural Networks in Predicting Bankruptcy of Korean Firms. Expert Systems with Applications, 29 (2005), pp. 1-16

[Lee and Urrutia, 1996] S.H. Lee, J.L. Urrutia. 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 (1996), pp. 121-130

[Lennox, 1999] C. Lennox. Identifying Failing Companies: A Re-evaluation of the Logit, Probit and DA Approaches. Journal of Economics and Business, 51 (July 1999), pp. 347-364

[Leshno and Spector, 1996] M. Leshno, Y. Spector. Neural Network Prediction Analysis: The Bankruptcy Case. Neurocomputing, 10 (1996), pp. 125-147

[Libby, 1975] R. Libby. Accounting ratios and the prediction of failure: Some behavioural evidence. Journal of Accounting Research, 13 (1975), pp. 150-161

[Lincoln, 1984] M. Lincoln. An empirical study of the usefulness of accounting ratios to describe levels of insolvency risk. Journal of Banking and Finance, 8 (1984), pp. 321-340

[Liou and Smith, 2006] D.K. Liou, M. Smith. Macroeconomic Variables in the Identification of Financial Distress. Working Paper, (May 2006),

[Lizarraga Dallo, 1997] F. Lizarraga Dallo. 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 (octubre-diciembre 1997), pp. 871-915

[Lizarraga Dallo, 1998] F. Lizarraga Dallo. Modelos de predicción del fracaso empresarial: ¿Funciona entre nuestras empresas el modelo de Altman de 1968?. Revista de Contabilidad, 1 (enerojunio 1998), pp. 137-164

[Lo, 1986] A.W. Lo. Logic Versus Discriminant Analysis. Journal of econometrics, 31 (1986), pp. 151-178

[López and Flórez, 1999] E. López, R. Flórez. El análisis de solvencia empresarial utilizando redes neuronales autoasociativas: el modelo Koh-León. Proceedings of the VI International Meeting on Advances in Computational Management,

[López and Flórez, 2000] E. López, R. Flórez. Aplicación de dos modelos de redes neuronales artificiales para el análisis económico-financiero empresarial. Revista Europea de Dirección y Economía de la Empresa, 9 (2000), pp. 139-164

[López et al., 1998] J. López, J.L. Gandía, R. Molina. La suspensión de pagos en las pymes: una aproximación empírica. Revista Española de Financiación y Contabilidad, 27 (eneromarzo 1998), pp. 71-97

[López et al., 1994] D. López, J. Moreno, P. Rodríguez. Modelos de predicción del fracaso empresarial. Aplicación a entidades de seguros en España. Esic-Market, 84 (1994), pp. 83-125

[Madrid and García, 2006] A. Madrid, D. García. Factores que explican el fracaso empresarial en la pyme. Gestión: Revista de Economía, 36 (marzo-junio 2006), pp. 5-9

[Mar Molinero and Ezzamel, 1991] C. Mar Molinero, M. Ezzamel. Multidimensional Scaling Applied to Corporate Failure. Omega, 19 (1991), pp. 259-274

[Mar and Serrano, 2001] C. Mar, C. Serrano. Bank Failure: A Multidimensional Scaling Approach. The European Journal of Finance, 7 (2001), pp. 165-183

[Marais et al., 1984] M. Marais, J. Patell, M. Wolfson. The Experimental Design of Classification Models: An Application of Recursive Partitioning and Bootstrapping to Commercial Bank Loan Classifications. Journal of Accounting Research, 22 (1984), pp. 87-118

[Marose, 1992] R.A. Marose. A Financial Neural Network Application. En Neural Networks in Finance and Investing, Probus Publishing, (1992)pp. 50-53

[Martin, 1977] D. Martin. Early Warning of Bank Failure. Journal of Banking and Finance, 1 (1977), pp. 249-276

[Martínez, 1996] I. Martínez. . Forecasting Company Failure: Neural Approach versus Discriminant Analysis: An Application to Spanish Insurance Companies, pp. 169-185

[Martínez et al., 1989] C. Martínez, M.V. Navarro, F. Sanz. Selección y explotación de los sistemas de alarma y prevención de quiebra. Investigaciones Económicas, (supl.), 13 (1989), pp. 135-141

