مجله مدیریت دارایی و تامین مالی
سال ششم شماره 4 (پیاپی 23، زمستان 1397)

Recently, banks and financial institutions have paid more and more attention to operational risk management. As Basel and other risk management frameworks have acknowledged, risk identification is considered as the first step of risk management process. Although estimating the risk of coverage capital is considered the ultimate goal of risk measurement and risk assessment, there is no doubt that risk management and control requires identification of the risks and factors causing them, as well as their causal mechanisms. In the present paper, a methodology was developed to map operational risks, risk factors, risk consequences and their causal links. Process based approach was used for risk mapping, and fuzzy cognitive map was introduced as risk mapping tool which is an expert based structuring technique and does not rely on operational risk data bases. Then, risk partitions were determined and finally a set of risk control policies were examined as three different scenarios.

This study investigates the influence of deficit and surplus and then the effect of industry characteristics, including industry concentration, industry munificence and industry dynamism on the capital structure adjustment. Results may be useful to distinguish main reasons of financing decisions in firms with same deficit/surplus and same industry characteristics. Listed companies in Tehran Stock Exchange were investigated from 2006 to 2016. Deficit and surplus, industry concentration and industry munificence have no effect on capital structure adjustments separately. In both high and low dynamism industries, firms tend to increase their debt. Firms with surplus and debt well below/above target are likely to reduce their debt compared to other firms. In high and low concentrated industries, firms with debt well below target are likely to reduce their debt. In high (low) munificent industries, firms with debt well below target, are more likely to adjust their debt downwards (upwards). Firms in less dynamic industries (More stable), whose debt is below the target, rapidly adjust their debt downwards.

Because of high information asymmetry, in the days before earnings announcements, stock trading is decreased. After checking it was found that the decrease in trading is asymmetric (imbalancing) and the decrease in purchases of shares in these days is more than of the sales decline the decision of traders to sell will lead to positive returns after the earnings announcement. Among the factors affecting the selling of shares, is increasing the cost of transactions. The contribution of this study was to define the new assumptions and investigating of profitability news and return variables in the form of simultaneous equations, that it was not any other reason. After reviewing data from 147 firms in Tehran Stock Exchange during the period of 2009-2014 was marked that profit news, investor attention and sell pressure variables have direct affect on return variable. As well as the relationship between investor attention and returns variables with profit news is direct, but the impact of selling pressure on profitability news is reversed.

Supply chain management association, in a comprehensive framework, defines supply chain as a strategic asset that can create value and consequently a competitive advantage for the organization This research aims at recognizing the formation and impact of supply chain complexity as an asset on its performance. The sub-goal of this research is to analyze the position of financial performance in the impact of supply chain complexity. Thus, after reviewing theoretical literature, the dimensions and terms of the model were identified and formulated as a reference model for supply chain processes. Then, a questionnaire was designed to assess the complexity of the supply chain and its impact on performance. CRONBACH alpha was used to assess the reliability and content, and construct validity was used to assess the validity. The statistical population of this research is home appliance companies. The statistical sample consists of 50 companies in the cities of Tehran, Isfahan, Tabriz, Mashhad, Shiraz and Ahvaz. In this research, 250 questionnaires were distributed in deferent layers of supply chain and 197 questionnaires were completed. Finally, by using Factor Analysis and Structural Equation Modeling method, proposed conceptual model was analyzed. Based on research findings, the supply chain complexity of the population has significant and negative impact on performance.

As banks’credit risk instability has a critical role on monetary and financial system, this study focuses on factors affecting both specific banking factors and macroeconomics factors on the credit risk of commercial banks in Iran. Based on Judgmental sampling method data of 9 independent and dependent variables was analyzed at the level of the commercial banks and the macroeconomics of the country by generalized method of moments (GMM) during the period from March 2009 to March 2016. The results show that the ratio of non-current facilities (NPL) of the past period, and the growth rate of GDP without oil with non-current facilities ratio (NPL) have a positive relationship with credit risk and bank's capital, the growth rate of oil revenue and credit growth, have a significant and negative relationship with credit risk. Also, bank size, inflation rate, and return on assets have a significant relationship with the credit risk criterion (NPL).

