نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 29 (زمستان 1395)

In this study¡ In addition to calculate the short and long trading positions¡ we examined In Sample and Out of Sample VaR to assess the quality forecast model is considered. To estimate VaR Result¡ family models Fractionally Integrated GARCH (long term memory) shows that the model HYGARCH (1¡ d¡ 1) with the distribution skewed Student-t similar to the result for FIGARCH (1¡ d¡ 1) with skewed Student-t distribution for fat-tail phenomenon exhibits. A comparison of the two models with different distribution model HYGARCH (1¡ d¡ 1) with skewed Student-t distribution based on AIC criteria and maximum log-likelihood model was superior. failure rates¡ ¡ and duration-based tests where were prepared for back testing in Sample VaR¡ Indicates that the VaR model of the student-t HYGARCH (1¡ d¡ 1) acceptable performance than other distributed models HYGARCH (1¡ d¡ 1) and the FIGARCH (1¡ d¡ 1) will be . So to estimate Out of Sample VaR by student-t HYGARCH (1¡ d¡ 1) has been paid. Like the analysis of the in sample VaR¡ Out of Sample VaR was compared with the observed output and results were evaluated by and DQ tests. Ultimately resulting VaR-based loss function at all levels quintile (either long or short term trading positions) shows that the model that has the characteristics of long memory in the conditional variance¡ minimum losses and better performance in assessment Forecast offers.

Efficiency volatility modeling in stock markets, as seen by academic people and operators in Finance science, in terms of its application in predicting stock efficiency, is of considerable importance. Financial and capital markets in the country has taken effective stepsto develop.This paper aims to develop a model for estimating and forecasting volatility of stock returns is done in the Tehran Stock Exchange. All three models use data on prices, daily top 50 shooters in a 5-year period from 2008 to 2013, is employed Brownian Motion, Fractional Brownian motion and GARCH models .using Excel and Eviews and MATLAB software was designed with the fractional Brownian model. Tests and comparisons using MSE, RMSE, and MAE were performed. Comparing the results of the model of Brownian motion, fractional Brownian fashion model and GARCH, the GARCH model has identified as a top model.

Given the importance of ranking of brokerage firms for customers, this paper analyzes the impact of relationship marketing factors on the ranking of brokerage firms and compares the obtained results with the results of the ranking conducted by the Securities and Exchange Organization (SEO). For this purpose, in the first stage, seven indicators of relationship marketing indicators which have the greatest impact on the ranking and performance evaluation of brokerage firms from the viewpoints of the experts, from among the indices derived from the theories and models of relationship marketing including customer confidence in the organization, customer commitment, empathy with the customers, financial interests, competence of the organization, conflict management, communication and financial interests was determined by questionnaire. The study population will consist of two parts: Active Brokerage firms in Stock Exchange and the customers of these brokerage firms in the cities of Tehran and Isfahan who have used the services offered by the brokerage firms at least twice. The data were collected from a sample of 873 customers of brokerages and 2 experts of capital market. To analyze the research data, the main influencing criteria on relationship marketing were evaluated in the form of questionnaire. Finally, the ranking algorithm was designed using Fuzzy AHP and Fuzzy TOPSIS and ranking was conducted. Correlation test between the ratings indicate that there is a direct correlation and integration between both tools.

Risk-return tradeoff and its analysis in alternative investments as a classic goal of finance have been the main subject of many researches in financial management. The use of technical indicators is a portfolio management tools. This research aims to use these indicators in mining stocks trading rules. The period of investigation is from beginning of 1388 until the end of 1393 and the sample of study is including 216 companies listed in TSE. In the period from 1388 to 1390 by using technical indicators and genetic algorithm with aim for maximize return and minimize risk, we obtain a model for portfolio optimization and in the period from 1391 to 1393 this model was used in portfolio management. In order to evaluate this model, the results were compared with the market index and found that by using technical indicators can outperform the market.

In capital market, currency fluctuations impacts on changes in financial asset prices such as stock. However, Considering the importance of The return risk of stock (rather than price) for market participants, the question arises that in addition to the impact of exchange rate changes on stock price volatility, does the exchange rate has a significant effect on the risk of the stock returns in periods time of short and long term? And whether this effect is same extent or is different in different states in terms of stock price behavior? To answer these two questions is necessary to extract the return risk of stock in different regimes. Thus, the present study attempted to use parametric models based on Markov-switching approach to extract of the return risk of the selected industry index (automotive, mining, cement) in the two different regimes. After extraction of the risk time series we estimate the effect of exchange rate fluctuations on the industry risk time series by using the econometric model ARDL risk in terms of different regimes. The results showed that time series of the return risk follow from regime transition and have got asymmetric reactions of external shocks. Oslo time series of the industry index return risk significantly effect from exchange rate fluctuations in the short-term and long-term.