[McDonald and Morris, 1984] B.D. McDonald, M.H. Morris. The Statistical Validity of the Ratio Method in Financial Analysis: An Empirical Examination, Journal of Business. Finance and Accounting, 11 (1984), pp. 89-97

[McGahan and Porter, 1997] A.M. McGahan, M.E. Porter. How Much Does Industry Matter, Really?. Strategic Management Journal, 18 (1997), pp. 15-30

[McGurr and DeVaney, 1998] P.T. McGurr, S.A. DeVaney. Predicting Business Failure of Retail Firms: An Analysis Using Mixed Industry Models. Journal of Business Research, 43 (1998), pp. 169-176

[McKee, 1990] T.E. McKee. Evaluation of Enterprise Continuity Status Via Neural Networks. Abstracts of the Thirteenth Annual Congress of the European Accounting Association, (1990), pp. 72

[McKee, 2000] T.E. McKee. Developing a Bankruptcy Prediction Model via Rough Sets Theory. International Journal of Intelligent Systems in Accounting. Finance & Management, 9 (September 2000), pp. 159-173 http://dx.doi.org/10.1371/journal.pone.0119445

[Mensah, 1984] Y.M. Mensah. An Examination of the Stationary of Multivariate Bankruptcy Prediction Models: A Methodological Study. Journal of Accounting Research, 22 (1984), pp. 380-395

[Messier and Hansen, 1988] W.F. Messier Jr., J.V. Hansen. Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data. Management Science, 34 (1988), pp. 1403-1415

[Meyer and Pifer, 1970] P.A. Meyer, H.W. Pifer. Predictions of Bank Failures. The Journal of Finance, 25 (September 1970), pp. 853-868

[Min et al., 2006] S.H. Min, J. Lee, I. Han. Hybrid Genetic Algorithms and Support Vector Machines for Bankruptcy Prediction. Expert Systems with Applications, 31 (2006), pp. 652-660

[Mora Enguídanos, 1994a] A. Mora Enguídanos. Limitaciones metodológicas de los trabajos empíricos sobre la predicción del fracaso empresarial. Revista Española de Financiación y Contabilidad, 24 (1994), pp. 709-732

[Mora Enguídanos, 1994b] A. Mora Enguídanos. Los modelos de predicción del fracaso empresarial: una aplicación empírica del logit. Revista Española de Financiación y Contabilidad, 24 (1994), pp. 203-233

[Norton, 1976] C.L. Norton. A Comparison of the Abilities of General Price Level and Conventional Financial Ratios to Predict Bankruptcy. Arizona State University, (1976)

[Norton and Smith, 1979] C. Norton, R. Smith. A Comparison of General Price Level and Historical Cost Financial Statements in the Prediction of Bankruptcy. The Accounting Review, 54 (January 1979), pp. 72-87

[Odom and Sharda, 1992] M.D. Odom, R. Sharda. A Neural Network Model for Bankruptcy Prediction. Neural networks in Finance and Investing, pp. p.163-p.168

[Ohlson, 1980] J.A. Ohlson. Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18 (1980), pp. 109-131

[Palepu, 1986] K.G. Palepu. Predicting Takeover Targets: A Methodological and Empirical Analysis. Journal of Accounting and Economics, 8 (March 1986), pp. 3-35

[Paradi et al., 2004] J.C. Paradi, M. Asmild, P.C. Simak. Using DEA and Worst Practice DEA in Credit Risk Evaluation. Journal of Productivity Analysis, 21 (March 2004), pp. 153-165

[Park and Han, 2002] C.S. Park, I. Han. A Case-Based Reasoning with the Feature Weights Derived by Analytic Hierarchy Process for Bankruptcy Prediction. Expert Systems with Applications, 23 (2002), pp. 255-264

[Peel and Peel, 1987] M.J. Peel, D.A. Peel. Some Further Empirical Evidence on Predicting Private Company Failure. Accounting and Business Research, 18 (1987), pp. 57-66

[Peel et al., 1986] M.J. Peel, D.A. Peel, P.F. Pope. Predicting Corporate Failure. Some Results for the UK Corporate Sector. Omega, 14 (1986), pp. 5-12

[Pina Martínez, 1989] V. Pina Martínez. Estudio empírico de la crisis bancaria. Revista Española de Financiación y contabilidad, 28 (enero-marzo 1989), pp. 309-338

