Forecasting Financial Failure of Firms via Genetic Algorithms

Eduardo Acosta-González () and Fernando Fernández-Rodríguez ()

Computational Economics, 2014, vol. 43, issue 2, 133-157

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

Keywords: Financial failure; Financial distress; Bankruptcy; Genetic algorithms; Variable selection (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s10614-013-9392-9

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