A COMPARATIVE STUDY OF GENETIC ALGORITHM AND NEURAL NETWORK MODEL IN BANKRUPTCY PREDICTION OF MANUFACTURING FIRMS IN NIGERIA

Authors

  • Egbunike, Francis Chinedu (PhD) Department of Accountancy, Faculty of Management Sciences, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.
  • Anachedo, Chima Kenneth Department of Banking and Finance, Faculty of Management Sciences, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.
  • Echekoba, Felix Nwaolisa (PhD) Department of Banking and Finance, Faculty of Management Sciences, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.
  • Ubesie, Cyril Madubuko (PhD) Department of Accountancy, Enugu State University of Science & Technology

Keywords:

Bankruptcy, Genetic Algorithm, Neural Network, Corporate Governance

Abstract

Previous studies have established the comparative accuracies of statistical failure models in Nigeria. However, the assumptions of these models often limit their practical application. The study, therefore, compares two models developed using AI techniques, the genetic algorithm (GA) and neural network on a sample of quoted manufacturing firms in Nigeria. This study adopts a quantitative approach and utilises a sample of sixty-six (66) companies listed on the Nigerian Stock Exchange (NSE), after excluding firms from the financial, natural, and oil & gas sectors. The study relied on secondary data from annual financial statements. The McNemar test was utilised to compare the accuracies of the two models. The model results showed a significant difference in the classification accuracies of the GA (96.94%; 97.85%) compared with the neural network (92.2%; 94.4%) models. In other words, the GA model outperformed the neural network model in corporate bankruptcy prediction. The inclusion of selected corporate governance variables also improved the accuracy of the models. The results demonstrate the practicality of using GA in a different context from prior western studies with different regulatory and institutional regimes.

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Published

25-04-2022

How to Cite

Egbunike, F. C., Anachedo, C. K., Echekoba, F. N., & Ubesie, C. M. (2022). A COMPARATIVE STUDY OF GENETIC ALGORITHM AND NEURAL NETWORK MODEL IN BANKRUPTCY PREDICTION OF MANUFACTURING FIRMS IN NIGERIA. Journal of Contemporary Issues in Accounting, 3(1), 231–271. Retrieved from https://journals.unizik.edu.ng/jocia/article/view/1200

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