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

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, ۱۴۴ companies were studied in the ۱۲ years of ۲۰۱۰-۲۰۲۱ 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:

Bankruptcy , Expert assessment , Black-Scholes Asset pricing model , Factors affecting bankruptcy , Bankruptcy prediction