Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis

Leila Bateni () and Farshid Asghari
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Leila Bateni: Islamic Azad University
Farshid Asghari: Islamic Azad University

Computational Economics, 2020, vol. 55, issue 1, No 13, 335-348

Abstract: Abstract One of important subjects for business and financial institutions in recent decades is bankruptcy prediction. In this study, we predict bankruptcy using both logit and genetic algorithm (GA) prediction techniques under sanctions circumstances. This study also compares the performance of bankruptcy prediction models by identifying conditions under which a model performs better to examine the relative performance of models, GA was used to classify 174 bankrupt and non-bankrupt Iranian firms listed in Tehran stock exchange for the period 2006–2014. Genetic model achieved 95 and 93.5 % accuracy rates in training and test samples, respectively; while logit model achieved only 77 and 75 % accuracy rates in training and test samples, respectively. The results suggest that two models have the capability of predicting bankruptcy and GA model is more accurate than the logit model in this regard.

Keywords: Genetic algorithm; Logit; Bankruptcy; Sanctions (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10614-016-9590-3

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