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

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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.

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Authors and Affiliations

  1. Science and Research Branch, Department of Management and Economics, Islamic Azad University, Tehran, Iran

    Leila Bateni & Farshid Asghari

Authors
  1. Leila Bateni
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  2. Farshid Asghari
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Correspondence to Leila Bateni.

Appendices

Appendix 1: Variables Used in GA Molel

Revenue (REV)

Current assets (CA)

Depreciation

Total assets (TA)

Expense (EXP)

Current liabilities (CLIAB)

Income before taxes

Income after taxes

Net income (NETINC)

Taxes

Cash (CASH)

Operating equipment

EBIT

Total non-current liabilities

Interest

Receivables

Retained earnings (REARN)

Total liabilities (TLIAB)

Working capital

Total non-current assets

Sales

Equity (EQUITY)

Total deferred credits

 

Appendix 2: Variables Used in Logit Model

Variable definition

Abbreviation

Working capital/total assets

WC/TA

Retained earnings (loss)/total assets

RE/TA

Earning before interest and taxes/Total assets

EBIT/TA

Book value of equity/total assets

BOE/TA

Sales/total assets

SALES/TA

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Bateni, L., Asghari, F. Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis. Comput Econ 55, 335–348 (2020). https://doi.org/10.1007/s10614-016-9590-3

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