[Piramuthu et al., 1998] S. Piramuthu, H. Ragavan, M.J. Shaw. Using Feature Construction to Improve the Performance of Neural Networks. Management Science, 44 (1998), pp. 416-430

[Platt and Platt, 1991] H.D. Platt, M.B. Platt. A Note on the Use of Industry Relative Ratios in Bankruptcy Prediction. Journal of Banking and Finance, 15 (1991), pp. 1183-1194

[Platt and Platt, 2002] H.D. Platt, M.B. Platt. Predicting Corporate Financial Distress: Reflections on Choice-Based Sample Bias. Journal of Economics and Finance, 26 (2002), pp. 184-199

[Platt et al., 1994] H.D. Platt, M.B. Platt, J.G. Pedersen. Bankruptcy Discrimination with Real Variables, Journal of Business. Finance and Accounting, 21 (June 1994), pp. 491-510

[Premachandra et al., 2009] I.M. Premachandra, G.S. Bhabra, T. Sueyoshi. DEA as a Tool for Bankruptcy Assessment: A Comparative Study with Logistic Regression Technique. European Journal of Operational Research, 193 (2009), pp. 412-424

[Ramírez Comeig, 1996] I. Ramírez Comeig. La utilidad del análisis multivariante para evaluar la solvencia de las pequeñas empresas. X Congreso Nacional de AEDEM, Granada, junio, Ponencias y Comunicaciones, (1996), pp. 463-473

[Ravi Kumar and Ravi, 2007] P. Ravi Kumar, V. Ravi. Bankruptcy Prediction in Banks and Firms Via Statistical and Intelligent Techniques - A Review. European Journal of Operational Research, 180 (2007), pp. 1-28

[Rodríguez Acebes, 1990] M.C. Rodríguez Acebes. La Predicción de las Crisis Empresariales. Modelos para el Sector de Seguros, Valladolid: Secretariado de Publicaciones. Universidad de Valladolid, (1990),

[Rodríguez Fernández, 1986] J.M. Rodríguez Fernández. Crisis en los bancos privados españoles: un modelo logit. Investigaciones Económicas, (supl.), (1986), pp. 59-64

[Rodríguez Fernández, 1987] J.M. Rodríguez Fernández. Crisis en los bancos privados españoles: un modelo logit. II. Jornadas de Economía Industrial, Madrid, (1987),

[Rodríguez Fernández, 1989a] J.M. Rodríguez Fernández. Análisis de las insolvencias bancarias en España: un modelo empírico. Moneda y Crédito, 189 (1989), pp. 187-227

[Rodríguez Fernández, 1989b] J.M. Rodríguez Fernández. The Crisis in Spanish Private Banks: A Logit Analysis. Finance, 10 (junio 1989), pp. 69-88

[Rodríguez and Díaz, 2005] M. Rodríguez, F. Díaz. 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,

[Rodríguez López, 2001] M. Rodríguez López. Predicción del fracaso empresarial en compañías no financieras. Consideración de técnicas de análisis multivariante de corte paramétrico. Actualidad Financiera, 6 (2001), pp. 27-42

[Román et al., 2002] I. Román, J.M. De La Torre, P.A. Castillo, J.J. Merelo. Sectorial Bankruptcy Prediction Analysis Using Artificial Neural Networks. The Case of Spanish Companies. European Accounting Congress,

[Román et al., 2001] I. Román, J.M. De la Torre, J.L. Zafra. Análisis sectorial de la predicción del riesgo de insolvencia: un estudio empírico. XI Congreso AECA: Empresa, Euro y Nueva Economía,

[Rose et al., 1982] P.S. Rose, W.T. Andrews, G.A. Giroux. Predicting Business Failure: A Macroeconomic Perspective, Journal of Accounting. Auditing and Finance, 6 (1982), pp. 20-31

[Rubio Misas, 2008] M. Rubio Misas. Análisis del fracaso empresarial en Andalucía. Especial referencia a la edad de la empresa. Cuadernos de CC.EE. Y EE., 54 (2008), pp. 35-56

[Rughupathi et al., 1993] W. Rughupathi, L. Schkade, B.S. Raju. A Neural Network to Bankruptcy Prediction. Neural Network in Finance and Investing, pp. 159-176

