FINANCIAL RESEARCH
Year:2017 | Volume:19 | Issue:2 |Papers Count:9

One of the main concerns of investors and financial managers is the way of dealing with investment risk; thus identification, calculation and management of risk are important issues in financial fields. So, in this study, the portfolio value-at-risk and expected shortfall are estimated by considering uncertainty in risk factors. The concept of fuzzy random variable, specifically possibility and necessity theory, is used to face uncertainty in financial data. In the following, the terms of fuzzy value-at-risk and expected shortfall introduced with assuming normal and t-student distribution and considering both state of fixed and stochastic for uncertainty factor. The results indicate that assumptions of t-student distribution and stochastic uncertainty factor make the estimation of both risk measures to be more conservative.

Risk and return of active mutual funds in Iran capital market. We apply multiple regression analysis, correlogram tests and GARCH model between 2008 – 2014 years to test the data. Findings show that there are some positive and negative relationships in return and risk of some mutual funds, however it is not generalized to all mutual funds as a regularity or behavior. In other words, there is no irrational trend in mutual funds’ risk and return. According to the results, there is no irrational return which has significant effect on all mutual funds in the sample of this study. Therefore, the public opinion including a decrease in return of mutual funds during Ramazan and Muharram months is invalid.

Investors who want to execute large orders always have to trade-off between price effect and opportunity cost. The purpose of this research is to investigate an optimal way to execute such orders. In this research we consider the possibility of order types and the optimal trading strategy based on the volume weighted average price (VWAP), using historical data of a share in Tehran Stock Exchange. In the simulated trading market, we also consider the price effect for large orders. The results show that for a large order on buy-side, execution strategy adopting multiple order types can perform better than those using single order type. The optimal strategy has managed to reduce the volume weighted average price (transaction costs) by 0.137 percent compared to the market.

Increasing the profits and reducing the risks have always been of the most important issues of concern to the investors in the financial markets. In recent years, many solutions and proposals have been suggested in respect to the frequency of portfolio optimization issue, with the highest return and the lowest possible risk. One of the most prominent suggestions is the Markowitz Model which is mostly known as the Modern Portfolio Theory. On the other hand, the TLBO algorithm which has been presented in 2010 is one of the most efficient meta-heuristic methods to solve the optimization problem.In this study, we are attempting to solve the portfolio optimization problem, according to the framework of the model introduced by Markowitz and using TLBO algorithm. For this purpose, the data related to the returns of 20 companies listed in TSE during the period 2012-2016 were collected. It is worth mentioning that four criteria including variance, mean absolute deviation, semi-variance and conditional value at risk (CvaR) were used in order to measure the risk level in this investigation.

The main purpose of this article is about investigation of cointegration and transactional between index price of Iran stock market and stock market of the most main trading partners of country and also analysis of common stochastic trends available between them during 2007-2015. To achieve to this goal, Johansen-Juselius method (1992), Gonzalo and Granger method (1995) have been used.The results show that there is a cointegration vector relation and finally 3 common stochastic trend between investigation markets show the incomplete long-term integration between these groups of variables. On one hand, the results of common stochastic trend during the period under Tehran stock exchange review show that there is low participation in common stochastic, and on the other hand, it has showed high reactions toward of those trends. Finally, according to weak convergence between index price of selected markets can be achieved to its profits by international portfolio diversification.

Accurate forecasting of stock prices according to high volatility and inherent risk of stock market is a major concern of investors and financial analysts, hence applying novel approaches to predict the stock priceisan inevitable necessity. Accordingly, the purpose of this research is to compare the performance of forecasting models such as neural network with classical model and introducing appropriate model to forecast tomorrow stock price. The daily market prices data and financial indicator have been used as input variables for designing neural network model and daily closing price data set as input variable for designing ARIMA and also tomorrow's closing price is considered as output variable from 2011 to 2014. The results show that the Bayesian neural network represents less error sand higher Predictive power than the ARIMA model. The findings indicate the efficiency of Bayesian neural network incapture short-term investment opportunities and also can help investors to choose the appropriate portfolio and to obtain more returns.

Portfolio optimization is the one of most important problems in financial theory. Different strategies can be selected to manage the portfolio that include two kinds is called passive and active. Index tracking is one of most important passive approach. So, there are different models and algorithms to make index tracking portfolio. The aim of this research is analyze of down side beta in the index tracking portfolio. So, three models are presented. Then, to solve these models, evolutionary algorithms include genetic and deferential evolutionary algorithm is used. To show sufficiency of the models, Tehran stock exchange data are selected. Results show the model based down side beta with deferential evolutionary algorithm has higher efficiency Compared with two another models.