نشریه تصمیم گیری و تحقیق در عملیات
سال چهارم شماره 2 (تابستان 1398)

The present paper presents the possibility of predicting firms' bankruptcy with Sprint, Altman, Fulmer, Zmijewski and Mckee Genetic models among companies in the Tehran Stock Exchange in a different way from previous research to introduce companies which have the potential for higher bankruptcy with a comparative approach among the models. To achieve this goal, 75 companies that are selected not covered base on 141 of the Commercial law. Required data for the 10 years (86-95) has been compiled. According to the results in each of the above models, some companies were identified as high-risk probability companies, and then companies that were identified as most likely to be bankrupt in most of these models. The results also show that, with the exception of Mckee model, in four other models, three companies with high bankruptcy probability were included. Among these four models, Zmijewski model has a higher coefficient of determination, hence we can say that relative to Other models have been more accurately predicted for bankruptcy and have a significant role in corporate bankruptcy among financial ratios, debt ratios, asset turnover, and asset returns.

Understanding behavioral accounting has a lot to do with studying human aspects of finance. This is because the research focuses on users of financial reports and their characteristics often assumes that financial information is in impartial, non-discriminatory and worthless. However, the information that investors and stakeholders in the capital market use to make economic decisions are provided by accountants who use their professional judgments when interpreting and applying accounting standards. The purpose of this paper is to examine the effect of accounting role on financial behavior. A questionnaire was used to infer the research hypotheses. This questionnaire was distributed among 288 investors in 1396. The main hypothesis of the relationship between the perception of accounting standards and financial behavior was investigated. Findings show that there is a positive and significant relationship between the perception of accounting standards and financial behavior.

One of the important issues that researchers and scientists face in the area of decision making and Predicting are choosing the variables that influence decision output and prediction. Therefore, if you can predict stock returns with appropriate variables and provide models for it, there will actually be more secure conditions in the capital market, which will help expand investment in financial markets. Therefore, the purpose of this paper is to examine the prediction of negative stock returns in Iran's capital market. In order to achieve this goal, data about 180 companies over the period 2010 to 2016 that was collected through a systematic elimination method that uses leverage, performance, turnover, volatility, quality, and Torpedo criteria to predict negative returns The results indicate that all of these criteria are all effective measures to predict the negative returns of shares in Tehran Stock Exchange.

The present research is aimed at predicting the beta coefficient (systematic risk) prediction dynamics within the framework of two macroeconomic structural models, the model in the context of dynamic stochastic general equilibrium (DSGE) and Panel Vector Autoregressive (PVAR) with the inclusion of financial data of companies and Some of the facts observed in the Iranian economy during the 15-year period (2002-2016). The results of the research show that economic shocks affect the beta coefficient of the stock. Also, in three approaches to predict stock beta coefficient, the VAR model has a lower error than the DSGE model. Finally, by comparing the moments of the present variables in the DSGE model and the real data of Iran's real moments, it shows the relative success of this model in the realities of Iran's economy.

Financial distress analysis is an essential phenomenon for financiers, creditors and those who use financial data. Predicting the possibility of a company’s distress is an interesting issue and is beneficial for managers, investors and creditors. This study localizes a method to identify the distressed companies in three levels, using the data of 1488 company from 1390 to 1395 and finally the financial distress for the next year and two years later is predicted by means of macroeconomic and accounting variable in the capital market of Iran by means of Matlab 2017, using the artificial intelligence algorithm of Gaussian kernel backup vector machine and Chide rule-oriented algorithm. One of the innovations of this study about the localization of the distress model in Iran using the worldwide and Iranian model together is using macroeconomic and accounting variables and artificial intelligence methods in three levels. The results of this study show that the non-linear algorithm for vector machine supporting the Gaussian kernel has more ability to predict the distress of companies, compared to the Chide rule-oriented algorithm.

Determining the best combination of corporate resources or capital structure is one of the most important issues that financial managers must consider maximizing shareholder wealth; combination of financial resources with characteristics such as low cost of capital can produce higher yields, and it claimed that optimal capital structure could increase the value of company and minimize the cost of capital. As regard to operational research (OR) research has played a decisive role in the development of financial theory and investment, this has led to a two-way relationship between finance and operational research techniques. As the various techniques of operational research have been used for financial affairs; therefore, financial theories require the development and improvement of the operational research solution techniques. Therefore, present study focus on application of operational research technique, which exist in structure.