Determinación del riesgo de fracaso financiero mediante la utilización de modelos paramétricos, de inteligencia artificial, y de información de auditoría

  1. Manuel Rodríguez López
  2. Carlos Piñeiro Sánchez
  3. Pablo de Llano Monelos
Revista:
Estudios de economía

ISSN: 0304-2758 0718-5286

Año de publicación: 2014

Volumen: 41

Número: 2

Páginas: 187-217

Tipo: Artículo

DOI: 10.4067/S0718-52862014000200002 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Estudios de economía

Objetivos de desarrollo sostenible

Resumen

This paper offers an exhaustive analysis of the effectiveness of several models and methodologies that are commonly used to forecast financial failure: Linear, MDA, Logit, and artificial neural network. Our main aim is to evaluate their relative strengths and weaknesses, in terms of technical reliability and error cost; to do so, models are estimated and validated, and then used to perform an artificial simulation to evaluate which of them causes the lower cost of errors. Reliability is examined in four forecast horizons, to collect evidences about temporal (in) stability. We also check the relative advantages of financial ratios-based models, versus audit-based forecast models. Our results suggest that all models attain a high performance rate; however, artificial neural networks' forecasts seem to be more stable, both in temporal and cross-sectional perspectives.

Referencias bibliográficas

  • Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance. XXIII. 589-609
  • Altman, E. I. (1977). Some estimates of the cost of lending errors for commercial banks. Journal of Commercial Bank Lending.
  • Altman, E. I. (2000). Predicting Financial Distress of Companies: Revisiting the Z-Score and ZETA© Models. NYU Salomon Center.
  • Altman, E. I, Haldeman, R. C, Narayanan, P. (1977). ZETA Analysis: A New Model to Identify Bankruptcy Risk Corporations. Journal of Banking and Finance. 29-54
  • Altman, E. I, Mcgough, T. (1974). Evaluation of a Company as a Going Concern. Journal of Accountancy. 50-57
  • Arnedo, L, Lizarraga, F, Sánchez, S. (2008). Going-concern Uncertainties in Prebankrupt Audit Reports: New Evidence Regarding Discretionary Accruals and Wording Ambiguity. International Journal of Auditing. 12. 25-44
  • Beaver, W. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research. 5. 71-111
  • Bell, T. B, Ribar, G. S, Verchio, J. (1990). Neural Nets versus Logistic Regression: A Comparison of Each Model's Ability to Predict Commercial Bank Failures. Auditing Symposium X Deloitte & Touche, Symposium on Auditing Problems. Kansas. 29-53
  • Brockett, P, Golden, L, Jang, J, Yang, C. (2006). A comparison of neural network, statistical methods, and variable choice for life insurers' financial distress prediction. The Journal of Risk and Insurance. 73. 397-419
  • Carcello, J, Palmrose, Z. (1994). Auditor litigation and modified reporting on bankrupt clients. Journal of Accounting Research. 1-30
  • Citron, D, Taffler, R. (2001). Ethical Behavior in the U.K. Audit Profession: The Case of the Self-Fulfilling Prophecy Under Going-Concern Uncertainties. Journal of Business Ethics. 29. 353-363
  • Deakin, E. (1975). A discriminant analysis of predictors of business failure. Journal of Accounting Research. 167-179
  • Dubois, D, Prade, H. (1992). Handbook of Applications and Advances in Rough Set Theory. Kluwer Academic. Dordrecht. 203-232
  • Dopuch, N, Holthausen, R.W, Leftwich, R.W. (1987). Predicting Audit Qualifications with Financial and Market Variables. The Accounting Review. 62. 431-454
  • Efron, B. (1975). The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis. Journal of the American Statistical Association. 70. 892-898
  • Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics. 7. 1-26
  • Eisenbeis, R. (1977). Pitfalls in the Application of Discriminant Analysis. Journal of Finance. 32. 875-900
  • Elam, R. (1975). The Effect of Lease Data on the Predictive Ability of Financial Ratios. The Accounting Review. 25-43
  • Ethridge, H.L, Sriram, R.S, Hsu, H.Y.K. (2000). A comparison of selected artificial neural networks that help auditors evaluate client financial viability. Decision Science. 31. 531-550
  • Ferrando, M, 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. XXVII. 499-540
  • Frydman, H, Altman, E. I, Kao, D. L. (1985). Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress. The Journal of Finance. XL. 269-291
  • Gabás Tribo, F. (1990). Técnicas Actuales de Análisis Contable: Evaluación de la Solvencia Empresarial. Instituto de Contabilidad y Auditoría de Cuentas. Madrid.
