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

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  • fernández, María t. Tascón
  • gutiérrez, Francisco J. Castaño

Abstract

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.

Suggested Citation

  • fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    • Handle: RePEc:eee:spacre:v:15:y:2012:i:1:p:7-58
    DOI: 10.1016/S1138-4891(12)70037-7
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    1. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "Methodological comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the perspective of bankruptcy assessment," European Journal of Operational Research, Elsevier, vol. 199(2), pages 561-575, December.
    2. Carlos Serrano-Cinca, 1997. "Feedforward neural networks in the classification of financial information," The European Journal of Finance, Taylor & Francis Journals, vol. 3(3), pages 183-202.
    3. Platt, Harlan D. & Platt, Marjorie B., 1991. "A note on the use of industry-relative ratios in bankruptcy prediction," Journal of Banking & Finance, Elsevier, vol. 15(6), pages 1183-1194, December.
    4. Casey, C & Bartczak, N, 1985. "Using Operating Cash Flow Data To Predict Financial Distress - Some Extensions," Journal of Accounting Research, Wiley Blackwell, vol. 23(1), pages 384-401.
    5. Sinkey, Joseph F, Jr, 1975. "A Multivariate Statistical Analysis of the Characteristics of Problem Banks," Journal of Finance, American Finance Association, vol. 30(1), pages 21-36, March.
    6. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
    7. Lennox, Clive, 1999. "Identifying failing companies: a re-evaluation of the logit, probit and DA approaches," Journal of Economics and Business, Elsevier, vol. 51(4), pages 347-364, July.
    8. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    9. R. Slowinski & C. Zopounidis, 1995. "Application of the Rough Set Approach to Evaluation of Bankruptcy Risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(1), pages 27-41, March.
    10. Jones,Stewart & Hensher,David A. (ed.), 2008. "Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction," Cambridge Books, Cambridge University Press, number 9780521689540, October.
    11. Teija Laitinen & Maria Kankaanpaa, 1999. "Comparative analysis of failure prediction methods: the Finnish case," European Accounting Review, Taylor & Francis Journals, vol. 8(1), pages 67-92.
    12. Nico Dewaelheyns & Cynthia Van Hulle, 2006. "Corporate Failure Prediction Modeling: Distorted by Business Groups' Internal Capital Markets?," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(5‐6), pages 909-931, June.
    13. Becchetti, Leonardo & Sierra, Jaime, 2003. "Bankruptcy risk and productive efficiency in manufacturing firms," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2099-2120, November.
    14. Blum, M, 1974. "Failing Company Discriminant-Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 12(1), pages 1-25.
    15. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    16. Peel, MJ & Peel, DA & Pope, PF, 1986. "Predicting corporate failure-- Some results for the UK corporate sector," Omega, Elsevier, vol. 14(1), pages 5-12.
    17. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    18. Edmister, Robert O., 1972. "An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(2), pages 1477-1493, March.
    19. Edward Altman & Gabriele Sabato, 2005. "Effects of the New Basel Capital Accord on Bank Capital Requirements for SMEs," Journal of Financial Services Research, Springer;Western Finance Association, vol. 28(1), pages 15-42, October.
    20. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    21. Shrieves, Ronald E. & Stevens, Donald L., 1979. "Bankruptcy Avoidance as a Motive For Merger," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 14(3), pages 501-515, September.
    22. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    23. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    24. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    25. Cielen, Anja & Peeters, Ludo & Vanhoof, Koen, 2004. "Bankruptcy prediction using a data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 154(2), pages 526-532, April.
    26. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    27. Laitinen, Ek, 1993. "Financial predictors for different phases of the failure process," Omega, Elsevier, vol. 21(2), pages 215-228, March.
    28. Santomero, Anthony M. & Vinso, Joseph D., 1977. "Estimating the probability of failure for commercial banks and the banking system," Journal of Banking & Finance, Elsevier, vol. 1(2), pages 185-205, October.
    29. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    30. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    31. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    32. Harlan Platt & Marjorie Platt, 2002. "Predicting corporate financial distress: Reflections on choice-based sample bias," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 26(2), pages 184-199, June.
    33. Hian Koh & Sen Tan, 1999. "A neural network approach to the prediction of going concern status," Accounting and Business Research, Taylor & Francis Journals, vol. 29(3), pages 211-216.
    34. N. Dewaelheyns & C. Van Hulle, 2004. "The Impact of Business Groups on Bankruptcy Prediction Modeling," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(4), pages 623-645.
    35. Joseph Paradi & Mette Asmild & Paul Simak, 2004. "Using DEA and Worst Practice DEA in Credit Risk Evaluation," Journal of Productivity Analysis, Springer, vol. 21(2), pages 153-165, March.
    36. Cecilio Mar-Molinero & Carlos Serrano-Cinca, 2001. "Bank failure: a multidimensional scaling approach," The European Journal of Finance, Taylor & Francis Journals, vol. 7(2), pages 165-183.
    37. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "Can R&D expenditure avoid corporate bankruptcy? Comparison between Japanese machinery and electric equipment industries using DEA-discriminant analysis," European Journal of Operational Research, Elsevier, vol. 196(1), pages 289-311, July.
