DEA as a business failure prediction toolApplication to the case of galician SMEs

  1. Pablo de Llano Monelos
  2. Carlos Piñeiro Sánchez
  3. Manuel Rodríguez López
Journal:
Contaduría y administración

ISSN: 0186-1042,  2448-8410

Year of publication: 2014

Volume: 59

Issue: 2

Pages: 65-96

Type: Article

DOI: 10.1016/S0186-1042(14)71255-0 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Contaduría y administración

Sustainable development goals

Abstract

In the research group we are working to provide further empirical evidence on the business failure forecast. Complex fitting modelling; the study of variables such as the audit impact on business failure; the treatment of traditional variables and ratios have led us to determine a starting point based on a reference mathematical model. In this regard, we have restricted the field of study to non-financial galician SMEs in order to develop a model¹ to diagnose and forecast business failure. We have developed models based on relevant financial variables from the perspective of the financial logic, voltage and financial failure, applying three methods of analysis: discriminant, logit and multivariate linear. Finally, we have closed the first cycle using mathematical programming -DEA or Data Envelopment Analysis- to support the failure forecast. The simultaneous use of models was intended to compare their respective conclusions and to look for inter-relations. We can say that the resulting models are satisfactory on the basis of their capacity for prediction. Nevertheless, DEA contains significant points of criticism regarding its applicability to business failure.