[Rumelt, 1997] R.P. Rumelt. How Much Does Industry Matter?. Strategic Management Journal, 12 (1997), pp. 167-185

[Santomero and Vinso, 1977] A.M. Santomero, J.D. Vinso. Estimating the Probability of Failure for Commercial Banks and the Banking System. Journal of Banking and Finance, 1 (October 1977), pp. 185-205

[Sarle, 1994] W.S. Sarle. Neural Networks and Statistical Models. Proceedings of the Nineteenth Annual SAS Users Group International Conference,

[Scott, 1981] J. Scott. The Probability of Bankruptcy. Journal of Banking and Finance, 5 (1981), pp. 317-344

[Serrano Cinca, 1994] C. Serrano Cinca. Las redes neuronales artificiales en el análisis de la información contable. (Tesis doctoral). Zaragoza: Universidad de Zaragoza, (1994),

[Serrano Cinca, 1996] C. Serrano Cinca. Self Organizing Neural Networks for Financial Diagnosis. Decision Support Systems, 17 (1996), pp. 227-238

[Serrano Cinca, 1997] C. Serrano Cinca. Feedforward Neural Networks in the Classification of Financial Information. European Journal of Finance, 3 (septiembre 1997), pp. 183-202

[Serrano and Martín, 1993] C. Serrano, B. Martín. Predicción de la crisis bancaria mediante el empleo de redes neuronales artificiales. Revista Española de Financiación y Contabilidad, 22 (1993), pp. 153-176

[Shin and Lee, 2002] K.S. Shin, Y.J. Lee. A Genetic Algorithm Application in Bankruptcy Prediction Modeling. Expert Systems with Applications, 23 (2002), pp. 321-328

[Shin et al., 1998] K.S. Shin, T.S. Shin, I. Han. 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, (1998),

[Shrieves and Stevens, 1979] R.E. Shrieves, D.L. Stevens. Bankruptcy Avoidance as a Motive for Merger. Journal of Financial and Quantitative Analysis, 3 (1979), pp. 501-515

[Shumway, 2001] T. Shumway. Forcasting Bankruptcy More Accurately: A Simple Hazard Model. Journal of business, 74 (January 2001), pp. 101-124

[Sinkey, 1975] J.F. Sinkey. A Multivariate Statistical Analysis of the Characteristics of Problem Banks. The Journal of Finance, 30 (March 1975), pp. 21-36

[Slowinski and Zopounidis, 1995] R. Slowinski, C. Zopounidis. Application of the Rough Set Approach to Evaluation of Bankruptcy Risk. International Journal of Intelligent Systems in Accounting Finance and Management, 4 (1995), pp. 27-41

[Somoza López, 2001] A. Somoza López. La consideración de factores cualitativos, macroeconómicos y sectoriales en los modelos de predicción de la solvencia empresarial. Papeles de Economía Española, 89/90, (2001), pp. 402-426

[Somoza López, 2002] A. Somoza López. Modelos de predicción de la insolvencia: la incorporación de otro tipo de variables. La gestión del riesgo de crédito, pp. 139-173

[Stein and Ziegler, 1984] J.H. Stein, W. Ziegler. The Prognosis and Surveillance of Risks from Commercial Credit Borrowers. Journal of Banking and Finance, 8 (June 1984), pp. 249-268

[Sueyoshi and Goto, 2009a] T. Sueyoshi, M. Goto. 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 (2009), pp. 289-311

[Sueyoshi and Goto, 2009b] T. Sueyoshi, M. Goto. DEA–DA for Bankruptcy-Based Performance Assessment: Misclassification Analysis of Japanese Construction Industry. European Journal of Operational Research, 199 (2009), pp. 576-594

[Sueyoshi and Goto, 2009c] T. Sueyoshi, M. Goto. Methodological Comparison between DEA (Data Envelopment Analysis) and DEA–DA (Discriminant Analysis) from the Perspective of Bankruptcy Assessment. European Journal of Operational Research, 199 (2009), pp. 561-575

[Swicegood and Clark, 2001] P. Swicegood, J.A. Clark. 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 (2001), pp. 169-186

[Surkan and Singleton, 1992] A.J. Surkan, J.C. Singleton. Neural Networks for Bond Rating Improved by Multiple Hidden Layers. Neural networks in Finance and Investing,