    38. Ketz, Je, 1978. "Effect Of General Price-Level Adjustments On The Predictive Ability Of Financial Ratios," Journal of Accounting Research, Wiley Blackwell, vol. 16, pages 273-284.
    39. Gentry, Ja & Newbold, P & Whitford, Dt, 1985. "Classifying Bankrupt Firms With Funds Flow Components," Journal of Accounting Research, Wiley Blackwell, vol. 23(1), pages 146-160.
    40. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
    41. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    42. Molinero, C Mar & Ezzamel, M, 1991. "Multidimensional scaling applied to corporate failure," Omega, Elsevier, vol. 19(4), pages 259-274.
    43. Gary Whalen, 1991. "A proportional hazards model of bank failure: an examination of its usefulness as an early warning tool," Economic Review, Federal Reserve Bank of Cleveland, vol. 27(Q I), pages 21-31.
    44. Grunert, Jens & Norden, Lars & Weber, Martin, 2005. "The role of non-financial factors in internal credit ratings," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 509-531, February.
    45. von Stein, Johann Heinrich & Ziegler, Werner, 1984. "The prognosis and surveillance of risks from commercial credit borrowers," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 249-268, June.
    46. Carlos Martínez Mongay & María Cruz Navarro & Fernando Sanz, 1989. "Selección y explotación de los sistemas de alarma y prevención de quiebra," Investigaciones Economicas, Fundación SEPI, vol. 13(3), pages 465-484, September.
    47. Marais, Ml & Patell, Jm & Wolfson, Ma, 1984. "The Experimental-Design Of Classification Models - An Application Of Recursive Partitioning And Bootstrapping To Commercial Bank Loan Classifications," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 87-114.
    48. Grice, John Stephen & Ingram, Robert W., 2001. "Tests of the generalizability of Altman's bankruptcy prediction model," Journal of Business Research, Elsevier, vol. 54(1), pages 53-61, October.
    49. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    50. Lincoln, Mervyn, 1984. "An empirical study of the usefulness of accounting ratios to describe levels of insolvency risk," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 321-340, June.
    51. David C. Wheelock & Paul W. Wilson, 2000. "Why do Banks Disappear? The Determinants of U.S. Bank Failures and Acquisitions," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 127-138, February.
    52. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    53. ANITA M. McGAHAN & MICHAEL E. PORTER, 1997. "How Much Does Industry Matter, Really?," Strategic Management Journal, Wiley Blackwell, vol. 18(S1), pages 15-30, July.
    54. McGurr, Paul T. & DeVaney, Sharon A., 1998. "Predicting Business Failure of Retail Firms: An Analysis Using Mixed Industry Models," Journal of Business Research, Elsevier, vol. 43(3), pages 169-176, November.
    55. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    56. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "DEA-DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry," European Journal of Operational Research, Elsevier, vol. 199(2), pages 576-594, December.
    57. Mensah, Ym, 1984. "An Examination Of The Stationarity Of Multivariate Bankruptcy Prediction Models - A Methodological Study," Journal of Accounting Research, Wiley Blackwell, vol. 22(1), pages 380-395.
    58. Lacher, R. C. & Coats, Pamela K. & Sharma, Shanker C. & Fant, L. Franklin, 1995. "A neural network for classifying the financial health of a firm," European Journal of Operational Research, Elsevier, vol. 85(1), pages 53-65, August.
    59. Dambolena, Ismael G & Khoury, Sarkis J, 1980. "Ratio Stability and Corporate Failure," Journal of Finance, American Finance Association, vol. 35(4), pages 1017-1026, September.
    60. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    61. Libby, R, 1975. "Accounting Ratios And Prediction Of Failure - Some Behavioral Evidence," Journal of Accounting Research, Wiley Blackwell, vol. 13(1), pages 150-161.
    62. Jones,Stewart & Hensher,David A. (ed.), 2008. "Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction," Cambridge Books, Cambridge University Press, number 9780521869287, October.
    63. Selwyn Piramuthu & Harish Ragavan & Michael J. Shaw, 1998. "Using Feature Construction to Improve the Performance of Neural Networks," Management Science, INFORMS, vol. 44(3), pages 416-430, March.
    64. Palepu, Krishna G., 1986. "Predicting takeover targets : A methodological and empirical analysis," Journal of Accounting and Economics, Elsevier, vol. 8(1), pages 3-35, March.
    65. Canbas, Serpil & Cabuk, Altan & Kilic, Suleyman Bilgin, 2005. "Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case," European Journal of Operational Research, Elsevier, vol. 166(2), pages 528-546, October.
    66. Collins, Robert A. & Green, Richard D., 1982. "Statistical methods for bankruptcy forecasting," Journal of Economics and Business, Elsevier, vol. 34(4), pages 349-354.
    67. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    68. William F. Messier, Jr. & James V. Hansen, 1988. "Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data," Management Science, INFORMS, vol. 34(12), pages 1403-1415, December.
    69. Westgaard, Sjur & van der Wijst, Nico, 2001. "Default probabilities in a corporate bank portfolio: A logistic model approach," European Journal of Operational Research, Elsevier, vol. 135(2), pages 338-349, December.
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    More about this item

    Keywords

    fracaso empresarial; quiebra; análisis de variables; ratios financieros; G33; L25; M41; business failure; bankruptcy; variable analysis; financial ratios; G33; L25; M41;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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