Factores determinantes de las quiebras en microempresas

  1. Vázquez Cueto, María José 1
  2. Irimia-Diéguez, Ana 1
  3. Blanco Oliver, Antonio José 1
  1. 1 Universidad de Sevilla
    info

    Universidad de Sevilla

    Sevilla, España

    ROR https://ror.org/03yxnpp24

Revista:
Anales de ASEPUMA

ISSN: 2171-892X

Ano de publicación: 2014

Número: 22

Tipo: Artigo

Outras publicacións en: Anales de ASEPUMA

Resumo

The small and medium enterprises are one of the main drivers of European economies. A large percentage consists of microenterprises that generate the most part of employment. Today this business segment is suffering the financial crisis, with the consequent increase in the rate of destruction of the same. So develop specific models for these bankruptcy and identify variables with greater explanatory power is challenging. So this study is becoming a pioneering work in this field in both the methodology used and the sector to which it applies, which has a higher opacity. Based on financial and non-financial variables that have been used with relative success in predicting bankruptcy in general, we try to determine which ones are affecting more to microfirms. We use a nonparametric learning technique based on the rough sets, which apply to a sample of UK firms, balanced on its failed situation and its familiar character, which determines the results.

Referencias bibliográficas

  • Altman, E.I. (1968). “Financial ratios, discriminant analysis and prediction of corporate bankruptcy”. Journal of Finance, 23(4), pp 589–609.
  • Altman, E.I., & Sabato, G. (2007). “Modeling Credit Risk for SMEs: Evidence from US” Market ABACUS, 43(3), pp 332-357.
  • Altman, E.I., Sabato, G. & Wilson, N. (2010). “The Value of Non-financial Information in Small and Medium-sized Enterprise Risk Management”. Journal of Credit Risk, 6(2), pp 95-127.
  • Beaver, W. (1966). “Financial Ratios as Predictors of Failure, Empirical Research in Accounting: Selected Studied”. Journal of Accounting Research, 4, pp 71-111.
  • Berger, A. N. and Frame, W. S. (2007). “Small Business Credit Scoring and Credit Availability”. Journal of Small Business Management, 45, pp 5–22.
  • Blanco Oliver, A.J. “Predicción de la insolvencia en microentidades mediante analisis logit y redes neuronales artificiales”. Tesis Doctoral. Sevilla, 2012.
  • Blum, M. (1974).”Failing company discriminant analysis”. Journal of Accounting Research, 12, pp 1-25.
  • Carter, R. & Auken, H. V. (2006). “Small Firm Bankruptcy”. Journal of Small Business Management, 44: 493–512.
  • Chen, S., Härdle, W.K. & Moro, R.A. (2011). “Modeling default risk with support vector machines”. Quantitative Finance, 11(1), pp 135-154.
  • Ciampi F. & Gordini, N. (2013). “Small Enterprise Default Prediction Modeling through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises”. Journal of Small Business Management, 51 (1): 23-45.
  • Citron, D., Wright, M. (2008).” Bankruptcy costs, leverage and multiple secured creditors: the case of managemnet buy-outs”. Accounting and Business Research, 38(1), pp 781-799.
  • Claessens, S., Djankov, S., & Lang, L. (2000). “The separation of ownership and control in east Asian corporations”. Journal of Financial Economics, 58, pp 81-112.
  • Deakin, E. B. (1972). “A discriminant analysis of predictors of business failure”. Journal of Accounting Research, 10(1), pp 167-179.
  • Dietsch, M., & Petey, J. (2004). “Should SME Exposures be treated as Retail or Corporate Exposures? A Comparative Analysis of Default Probabilities and Asset Correlation in French and German SMEs”. Journal of Banking and Finance, 28(4), pp 773-788.
  • Dimitras, A., Zanakis S., & 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.
  • Edmister, R., (1972). “An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction”. Journal of Financial and Quantitative Analysis, 7(2), pp 1477-1493.
  • Fan, J., y Wong, T. (2001).”Corporate ownership structure and the informativeness of accounting earnings in East Asia”. Journal of Accounting and Economics, 33, pp 401-426.
  • Fletcher, D., & Goss, E. (1993).”Forecasting with neural networks: An application using bankruptcy data”. Information and Management, 24(3), pp 159-167.
