mohammadjavad sadehvand

Financial distress, which is defined as the uncertainty about the company's ability to meet its obligations and repay its debts, has been estimated by different models divided into three groups of fundamental models (based on accounting or financial data), structural models (based on the company's capital structure or market information) and hybrid models.  Accurately predicting financial distress is still a major point of challenge for financial researchers. Scholars acknowledge that financial distress will be experienced when it happens. Therefore, the best thing to do is to initially estimate the probability of a company's financial distress. In this regard, in the current study, first, a hybrid model was presented to investigate the ability of financial distress prediction models. Next, in order to compare the hybrid model with accounting-based models, the second version of Altman's Z model known as the Z˝ model was used. To compare the hybrid model with market-based models, Merton's model was used in three groups including healthy, distressing, and distressed companies.

In this research, by reviewing past studies, 47 variables affecting financial distress, such as accounting variables, market variables, and macroeconomic indicators were identified. Afterward, considering the frequency and successful performance of these variables in past studies, 19 variables were selected. In the next step, using the Stepwise regression test, among the 19 variables, 10 variables with probability values ​​smaller than 0.05 were chosen. Also, to determine the dependent variable, the European option pricing model (Merton's model) was used. Finally, by the use of the Multinomial logit model and identifying the relationship between the dependent and independent variables, the hybrid model for predicting financial distress was designed. In order to compare the produced hybrid model with accounting-based fundamental models, the second version of Altman's Z model known as the Z˝ model was used. To compare the hybrid model with market-based structural models, Merton's model was used. Moreover, in order to test the ability of financial distress prediction models, a sample including 100 companies listed on the Tehran Stock Exchange (TSE) or Iran FaraBourse (IFB) was selected. Then, considering the defined criteria, these companies were divided into three groups consisting of healthy, distressing, and distressed companies. Finally, the ability of the above-mentioned models in predicting financial distress was investigated.

Research findings indicated that in the hybrid model, the ratios of Net Working Capital to Total Assets (WCTA), Operating Cash Flow to Total Assets (OCTA), Sales to Total Assets (STA), Net Income to Total Assets (NITA), Short-term and Long-term Debts to Equity (TLTE), Price to Earnings Per Share (P/E) and Price to Sales (P/S), and the variable of Interest Rate (INT) had significant relations with company's financial distress probability. Also, a comparison of the hybrid model and conventional models revealed that in the group of financially distressed companies, respectively, the Z˝ model with 100% accuracy, Merton's model with 85% accuracy, and the hybrid model with 90% accuracy had correctly predicted the financial situation of the companies. While, in the group of financially distressing companies, the accuracy of the Z˝ model, Merton's model, and the hybrid model in predicting the financial situation of the companies, stood at 50%, 85%, and 85%, respectively. In addition, in the group of healthy companies, these models were able to correctly predict 95%, 85%, and 90% of the companies' financial situation, respectively.

According to achieved results, the Z˝ model has higher predictive power on healthy and distressed companies, compared to the hybrid and Merton models. While, the hybrid and Merton models are better at predicting the financial situation of distressing companies than the Z˝ model. Therefore, considering that the performance of the market-based model of Merton in predicting the financial situation of the companies is weaker than those of the Z˝ and that the hybrid models which are mainly formed by financial or accounting ratios, and also in regard to the findings of past studies which proved the inefficiency of the stock market in Iran, it can be concluded that it is better to use accounting variables in future research in the field of predicting financial distress.

Corporate financial distress is the most unpleasant event that will result in catastrophic issues for its stakeholders. In addition to the huge losses for the business itself, this event can potentially affect the country's economy. Therefore, quick and timely detection of financial distress is essential to support various financial and social investments. In this regard, the present study aimed at providing a combined model of corporate default prediction and classifying firms into three groups: healthy, stressed and distressed. In this study, first, using a library research method, 47 variables or ratios were identified, selected and classified into three groups: fundamental or financial variables, market variables and macroeconomic variables. Then, considering the frequency and successful performance of these ratios in previous studies and by performing statistical tests, potential variables affecting financial distress were identified. In this study, multinomial logistic regression was used to provide a combined model of corporate default prediction. Also, in order to measure corporate default, the Black-Scholes-Merton (BSM) model was used. Findings indicated that 8 variables, including 5 financial variables, 2 market variables and 1 macroeconomic variable were statistically significant in the final model, and in fiscal year 1398, the accuracy of this model was 90% in the group of distressed firms, 85% in the group of stressed firms and 90% in the group of healthy firms.

This paper is to study the relationship between momentum effect (continuation of prior returns) and financial distress risk using data from companies listed on Tehran stock exchange during 31/01/1387-31/04/1393. In this research, financial distress risk was calculated by the second version of Altman Z-Score model. To describe momentum effect, by determining the formation period to be 6 months, and the holding period to be 3, 6, or 12 months, we firstly examined the profitability of short term (3/6), midterm (6/6), and long term (12/6) momentum strategies and found that during abovementioned time period, only midterm momentum strategy was profitable. Then, we showed that momentum anomaly was driven by market under-reaction to financial distress risk. In other words, it can be said that momentum is proxying for distress risk, and is largely subsumed by distress risk factor.

Using data acquired from companies listed on Tehran stock exchange, during a five-year time period from 1388 to 1392, this study documents the impacts of economic value added (EVA), EVA spread, EVA momentum, and return on assets (ROA) on stock return as a measure of investors’ wealth creation. EVA spread and EVA momentum are new methods of firms’ performance measurement constructed on the basis of EVA. EVA and ROA are the traditional methods of performance measurement widely used currently. EVA spread and EVA momentum are argued to have better ability in explaining the variance of stock return compared to the two traditional methods. To test hypotheses, this study applies Multiple Linear Regression Model. The results indicate that EVA spread, EVA momentum, and ROA have significant effects on stock return, while the relationship between EVA and stock return is not significant. The regression coefficients of EVA spread and ROA are positive, but, it is found that the relationship between EVA momentum and stock return is negative.

Securitization has the potential to improve market efficiency and capital utilization in the insurance industry. Through selling risk to investors, insurers can reduce their capital requirements, and increase ability for the acquisition of new businesses. Also, in this way, they can reduce cost of capital, and enhance return on equity.
Securitization offers insurers the opportunity to unlock the embedded profits in blocks of insurance presently carried on balance sheet and to provide an alternative source of financing in an industry where traditional financing mechanisms are often restricted due to regulation.
Securitized transactions also permit insurers to achieve liquidity goals and can add transparency to many on-balance sheet assets and liabilities traditionally characterized by illiquidity, complexity, and informational opacity.