FINANCIAL RESEARCH
Year:2017 | Volume:19 | Issue:1 |Papers Count:10

Reviewing trend and forecasting the debt ratio is one of the attractive topics for investors, managers, financial analysts and creditors. In this research, in order to forecasting debt ratio for listed companies in Tehran Stock Exchange, target leverage and speed of adjustment to the target leverage were used in four simulation models. first, The process and the stability of the capital structure were investigated on a seasonal period of 11 years from the first quarter of 1383 to the third quarter of 1393. The results indicate that, If the review period of stability regime is prolonge, sustainable regime will be less. Often, sustainable regime occured in debt ratios higher than 4.0. The results of the simulation indicate that Time-varying Target Ratios Models and Flexible Target Zone Models have best function in debt ratio simulation in real data.

This paper analyses whether joint probability distribution function of losses due to different exposures covered under the same policy could be modeled in an appropriate manner via mixture distribution proposed and copula concept.Special type of distribution which is a mixture of Generalized Hyperbolic Skew t distribution and Extreme Value theory (EVT) has been used for modeling marginal distributions of claims and copula function has been considered as a means of modeling dependency structure among claims. Most important copula including; Gaussian, t, Frank, Gumbel and Clayton was tested from goodness of fit point of view.The data used in this study are the amount of property damage and bodily injury covered under automobile liability insurance.Results reveal that joint probability distribution of claims could be effectively modeled by Clayton copula and proposed mixture distribution.

Cash adequacy in banks’ branches is considered as the significant issues for branch managers; because the daily cash shortage in branches’ funds might lead to the lack of fulfilling customers’ needs. On the other hand, cash surplus in branches will increase the expenses which arise from its transfer to the banks’ treasuries. Therefore, banks have always been attempting to estimate their required cash according to their daily operations and. In this regard, iIn this article, branches of Tejarat Bank, with regard to their diversity, have been classified in similar clusters with the two methods of hierarchical clustering and clustering based on Bayesian approach .Then, based on the results obtained from the clustering, the input cash to the branches as well as the cash consumption in the branches were estimated through the neural networks, which made it possible to calculate the required cash in branches. The results show that the estimation of input and consumed cash of branches using neural network and regarding the results obtained from Bayesian approach for branches clustering enjoys higher precision in comparison to the results obtained from the classic methods of clustering.

The purpose of this paper is to examine the impact of the determinants of risk-taking in the banking industry in iran with a special emphasis on ownership structure variable.. To do so, a panel data set of 18 banks over the periods of 2009- 2014 is being collected and used. Using panel data estimation methods, indicators associated in credit risk and stability is regressed on ownership structure variable and a set of other explanatory and controlling variables. The findings showed that in general banks with high ownership concentration are at higher risk. Also, the concentration of ownership in private banks caused to increase the risk and at the same time reduce the stability indicator while state-owned banks show similarly, an increase in the risk but with less intensity and an increase in the stability contrary to private banks.

Estimation of the return distribution has a crucial role in Risk measurement and since the precision of risk measures depends on the precision of the return distribution, truly estimation of return distribution has attracted a huge attention. Although using Stochastic Volatility models with parametric assumptions for estimation and illustration of the volatilities has been common in research, these assumptions usually result in careless estimations. So in the following research a semiparametric approach has been used for estimation of the volatility by using a normal mixture dirichlet process. In this paper the distribution of the logarithm of the squared returns of banking index of Tehran Stock Exchange has been estimated by using mixtures of normal family and employing an MCMC algorithm. Finally, the results has been compared to the Basic stochastic volatility model. The results show that when the return distribution is skewed, estimates of volatility using the model can differ dramatically from those using a Normal return distribution. Furthermore, when return distribution is similar to a normal distribution, the results of this model are similar to the results of the parametric model.

In this research, by using statistical data of 24 accepted investment companies on a monthly basis, and by using Lakonishok model (1992) for the period 1388-1394, impact of herding behavior on the performance of the companies based on modern and post modern portfolio theory is examined. In order to analyze data and examine the hypothesis, after reviewing the static of variables, has been used by Generalized Method of Moments (GMM) and Generalized Least Squares (EGLS) methods of estimations. Results indicate that herding behavior in investment companies’ significant negative impact on performance criteria based on the theories of modern and post modern portfolio. In other words, herding behavior is negatively affected on all performance measures Jensen, Sharp, Trainer, Sortino, good potential, Omega, Trainer adjusted and Jensen adjusted.

Todays, social entrepreneurship is recognized as a way of effective and sustainable economic and social development. So, this concept needs more attention on the society and the country level. However, financing is a strategic issues that social entrepreneurs are facing in launching and managing social businesses. The aims of this study is to identify the ways of financing social entrepreneurship. Methodology of the study is survey approach and the purpose is an applied research. Data was gathered by researcher made questionnaire that validity are confirmed by experts and reliability was proved by Alpha. Statistical population, are executives and social entrepreneurs in Tehran city. The sample included 120 active social entrepreneurs. Data analysis is done by descriptive statistics and confirmative factor analysis. The results showed that grants, in come, investment, and government support are the most important role in social entrepreneurship financing.

Financial distress prediction (FDP) is a great important subject that has always been interesting to researchers, financial institutions and banks. Tough many works have been done in this area, but use of combined approach of feature selection and classifier is an issue that has attracted researchers' attention just in recent years. In this paper, four well-known kinds of SVM that each of them has it's own kernel function including: linear, polynomial, radial and sigmoid have been introduced as the main classifiers of our proposed approach. These four methods have been integrated with genetic algorithm (GA) as a wrapper feature selection approach as well as three techniques of filtering feature selection approach called: principle component analysis (PCA), information gain and relief. Brought results indicated that genetic algorithm outperformed the other feature selection techniques in it's combination with SVM methods. Furthermore, implemented hypothesis test implied that there was no significance level among GA-SVM (linear), GA-SVM (radial), GA-SVM (polynomial) and GA-SVM (sigmoid) techniques with confidence level of %95.

In this paper portfolio selection problem with interval optimization approach is surveyed. CVaR is risk measure. CVaR is the expected loss depending on the chosen confidence level. Using CVaR makes the portfolio selection problem linear programming. Contribution of this paper is to consider mean expected interval; this development help portfolio selection problem to consider uncertainty. Interval optimization is modeling approach to consider parameters uncertainty in this paper. Considering uncertainty make model more realistic. The results of model show that this approach has computational efficiency and on the other hand proposed model produce better solution in risk and portfolio rate of return point of view.

Up to now, the effects of diversification of business portfolio has been a controversial topic. Models of the effects of diversification on performance are categorized in three groups: Value Enhancing Models, Inverted-U Models and Value-Destroying Models. Researches on this subject have not achieved maturity because They have not achieved consensus on a single conclusion and These researches have not achieved interpretable and stable results. Specially diversification of Iranian holding companies have not been considered yet. In this survey, we consider the impact of business portfolio diversification on financial performance and risk. 37 LLP conglomerates that are active in The Teheran Stock Market are considered during 1390 to 1394 (5 years). For considering validity of model and coefficients respectively the F test and T test is used. These tests are done by Eviews 9. In the 95% level of confidence, the conclusions shows that there isn’t a linear relationship between diversification with ROE and risk.