  • Gómez-Domínguez, D.; Vazquez-Cueto, M. J. (2013) “Utilidad de la metodología de los conjuntos imprecisos en la elaboración de señales de alerta temprana de crisis financieras” . Revista Análisis Financiero, 123, pp 76-87
  • Gorzalczany, M.B., y Piasta, Z. (1999). “Neuro-Fuzzy approach versus rough-set inspired methodology for intelligent decision support”. Information Sciences 120, pp 45-68
  • Greco, S., Matarazzo, B., Slowinski, R., (1998). “A new rough set approach to evaluation of bankruptcy risk”. In: Zopounidis, C. (Ed.), Operational Tools in the Management of Financial Risks. Kluwer Academic Publishers, Dordrecht, pp. 121–136.
  • Greco, S., Matarazzo, B. y Slowinski, R. (2002) “Rough sets methodology forsorting problems in presence of multiple attributes and criteria”. European Journal of Operational Research 138, pp 247–259. Hudson, J. (1987). “The Age, Regional and Industrial Structure of Company Liquidations”.Journal of Business Finance and Accounting, 14 (2), pp 199-213.
  • Jacobson, T., Kindell, R., Linde, J., & Roszbach, K. (2008). “Firm Default and Aggregate Fluctuations”. Working Paper, Sveriges Riskbank, 226.
  • Leshno, M., & Spector, Y. (1996). “Neural network prediction analysis: The bankruptcy case”. Neurocomputing, 10(2), pp 125-147.
  • Liou, D.K., & Smith, M. (2006). “Macroeconomic Variables in the Identification of Financial Distress”, Working Paper, May.
  • McKee, T. (2000). ”Developing a Bankruptcy Prediction Model via Rough Sets Theory” International Journal of Intelligent Systems in Accounting, Finance & Management, 9, pp 159-173
  • McPherson, M.A. (1996).”Growth of micro and small enterprises in southern Africa” Journal of Development Economics, 48, pp 253-277.
  • Mead, D.C y Liedholm, C. (1998).”The Dynamics of Micro and Small Enterprises in Developing Countries”. World Development, 26, pp 161-74. Elsevier.
  • Miller, K., Griffiths, T., and Jordan, M. (2009).”Nonparametric latent feature models for link prediction”. In NIPS.
  • Morten B., Nielsen, K. M., Perez-Gonzalez, F., & Wolfenzon, D. (2007). “Inside the Family Firm: The Role of Families in Succession Decisions and Performance”. The Quarterly Journal of Economics, 122(2), pp 647-691.
  • Mosqueda Almanza, R. M. (2010). “Falibilidad del método Rough Set en la conformación de modelos índice de riesgo dinámico en la predicción del fracaso”. Journal of Economics, Finance and Administrative Science, 15 (28), pp 65-87.
  • Myers, S. (1977). “Determinants of corporate borrowing”. Journal of Financial Economics, 5(2), pp 147-175.
  • Ohlson, J. A. (1980). “Financial ratios and the probabilistic prediction of bankruptcy”. Journal of Accounting Research, 18(1), pp 109-131.
  • O´Leary, D. (1995).On the history of AI applications. II. IEEE Conference on Artificial Intelligence Applications IEEE Expert, 10(1), pp 61-65
  • Pawlak, Z. (1982).”Rough sets”. International Journal of Information and computer Sciences 11, pp 341-356.
  • Pawlak, Z., Grzymala-Busse, J.W., Slowinski, R. y Ziarko, W. (1995). “Rough Sets”. Communications of the ACM, 38 (11), pp 89-95.
  • Ravi Kumar, P., & 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.
  • Samaniego Medina, R.,Vázquez-Cueto, M. J.(2013).”Modeling credit risk: an application of the rough set methodology”. International Journal Of Banking And Finance,10(1)
  • 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, pp 27-41
  • Taffler, R. J. (1984). “Empirical Models for the Monitoring of UK Corporations”. Journal of Banking and Finance, 8(2), pp 199-227.
  • Tay, F. E. & SHEN, L. (2002). “Economic and financial prediction using rough set model European”. Journal of Operational Research, 129, 141, pp 641-659.
  • Tsukuda, J., & Baba, S. (1994). “Prediction Japanese corporate bankruptcy in terms of financial data using neural networks”. Computers and Industrial Engineering, 27(1-4), pp 445-448.
  • Tsumoto, S. (1998). “Automated induction of medical expert system rules from clinical databases based on rough set theory”. Information Sciences, 112, pp 67-84.
  • Zmijewski, M. E. (1984). “Methodological Issues Related to the Estimation of Financial Distress Prediction Models”. Journal of Accounting Research, 22, pp 59-82.