  • Gallego, A. M, Gómez, J. C, Yáñez, L. (1996). Modelos de Predicción de Quiebras en Empresas No Financieras. IV Foro de Finanzas AEFIN. Madrid.785-804
  • Gandía, J. L, García, J. L, Molina, R. (1995). Estudio Empírico de la Solvencia Empresarial en la Comunidad Valenciana. Instituto Valenciano de Investigaciones Económicas. Valencia.
  • García, D, Arqués, A, Calvo-Flores, A. (1995). Un Modelo Discriminante para Evaluar el Riesgo Bancario en los Créditos a Empresas. Revista Española de Financiación y Contabilidad. XXIV. 175-200
  • Gombola, M. J, Ketz, E. (1983). A Note on Cash Flow and Classification Patterns of Financial Ratios. The Accounting Review. LVIII. 105-114
  • Hansen, J, Messier, W. (1991). Artificial neural networks: foundations and application to a decision problem. Expert Systems with Applications. 3. 135-141
  • Hopwood, M, McKeown, J, Mutchler, J. (1989). A test of the incremental explanatory power of opinions qualified for consistency and uncertainty. The Accounting Review. 64. 24-48
  • Joy, O, Tollefson, J. (1975). On the Financial Applications of Discriminant Analysis. Journal of Financial and Quantitative Analysis. 10. 723-739
  • Keasey, K, Watson, R. (1987). Non-Financial Symptoms and the Prediction of Small Company Failure: A Test of Argenti's Hypotheses. Journal of Business Finance and Accounting. 14. 335-354
  • Kim, S. (2011). Prediction of Hotel Bankruptcy Using Support Vector Machine, Artificial Neural Network, Logistic Regression, and Multivariate Discriminant Analysis. The Service Industries Journal. 31. 441-468
  • Koh, H.C. (1992). The Sensitivity of Optimal Cutoff Points to Misclassification Costs or Type I and Type II Errors in the Going-Concern Prediction Context. Journal of Business Finance and Accounting. 17. 187-197
  • Koh, H.C, Tan, S. (1999). A neural network approach to the prediction of going concern status. Accounting and Business Research. 29. 211-216
  • Laffarga, J, Martín, J. L, Vázquez, M. J. (1985). El Análisis de la Solvencia en las Instituciones Bancarias: Propuesta de una Metodología y Aplicaciones a la Banca Española. Esic Market. 51-73
  • Lam, K, Mensah, Y. (2006). Auditors' decision-making under going-concern uncertainties in low litigation-risk environments: Evidence from Hong Kong. Journal of Accounting and Public Policy. 25. 706-739
  • Lieber, Z, Orgler, Y. (1975). An Integrated Model for Accounts Receivable Management. Management Science. 22. 212-219
  • Lizarraga, F. (1997). Utilidad de la Información Contable en el Proceso de Fracaso: Análisis del Sector Industrial de la Mediana Empresa Española. Revista Española de Financiación y Contabilidad (REFC). XXVI. 871-915
  • López, D, Moreno, J, Rodríguez, P. (1994). Modelos de Previsión del Fracaso Empresarial: Aplicación a Entidades de Seguros en España. Esic Market. 84. 83-125
  • Martin, D. (1977). Early Warning of Bank Failure. Journal of Banking and Finance. 1. 249-276
  • McKee, T. (2003). Rough Sets Bankruptcy Prediction Models versus Auditor Signalling Rates. Journal of Forecasting. 22. 569-586
  • McKee, T, Lensberg, T. (2002). Genetic programming and rough sets: a hybrid approach to bankruptcy classification. European Journal of Operational Research. 138. 436-451
  • Messier, W, Hansen, J. (1988). Inducing rules for expert system development: an example using default and bankruptcy data. Management Science. 34. 1403-1415
  • Moizer, P. (1995). An Ethical Approach to the Choices Faced by Auditors. Critical Perspectives on Accounting. 415-431
  • Mora, A. (1994). Los Modelos de Predicción del Fracaso Empresarial: Una Aplicación Empírica del Logit. Revista Española de Financiación y Contabilidad. 78. 203-233
  • Mora, A. (1994). Limitaciones Metodológicas de los Trabajos Empíricos sobre la Predicción del Fracaso Empresarial. Revista Española de Financiación y Contabilidad. 80. 709-732
  • Moyer, R. C. (1977). Forecasting Financial Failure: A Re-examination. Financial Management. 6. 11-17
  • Nanda, S, Pendharkar, P. (2001). Linear Models for Minimizing Misclassification Costs in Bankruptcy Prediction. International Journal of Intelligent Systems in Accounting, Finance & Management. 10. 155168
  • Norton, C. L, Smith, R. E. (1979). A Comparison of General Price Level and Historical Cost Financial Statements in the Prediction of Bankruptcy. The Accounting Review. 72-87
  • O'Clock, P, Devine, K. (1995). An Investigation of Framing and Firm Size on the Auditor's Going Concern Decision. Accounting & Business Research. 25. 197-207
  • Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction Bankruptcy. Journal of Accounting Research. 18. 109-131
