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Forecasting Financial Failure of Firms via Genetic Algorithms

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  • Eduardo Acosta-González
  • Fernando Fernández-Rodríguez

Abstract

Given a wide amount of possible ratios available for constructing a LOGIT model for forecasting bankruptcy, this paper provides a computational search methodology, only guided by data, for selecting the financial ratios employed in the model. This procedure is based on genetic algorithms which are used to explore the universe of models made available by all possible existing financial ratios (with very redundant information). This search process of the correct model is guided by the Schwarz information criterion. As an empirical illustration, the methodology is applied to forecasting the failure of firms in the Spanish building industry using annual public accounting information. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Eduardo Acosta-González & Fernando Fernández-Rodríguez, 2014. "Forecasting Financial Failure of Firms via Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 43(2), pages 133-157, February.
    • Handle: RePEc:kap:compec:v:43:y:2014:i:2:p:133-157
    DOI: 10.1007/s10614-013-9392-9
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    Cited by:

    1. Wei Xu & Yuchen Pan & Wenting Chen & Hongyong Fu, 2019. "Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine," Energies, MDPI, vol. 12(12), pages 1-20, June.
    2. Rémi Stellian & Jenny Paola Danna-Buitrago & David Andrés Londoño Bedoya, 2018. "Fragilidad financiera empresarial y expectativas de ingresos: evidencias de un modelo multi-agentes," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 37(73), February.

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