International Journal of Nonlinear Analysis and Applications

Bankruptcy prediction using the Black-Scholes asset pricing model (Experimental evidence: Tehran Stock Exchange)

Document Type : Research Paper

Authors

1 Department of Financial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Accounting, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Department of Law, Faculty of Theology and Political Sciences, Sciences and Research Branch, Islamic Azad University, Tehran, Iran

4 Department of Agricultural Economic, Faculty of Agricultural Sciences and Food Engineering, Sciences and Research Branch, Islamic Azad University, Tehran, Iran}

Abstract

The bankruptcy of companies is essential in financial literature, and the development of bankruptcy forecasting techniques and models is the priority of financial research goals. Many studies have been conducted on predicting the bankruptcy of companies. This study first used a combination of theoretical and expert analysis to determine the financial ratios and macroeconomic variables affecting bankruptcy. Thus, bankrupt companies were distinguished from non-bankrupt ones referring to Black and Scholes’s asset pricing models based on the intrinsic value of liabilities and assets. Therefore, 144 companies were studied in the 12 years of 2010-2021 in the screening process. The analysis of multilayer artificial neural networks for evaluating the reliability of the results in identifying the factors affecting the prediction of bankruptcy and prioritizing these factors showed that the least important factor was the ratio of capital to the net profit of the company and the most critical factor was the ratio of profit before interest and taxes to the total assets of the company.

Keywords

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International Journal of Nonlinear Analysis and Applications
Volume 15, Issue 5
May 2024
Pages 121-142
  • Receive Date: 04 February 2023
  • Revise Date: 28 March 2023
  • Accept Date: 07 May 2023