A Comparative Study of Genetic Algorithm and Neural Network Model in Bankruptcy Prediction of Manufacturing Firms in Nigeria

Egbunike, Francis Chinedu PhD 1
Department of Accountancy,
Nnamdi Azikiwe University
cf.egbunike@unizik.edu.ng
Anachedo, Chima Kenneth 2
Department of Banking and Finance,
Nnamdi Azikiwe University
ck.anachedo@unizik.edu.ng
Echekoba, Felix Nwaolisa PhD 3
Department of Banking and Finance,
Nnamdi Azikiwe University
fn.echekoba@unizik.edu.ng
Ubesie, Cyril Madubuko PhD 4
Department of Accountancy,
Enugu State University of Science & Technology
ubesiemadubuko@yahoo.com

The study compares predictive accuracies of two alternative bankruptcy forecasting models on a sample of Nigerian manufacturing firms. Specifically, the study compares model for bankruptcy prediction developed using Genetic Algorithm (GA) and Neural Network model. The authors utilized a purposive sample of sixty-six (66) companies listed on the Nigerian Stock Exchange (NSE) after excluding firms from financial services, natural resources, and oil & gas sectors. The study relied on secondary data from annual financial statements. The McNemar test was utilised to compare 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 different context from prior western studies with different regulatory and institutional regimes.
Keywords: Bankruptcy, Genetic Algorithm, Neural Network, Corporate Governance.

Download Full Text Here

Leave a Reply

Your email address will not be published.