In this study, a formula is de rived for the period specific beta (market risk) for a portfolio of financial assets that has been formed on the basis of directional forecasts. This is an important contribution to the literature since measuring the risk of an actively managed portfolio is problematic due to the fact that managers may change fund risk conditional on market expectations. The period- specific nature of the measure is a significant advantage since historical fund re turns are not required and the beta is not influenced by prior fund returns’ deviations from the bench mark. The methodology employed allows for the development of a time series of fund betas that permits investigation into a number of important Empirical issues. This study is also of practical interest from the perspective of risk management and for both portfolio performance and attribution. Finally, there are many active strategies based on directional forecasts and the approach used here en com passes a significant proportion of these. The author of this article used of consultation and guidance of Rahnama Roodposhti Fereidoun, Professor and A member of the science team Islamic Azad University, Science and Research Branch ,Tehran, and thankses a lot of him.

For more than a decade of modern financial theory as only the most filed comments on the capital market has been the basis of most studies. But the crisis created in the past decade in most countries and as wll as that many research topic it shows the failure of the modern theory and traditional assumption in fainancial market . So need to reload this theory strongly felt. This new approach according to chaos theory and the fractal science ,Has transformed the classical ideas and methods and words are sufficient to describe the capital market to be done. In this paper , we attempt to introudioced the new approach in fainance. This paper present the mulifractal models of assest return (MMAR). and test the investing company return by three method (MS-EGARCH), Multifractal, Symmetric market (GARCH), Asymmetric (EGARCH).

In any economic system the role of banks to collect deposits of the banking system (Procurement of resource) and its application in the financing of investment projects (allocation of resource) is very important. Nowadays, Expansion of global markets and increase competition in the markets for financial services is affected the profitability of the banking industry, significantly. Given that the profitability of banks is one of functions of banks and a more profitable bank has more power to deal with negative shocks to the market, Therefore, attention to the profitability index as one indicator of evaluating the performance of the banks and its role in decisions related to the mobilization of resources, financing and resource allocation is essential. in this study To achieve this goal, the affective factors on profitability of commercial banks has been modeled and studied during 2009-2013. The scope of this study contains 8 commercial banks listed on Tehran Stock Exchange including Eghtesade Novin, Parsian, Karafarin, Pasargad, Mellat, Tejarat, Saderat and Sina. The study also is used the Panel ARDL method to achieve the targets. The results of this study showed that, The ratio of shareholder's equity to assets, The ratio of credit to assets, bank size and inflation have a direct and positive effect on the profitability of commercial banks. So, increasing and improvement of this variables increase the profitability of commercial banks in the short term and long term. However, the effect of credit risk on commercial bank's profitability index is negative. So that increasing of this variable deacrease the profitability of commercial banks in the short term and long term. finally, The results of ECM model reveal that the speed of adjustment towards long-run equilibrium is low, So that if the shock enters the model the long time require for correct the short-run and long-run imbalance equilibrium and back model to first equilibrium.

Nowadays, environmental impacts and lack of accountability for human resources of the planet has become the main Concern. All organizations, including automakers are required to enter reverse logistics processes, as they are obliged to take responsibility for the proper disposal of waste products and their return. In this study, to achieve the readiness of Irankhodro in implementing reverse logistics, first 21 index and 52 sub-indicators contributing to the implementation of reverse logistics extracted from the literature and these indicators into four groups, agents, strategic, tactical and operational division were classified. After preparing the model, based on the first questionnaire and get expert opinions, weighted average priority (rank) of measurement was calculated and normalized. Complimentary 4 is designated as input, for each of the 3 linguistic variable low, medium and high-definition and 81 fuzzy rule if-then as a knowledge base into the MATLAB software. Using information obtained from the questionnaire's the 4 variables were calculated according to the membership function defined in an application for input variables, the readiness of Irankhodro Industrial Group fuzzy set "average" was awarded. Accordingly, proposals to implement the reverse logistics in industry group better presented.

Research in financial distress and bankruptcy emphasize the design of more sophisticated classifiers, and less feature (variables) selection and their appropriate methods. In this regard, the purpose of this study is to compare performance of different feature selection methods in financial distress prediction of the companies listed on Tehran Stock Exchange (TSE). In this regard, we investigated and compared five feature selection methods, including t-test, stepwise regression, factor analysis, relief, wrapper subset selection and RFE-SVM feature selection. To obtain comparable experimental results (reliable comparison), three different classifiers (including neural networks, support vector machine and AdaBoost) were used in this study. In overall, the experimental results confirmed the usefulness of variable selection methods and significant difference among level (amount) of different methods performance. In other words, the application of the feature selection methods increases the mean of accuracy, and reduces the occurrence of type I and type II errors. Furthermore, the results indicated that wrapper subset selection method outperforms the other feature selection methods.