[Taffler, 1982] R.J. Taffler. Forecasting Company Failure in the UK using Discriminant Analysis and Finance Ratio Data. Journal of the Royal Statistical Society Series A, 145 (1982), pp. 342-358

[Taffler, 1983] R.J. Taffler. The Assessment of Company Solvency and Performance Using a Statistical Model. Accounting and Business Research, 15 (1983), pp. 295-307

[Tam, 1991] K.Y. Tam. Neural Network Models and the Prediction of Bank Bankruptcy. Omega, 19 (1991), pp. 429-445

[Tam and Kiang, 1992] K.Y. Tam, M.Y. Kiang. Managerial Applications of Neural Networks: The Case of Bank Failure Predictions. Management Science, 38 (July 1992), pp. 926-947

[Troutt et al., 1996] M.D. Troutt, A. Rai, A. Zhang. The Potential Use of DEA for Credit Applicant Acceptance Systems. Computers & Operations Research, 23 (April 1996), pp. 405-408 http://dx.doi.org/10.1103/PhysRevLett.114.090503

[Tsukuda and Baba, 1994] J. Tsukuda, S.I. Baba. Predicting Japanese Corporate Bankruptcy in Terms of Finance Data Using Neural Network. Computers and Industrial Engineering, 27 (1994), pp. 445-448

[Westgaard and Van Der Wijst, 2001] S. Westgaard, N. Van Der Wijst. Default Probabilities in a Corporate Bank Portfolio: A Logistic Model Approach. European Journal of Operational Research, 135 (December 2001), pp. 338-349

[Whalen, 1991] G. Whalen. 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 (1991), pp. 21-31

[Wheelock and Wilson, 2000] D.C. Wheelock, P.W. Wilson. Why do Banks Disappear? The Determinants of U. S. Bank Failures and Acquisitions. The Review of Economics and Statistics, 82 (February 2000), pp. 127-138

[Wilson and Sharda, 1994a] R.L. Wilson, R. Sharda. Bankruptcy Prediction Using Neural Networks. Decision Support Systems, 11 (1994), pp. 545-557

[Whittred and Zimmer, 1984] G.P. Whittred, I. Zimmer. Timeliness of Financial Reporting and Financial Distress. The Accounting Review, 59 (April 1984), pp. 295-297

[Wilcox, 1971] J.W. Wilcox. A Gamblers Ruin Prediction of Business Failure Using Accounting Data. Sloan Management Review, 12 (September 1971), pp. 1-10

[Wilcox, 1976] J.W. Wilcox. The Gamblers Ruin Approach to Business Risk. Sloan Management Review, 18 (autumn), (1976), pp. 33-46

[Wilson and Sharda, 1994b] R.L. Wilson, R. Sharda. Bankruptcy Prediction using Neural Networks. Decision Support Systems, 11 (1994), pp. 545-557

[Xu and Zhang, 2009] M. Xu, C. Zhang. Bankruptcy Prediction: The Case of Japanese Listed Companies. Review of Accounting Studies, 14 (December 2009), pp. 534-558

[Zavgren, 1983] C.V. Zavgren. The prediction of corporate failure: the state of the art. Journal of Accounting Literature, 2 (1983), pp. 1-38

[Zavgren, 1985] C.V. Zavgren. Assessing the Vulnerability of Failure of American Industrial Firms: A Logistic Analysis. Journal of Banking and Finance., 12 (1985), pp. 19-45

[Zavgren, 1988] C.V. Zavgren. The Association between Probabilities of Bankrupcy and Market Responses- A Test of Market Anticipation, Journal of Business. Finance and Accounting., 15 (1988), pp. 27-45

[Zhang et al., 1999] G.P. Zhang, M.Y. Hu, B.E. Patuwo, D.C. e Indro. Artificial Neural Networks in Bankruptcy Prediction: General Framework and Cross-Validation Analysis. European Journal of Operational Research, 116 (July 1999), pp. 16-32

[Zmijewski, 1984] M. Zmijewski. Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22 (supplement), (1984), pp. 59-86
Publicado
01-01-2012
Cómo citar
Tascon, M. T., & Castaño Gutierrez, F. (2012). Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente. Revista De Contabilidad - Spanish Accounting Review, 15(1), 7-58. https://doi.org/10.1016/S1138-4891(12)70037-7
Sección
Artículos