  • Palepu, K.G. (1986). Predicting Takeover Targets: A Methodological and Empirical Analysis. Journal of Accounting and Economics. 8. 3-35
  • Peel, M. J, Peel, D. A, Pope, P. F. (1986). Predicting Corporate Failure: Some Results for the UK Corporate Sector. Omega: The International Journal of Management Science. 14. 5-12
  • Pina, V. (1989). La Información Contable en la Predicción de la Crisis Bancaria 1977-1985. Revista Española de Financiación y Contabilidad (REFC). 18. 309-338
  • Pina, V. (1992). Estructura y Clasificación de las Ratios: Principio de Devengo vs. Flujos de Caja. Revista Española de Financiación y Contabilidad (REFC). 21. 9-27
  • Pindado, J, Rodrigues, L, de la Torre, C. (2008). Estimating financial distress likelihood. Journal of Business Research. 61. 995-1003
  • Piñeiro, C, De Llano, P, Rodríguez, M. (2012). ¿Proporciona la auditoría evidencias para detectar y evaluar tensiones financieras latentes?: Un diagnóstico comparativo mediante técnicas econométricas e inteligencia artificial. Revista Europea de Dirección y Economía de la Empresa (REDEE). 4. 1-78
  • Piñeiro, C, De Llano, P, Rodríguez, M. (2012). La evaluación de la probabilidad de fracaso financiero: Contraste empírico del contenido informational de la auditoría de cuentas. Revista Española de Financiación y Contabilidad (REFC). XLI. 565-588
  • Piñeiro, C, De Llano, P, Rodríguez. (2013). A parsimonious model to forecast financial distress, based on audit. Revista de Contaduría y Administración. 58. 151-173
  • Platt, H. D, Platt, M. B, Pedersen, J. G. (1994). Bankruptcy Discrimination With Real Variables. Journal of Business, Finance and Accounting. 21. 491-510
  • Pottier, S. (1998). Life insurer financial distress, Best's ratings and financial ratios. The Journal of Risk and Insurance. 65. 275-288
  • Ramírez, I. (1996). La Utilidad del Análisis Multivariante para Evaluar la Solvencia de las Pequeñas Empresas. X Congreso Nacional de AEDEM. Granada.463-473
  • Robinson, D. (2008). Auditor Independence and Auditor-Provided Tax Service: Evidence from Going-Concern Audit Opinions Prior to Bankruptcy Filings. Auditing: a Journal of Practice & Theory. 27. 31-54
  • Rodríguez, M. C. (1990). La Predicción de las Crisis Empresariales: Modelos para el Sector de Seguros. Universidad de Valladolid. Valladolid.
  • Rodríguez, J. M. (1989). Análisis de las Insolvencias Bancarias en España: Un Modelo Empírico. Moneda y Crédito. 189. 187-227
  • Rodríguez, M. (2002). La gestión del riesgo de crédito. AECA. Madrid. 73-114
  • Rose, P. S, Andrews, W. T, Giroux, G. A. (1982). Predicting Business Failure: A Macroeconomic Perspective. Journal of Accounting Auditing and Finance. 20-31
  • Ruiz, E, Gómez, N. (2007). Análisis empírico de los factores que explican la mejora de la opinión de auditoría: compra de opinión y mejora en las prácticas contables de la empresa. Revista Española de Financiación y Contabilidad. XXXVI. 317-350
  • Rumelhart, D, Hinton, G, Williams, R. (1986). Learning Representations by Back-Propagating Errors. Nature. 323. 533-536
  • Schwartz, K, Soo, B. (1995). An Analysis of form 8-K disclosures of auditor changes by firms approaching bankruptcy. Auditing: A journal of Practice & Theory. 14. 125-136
  • Serrano, C, Martín del Brio, B. (1993). Predicción de la Quiebra Bancaria Mediante el Empleo de Redes Neuronales Artificiales. Revista Española de Financiación y Contabilidad. 22. 153-176
  • Simunic, D. (1984). Auditing, consulting and auditor independence. Journal of Accounting Research. 22. 679-702
  • Slowinski, R, Zopounidis, C. (1995). Application of the rough set approach to evaluation of bankruptcy risk. International Journal of Intelligent Systems In Accounting, Finance & Management. 4. 27-41
  • Somoza, A. (2001). La Consideración de Factores Cualitativos, Macroeconómicos y Sectoriales en los Modelos de Predicción de la Insolvencia Empresarial: Su Aplicación al Sector Textil y Confección de Barcelona (1994-1997). Papeles de Economía Española. 89-90. 402-426
  • Sun, J, Li, H. (2009). Financial distress early warning based on group decision making. Computers & Operations Research. 36. 885-906
  • Venuti, E. (2004). The going-concern assumption revisited: assessing a company's future viability. The CPA Journal. 40-44
  • Wilkins, M. (1997). Technical default, auditors' decisions, and future financial distress. Accounting Horizons. 11. 40-48
  • Zhang, J, Mani, I. (2003). KNN approach to unbalanced data distributions: A case study involving information extraction. 20 ICML Workshop on Learning from Imbalanced Data Sets. Washington DC.
  • Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research. 59-82