Business failure prediction. A contribution to the synthesis of a theory, through comparative analysis of different prediction techniques

  • Pablo de Llano Grupo de Investigación en Finanzas y Sistemas de Información para la Gestión (FYSIG), Departamento de Economía Financiera y Contabilidad, Universidad de A Coruña.
  • Carlos Piñeiro Grupo de Investigación en Finanzas y Sistemas de Información para la Gestión (FYSIG), Departamento de Economía Financiera y Contabilidad, Universidad de A Coruña.
  • Manuel Rodríguez Grupo de Investigación en Finanzas y Sistemas de Información para la Gestión (FYSIG), Departamento de Economía Financiera y Contabilidad, Universidad de A Coruña.

Abstract

This paper offers a comparative analysis of the effectiveness of eight popular forecasting methods: univariate, linear, discriminate and logit regression; recursive partitioning, rough sets, artificial neural networks, and DEA. Our goals are: clarify the complexity-effectiveness balance of each methodology; identify a reduced set of independent variables that are significant predictors whatever the methodology is; and discuss and relate these findings to the financial theory, to help consolidate the foundations of a theory of financial failure. Our results indicate that, whatever the methodology is, reliable predictions can be made using four variables; these ratios convey information about profitability, financial structure, rotation, and operating cash flows.
Keywords Financial failure forecast, multivariate methods, artificial intelligence, machine learning
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How to Cite
Llano, P., Piñeiro, C., & Rodríguez, M. (2016). Business failure prediction. A contribution to the synthesis of a theory, through comparative analysis of different prediction techniques. Estudios de Economía, 43(2), pp. 163-198. Retrieved from https://estudiosdeeconomia.uchile.cl/index.php/EDE/article/view/44103/46116
Section
Articles
Published
2016-11-16