This research investigates the effect of financial information risk on firms' capital structure with agency approach from 2004 to end of 2013 in 87 firms listed in Tehran Stock Exchange. The conflicts of interests between managers and owners due to Information asymmetry, I ncrease the risk of financial information which is effective on capital structure. The pooled regression analysis is incorporated to test the hypotheses. The results show an increase in conflicts of interest between managers, the financing deficit increase and as a result, the financing of the debt increases, so there is a positive and significant relationship between the financing deficit and changes in capital structure. Also, the impact of financial information risk on the relationship between financing deficit and changes in capital structure is significant and negative. On the other hand, the impact of financial information risk on the relationship between financing deficit and changes in capital structure with intervention of asymmetry as a first agency proxy was confirm, but with the intervention of asymmetry as a second agency proxy was not confirm.

exchange rate as a source of uncertainty exposes investors to some kind of risk. When investors are exposure to exchange risk, it’s expected that investors expect higher return but some empirical evidences show that exchange risk is not priced. Since the exchange rate risk and stock returns are not correlated, we face some kind of anomaly that it is called “exposure risk” in the exchange-rate literature. Some evidences indicate, we can’t price exchange-rate risk because of using contemporaneous exchange-rate movements. This evidences state that persistent exchange-rate movements is a state variable and a financial distress risk and if we use persistent exchange-rate movements instead of contemporaneous exchange-rate movements then we can see that exchange-rate risk is priced. The purpose of this research is to test exchange-rate risk, as a state and financial distress risk variable, pricing in the Tehran security exchange to find a solution for exposure puzzle. In this research we calculate exchange-rate risk premium by “tracking portfolio” approach and then we test its pricing. For this purpose, we chose a sample with about 160 firms in Tehran security exchange in the period of 1384 to 1394. To survey our hypothesis, we use time series regression. The results show that persistent exchange-rate risk is priced in Tehran security exchange. So if we use persistent exchange-rate movements instead of contemporaneous exchange-rate movements, we can solve exposure puzzle.

Bankruptcy prediction in economic corporations is one of the financial branches that has become more prominent in the last two decades, so that the bankruptcy models have been developed and included variables of the macroeconomic environment and market conditions along with accounting variables. Since accounting approach is frequently used in Iran, comparing different approaches is of great importance. This study mainly aims to investigate the hazard model proposed by Campbell et al. (2008) in Iran's economic environment and compare it with the accounting model consisting of variables of the Ohlson's model (1980) and the Shumway's hazard model (2001) in prediction of bankruptcy. Figures of 241 Iranian corporations, admitted in Tehran stock Exchange between 2003 and 2015 have been used. In order to test the models, the logistic regression method was used and to compare the power of the models in prediction of bankruptcy, Receiver Operating Characteristic (ROC) curve analysis method was employed. The results show that the hazard model developed by Campbell et al. (2008) significantly predicts bankruptcy of non-financial corporations and is more accurate than the Ohlson's (2010) accounting model in predicting bankruptcy, But its difference with the Shumway model (2001) was not significant in the dimension of accuracy.

During last decades, financial markets have witnessed large losses due to their exposure to unexpected market crash. Resulting in these financial disasters, financial institutions, regulators and academics have developed intensive research to provide better measurement techniques and hedging tools. Value-at-Risk (VaR) is the most popular risk measure in the financial industry. In this paper Application Extreme Value Theory and long-memory to Stock Market in Iran (In Framework Model-GARCH) was Checked. We use same long-range memory GARCH-type models (FIAGARCH, HYGARCH and FIAPARCH) and EVT to forecast the financial market risk. Findings Indicated that The FIAPARCH-EVT approach performs better in predicting the one day ahead VaRs for different series studied.

The issue of portfolio selection has always been considered as one of the key issues in the field of investment. To select optimal portfolios, various models and methods have been represented since the initial presentation of the Markowitz approach. However, finding the most efficient model in portfolio selection has always been the subject of concern. Introducing a new model, called “the composition model of minimum-variance and N/1”, this paper aims to examine the efficiency of three different models of portfolio optimization. For this purpose, the performance of the composition model is compared with the sole minimum-variance model and the sole N/1 model. To evaluate the performance of the portfolios, some criteria such as Sharpe ratio, Trainer ratio, Modigliani and Modigliani ratio, Sortino ratio, and Information ratio have been applied. Finally, the TOPSIS multi-criteria decision-making method for ranking the research models has been used. The results indicate the superiority of the composition model over the two models applied solely.