Determination of Financial Failure Indicators by Gray Relational Analysis and Application of Data Envelopment Analysis and Logistic Regression Analysis in BIST 100 Index

Abstract

Financial failure prediction models have been developed by using Logistic Regression (LR) analysis from traditional statistical methods and Data Envelopment Analysis (DEA), which is a mathematically based nonparametric method over the financial reports of the companies traded in The Istanbul Stock Exchange National 100 Index (BIST 100) between the years 2014-2016. In the development of these models, the variables included in the model are as important as the method applied. For this reason, the gray relational analysis method has been considered in determining the indicators that affect the financial situation of the companies. As a result of the analysis, it was determined that the LR model, which is one of the prediction models, has a higher rate of prediction power than the data envelopment analysis in predicting the financial failure of the companies. However, DEA is also an easy and fast method for predicting financial failures, and is recommended to companies on the indicators that they need to improve in order to be successful. As a result of the study, it has been found that both methods are feasible in the prediction of financial failure, but these methods also have different advantages and disadvantages.