Bibliographic References

  • Adler, N., Friedman, L., Sinuany-Stern, Z.. (2002). Review of Ranking in the Data Envelopment Analysis Context. European Journal of Operational Research. 249-265
  • Altman, E.. (1968). Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy. Journal of Finance. 589-609
  • Altman, E.. (2000). Predicting Financial Distress of Companies: Revisiting the Z-Score and ZETA© Models. NYU Salomon Center.
  • Altman, E., 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.. (2010). A Simple Empirical Model of Equity-Implied Probabilities of Default.
  • Barr, R. S., Seiford, L. M., Siems, T. F.. (1993). An Envelopment-Analysis Approach to Measuring the Management Quality of Banks. Annals of Operations Research. 1-13
  • Beaver, W. H.. (1966). Financial ratios as predictors of failure. Empirical Research in Accounting from. Journal of accountant Research. 71-111
  • Bell, T. B., Ribar, G. S., Verchio, J.. (1990). X Auditing Symposium Deloitte & Touche. Symposium on Auditing Problems. Kansas. 29-53
  • Blay, A.. (2005). Independence threats, litigation risk, and the auditor's decision process. Contemporary Accounting Research. 22. 759-789
  • 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
  • Charnes, A., Cooper, W.W., Rhodes, E.. (1978). Measuring the efficiency of decision making units. European Journal of Operations Research. 429-444
  • Charnes, A., Cooper, W.W., Golany, B., Seiford, L.. (1985). Foundations of Data Envelopment Analysis form Pareto-Koopmans Efficient Empirical Production Functions. Journal of Econometrics. 91-107
  • Chen, Y., Morita, H., Zhu, J.. (2003). Multiplier Bounds in DEA via Strong Complementary Slackness Condition Solution. International Journal of Production Economics. 11-19
  • Chen, Y., Liang, L., Xie, J.. (2012). DEA model for Extended Two-stageNetwork Structures. Omega. 611-618
  • Cook, W.D., Seiford, L.M.. (2009). Data Envelopment Analysis (DEA) - Thirty Years On. European Journal of Operation Research. 1-17
  • Dubois, D., Prade, H.. (1992). Putting rough sets and fuzzy sets together. In Intelligent Decision Support. Handbook of Applications and Advances in Rough Set Theory, Kluwer Academic. Dordrecht. 203-232
  • Frydman, H., Altman, E. I., Kao, D. L.. (1985). Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress. The Journal of Finance. 40. 269-291
  • Hansen, J., Messier, W.. (1991). Artificial neural networks: foundations and application to a decision problem. Expert Systems with Applications. 135-141
  • Härdle, W., Moro, R., Schäfer, D.. (2005). Predicting Bankruptcy with Support Vector Machines.
  • 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
  • Koh, H., Tan, S.. (1999). A neural network approach to the prediction of going concern status. Accounting and Business Research. 29. 211-216
  • Liu, F.F., Chen, C.L.. (2009). The Worst-Practice DEA Model with Slack-Based Measurement. Computers & Industrial Engineering. 496-505
  • Llano, P. de, Piñeiro, C.. (2011). Elementos fundamentales de dirección financiera. Andavira Editorial.
  • Llano, P. de. (2011). Contraste de los modelos de pronóstico del fallo empresarial en las pymes sanas gallegas. XXV Congreso de AEDEM. Valencia.
  • Llano, P. de. (2011). Modelos de pronóstico del fallo empresarial en las Pymes sanas gallegas. XXV Congreso de AEDEM. Valencia.
  • Llano, P. de. (2011). A model to forecast financial failure, in non-financial Galician SMEs. XII Iberian-Italian Congress of Financial and Actuarial Mathematics. Lisbon.
  • Martin, D.. (1977). Early Warning of Bank Failure: a Logit regression approach. Journal of Banking and Finance. 1. 249-276
  • Matsumura, E., Subramanyam, K., Tucker, R.. (1997). Strategic auditor behaviour and going-concern decisions. Journal of Business Finance and Accounting. 77-759
  • McKee, T., Lensberg, T.. (2002). Genetic programming and rough sets: a hybrid approach to bankruptcy classification. European Journal of Operational Research. 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
  • Moyer, R. C.. (1977). Forecasting Financial Failure: A Reexamination. Financial Management. 6. 11-17
  • Ohlson, J.. (1980). Financial Ratios and Probabilistic Prediction of Bankruptcy. Journal of Accounting Research. 18.
  • Otto, W.. (2010). La alta dirección a examen.
  • 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
  • Pindado, J., Rodrigues, R., de la Torre, C.. (2008). Estimating Financial Distress Likelihood. Journal of Business Research. 995-1003
  • Piñeiro, C., de Llano, P., Rodríguez, M.. (2011). Fracaso empresarial y auditoría de cuentas. XXV Congreso de AEDEM. Valencia.
  • Premachandra, I.M., Bhabra, G.S., Sueyoshi, T.. (2009). DEA as a tool for bankruptcy assessment: a comparative study with logistic regression technique. European Journal of Operational Research. 412-424
  • Retzlaff-Roberts, D.L.. (1996). Relating Discriminant Analysis and Data Envelopment Analysis to One Another. Computers Operations Research. 23. 311-322
  • 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., de Llano, P., Piñeiro, C.. (2010). Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence. International Conference on Applied Business & Economics (ICABE 2010). A Coruña.
  • Rodríguez, M.. (2010). Contraste de la capacidad predictiva de la opinión técnica de auditoría frente a modelos paramétricos multivariantes de predicción de insolvencia y fracaso empresarial. XIV Encuentro ASEPUC.
  • Rose, P. S., Andrews, W. T., Giroux, G. A.. Journal of Accounting Auditing and Finance. Fall. 20-31
  • Ruiz, E., Gómez, N.. (2001). 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
  • Schwartz, K., Menon, K.. (1985). Auditor switches by failing firms. Accounting Review. 248-261
  • 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, B.. (1993). Predicción de la quiebra bancaria mediante el empleo de redes neuronales artificiales. Revista Española de Financiación y Contabilidad. 22. 153-176
  • Shetty, U., Pakkala, T.P.M., Mallikarjunappa, T.. (2012). A Modified Directional Distance Formulation of DEA to Assess Bankruptcy: An Application to IT/ITES Companies in India. Expert Systems with Applications. 1988-1997
  • Shin, K., Lee, T., Kim, H.. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications. 127-135
  • Sinauny-Stern, Z., Friedman, L.. (1998). DEA and the Discriminant Analysis of Ratios for Ranking Units. European Journal of Operational Research. 470-478
  • 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
  • Sueyoshi, T., Goto, M.. (2009). Can R&D Expenditure Avoid Corporate Bankruptcy? Comparison Between Japanese Machinery and Electric Equipment Industries Usin DEA-Discriminant Analysis. European Journal of Operation Research. 289-311
  • Sueyoshi, T.. (2009). Methodological Comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the Perspective of Bankruptcy Assessment. European Journal of Operation Research. 561-575
  • Sueyoshi, T.. (2009). DEA-DA for Bankruptcy-based Performance Assessment: Misclassification Analysis of Japanese Construction Industry. European Journal of Operation Research. 576-594
  • Sueyoshi, T.. (2011). A combined use of DEA (Data Envelopment Analysis) with Strong Complementary Slackness Condition and DEA-DA (Discriminant Analysis). Applied Mathematics Letters. 1051-1056
  • Sueyoshi, T.. (2011). Efficiency-based rank assessment for electric power industry: A combined use of Data Envelopment Analysis (DEA) and DEA-Discriminant Analysis (DA). Power System, IEEE Transactions on Energy Economic. 19. 919-925
  • Sun, J., Li, H.. (2009). Financial distress early warning based on group decision making. Computers & Operations Research. 885-906
  • Troutt, M.D., Arun, R., Aimao, Z.. (1996). The Potential use of DEA for Credit Applicant Acceptance Systems. Computer Operations Research. Elsevier Science Ltd. 23. 405-408
  • Tucker, R. R., Matsumura, E. M.. (1998). Going concern judgments: an economic perspective. Behavioral Research in Accounting. 10. 197-218
  • Warner, J.. (1977). Bankrupting costs; some evidence. Journal of Finance. 337-347
  • Zmijewski, M. E.. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research. 59-82
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