Comparison of Machine Learning Methods in Prediction of Financial Failure of Businesses in The Manufacturing Industry: Evidence from Borsa İstanbul

Abstract

In this study, Artificial Neural Networks (NN), C5.0 Classification Algorithm, Classification and Regression Trees (CART) analyses were used to predict the financial success/failure of 126 businesses that are operating in the BIST (Borsa İstanbul) Manufacturing Industry Sector. The data contains the years 2006 to 2009. In the study, 25 quantitative variable and 4 qualitative variable were used. The overall classification accuracy from the highest to the lowest of 3 years prior to successful-failure year (for 2006) is 84.21% for CART, 81.58% for ANN and 76.32% for C5.0, respectively. The overall classification accuracy from the highest to the lowest of 2 years prior to successful-failure year (for 2007) is 86.84% for CART, 84.21% for ANN, 78.95% for C5.0, respectively. The overall classification accuracy from the highest to the lowest of 1 year prior to successful-failure year (for 2008) is 92.11% for CART, 92.11 for ANN and 86.84% for C5.0, respectively. ANN and CART models are notable in terms of their ability to predict upcoming financial failure of unsuccessful businesses with 100% classification accuracy from a year ago. The prediction of the financial success/failure by the three models obtained in the study more than one, two and three years ago shows that the models used in this study can be included in the model used by those concerned.