جستجوی مقالات مرتبط با کلیدواژه « Bankruptcy risk » در نشریات گروه « مالی »

In this paper, analysis of the sensitivity of the bankruptcy index to financial variables during the firm life cycle was investigated using Monte Carlo simulation. To measure bankruptcy risk, Altman's modified bankruptcy model was used. To simulate score ​​of Altman's modified model, the probability distribution of each index of Altman's modified model was determined based on the historical data of the listed firms listed in the Tehran Stock Exchange (TSE) during the years 2011-2021. With the help of these distributions, 10,000 items for the bankruptcy index were simulated in each life cycle. The results of simulations showed that the bankruptcy index had high and low sensitivities to the indicators of working capital to total assets (X1) and accumulated earnings to total assets (X2) compared to the indices of earnings before interest and taxes to total assets (X3), the book value of equity to book value of total liabilities (X4), and sales to total assets (X5) in all stages of the firm life cycle, except for the decline stage.

Financial analysts and other users need relevant and reliable information to predict corporate bankruptcy, which should be distributed symmetrically to all users. Accordingly, the purpose of this study is to compare the prediction accuracy of Artificial Neural Network (ANN) systems based on the Multilayer Perceptron Approach and Falmer Binary-Logistics Model in order to predict bankruptcy. To test the hypotheses, the combined data of 172 companies listed on the Tehran Stock Exchange in the period 2007-2016 were used. The results of the analysis of the research data show that the ANN system can identify of the factors affecting on bankruptcy of Iranian companies in the year before bankruptcy by Precision equal 98%. Findings from the binary-logistic model showed that the forecasting model designed based on the Falmer regression method is able to predict with 82% accuracy the bankruptcy of the sample companies. Therefore, the use of artificial neural networks can more powerfully and accurately predict bankruptcy than regression models.

The purpose of this study is to examine the relationship between bank profitability, bankruptcy and credit risk and selected bank sector variables. The study population includes banks accepted in Tehran Stock Exchange from 2009 to 2017. To test the research hypotheses, dynamic panel data model has been used. The results of hypotheses testing indicate that there is a negative and significant relationship between the asset returns of the previous year, cash assets to total assets and the exchange rate with the return on assets. There are positive and significant relationships between the capital adequacy rate as well as inflation rate and the rate of return on assets of banks, between both cash assets to total assets and exchange rate and the return on equity ratio; and vice versa between the economic growth rate as well as inflation rate and the rate of return on salaries. There is a negative and significant relationship between cash assets to total assets, exchange rate and inflation rate and banks' bankruptcy risk. There is a positive and significant relationship between the economic growth rate and banks' bankruptcy risk and there is a positive and significant relationship between credit risk of the previous year, capital adequacy and credit risk of banks, and ultimately there is a negative and significant relationship between net assets and credit risk of banks.

Accounting earnings and the continuous operation assumption are two principles of Ohlson’s (1995) prediction and valuation models. Regarding the influence of bankruptcy risk on these two factors, in this research, these models are adjusted by the inclusion of bankruptcy risk. As a result, the primary models are compared with adjusted models during a 5-year (from 2003 to 2008) and a 10-year period (from 2003 to 2013) using panel data of companies listed in the Tehran Stock Exchange. The results indicate that the inclusion of the bankruptcy risk improves Ohlson’s prediction and valuation models during both 5 and 10 years estimated periods. However, due to the uncontrolled growth of prices in 2012 and 2013, which resulted in the price bubble formation, the estimated values by both original and adjusted models during 10 years estimated period are significantly lower than the actual market values.

Based on the literature Review about the influence of bankruptcy risk on three principle factors of the abnormal earning valuation models (the going concern assumption, the accounting conservatism and the accounting earnings) in this research, ohlson (1995) and feltham-ohlson (1995) abnormal earning valuation models are adjusted considering the bankruptcy risk and primary models are compared with adjusted models during the 5-years period (from 2003 to 2008) and 10-years period (from 2003 to 2013) using panel data for 110 companies listed on Tehran Stock Exchange. The research results indicate that considering the bankruptcy risk improves the prediction and valuation power of the abnormal earning valuation models during both 5 and 10 years estimated periods. However, due to the uncontrolled growth of prices in the last years of the 10-years period (especially 2012 and 2013) the estimated values by both primary and adjusted models during 10-years estimated period are significantly lower than the actual market values.