International Journal of Nonlinear Analysis and Applications

Comparison of the combination model with the structural and accounting model in predicting the financial distress

Document Type : Research Paper

Authors

Department of Accounting, Faculty of Humanities, Urmia Branch, Islamic Azad University, Urmia, Iran

Abstract

The current research aims to investigate the power of financial distress prediction models while presenting a combination model, comparing the extracted model with the Merton model and the binary logistic regression model in predicting financial distress. In order to achieve the purpose of the research, the information of 168 distressed companies selected based on the specific criteria of distress and 168 healthy companies admitted to the Tehran Stock Exchange between 2006 and 2019 have been used. After reviewing past studies, 25 variables affecting financial distress, including 17 accounting variables, 4 market variables, and 4 macroeconomic variables, were identified, and by emphasizing the frequency and successful performance of these ratios in past studies and performing statistical tests, the final indicators were selected. To determine the dependent variable, Merton's model was used, and finally, by applying the logit model and determining the relationship between the independent variables and the dependent variable, a composite model was extracted. The research results showed that adding economic and stock market variables to financial variables does not increase the ability to predict financial distress and the combined model has better explanatory power than the Merton model and binary logistic regression. In the present research, to predict financial distress, all three categories of accounting, economic and stock market variables are considered together, and the emphasis is not only on accounting variables, and the combined model is compared with the accounting and market model.

Keywords

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International Journal of Nonlinear Analysis and Applications
Volume 15, Issue 5
May 2024
Pages 277-290
  • Receive Date: 26 February 2023
  • Revise Date: 24 April 2023
  • Accept Date: 02 May 2023