FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES)
Year:2015 | Volume:7 | Issue:24|Papers Count:9

Holding companies are one of the main factors in economic development of each country. Corporate governance can affect these companies from different aspects. Financing plan as a related subject in implementing development plans is very important. Therefore, the aim of this research is to investigate the relationship between corporate governance and financial risk of holding companies listed in Tehran Stock Exchange during 2009-2013. For measuring corporate governance, we use ownership concentration, institutional ownership, size of board and board independency. For measuring financial risk we use debt ratio. The population of this research is 83 active holding companies listed in TSE. The results confirm a significant but negative relationship between institutional ownership and financial risk. Other variables didn’t have any significant relationship with financial risk.

Always one of the concerns of researchers in the study of time series, the long-term memory and whether the observed long memory series is affected by level shifts or not? To avoid spurious long memory that may be caused by levels shifts, different methods have been tested. In the present article, we will review it for return and volatility (Are two approaches: First approach: GARCH and ARMA-GARCH-filtered series. Second approach: GARCH-filtered-squared series and squared returns) based on modified GPH of smith (2005). The results indicate that long memory in return series TEPIX is accepted by the GPH but modified GPH test (Bandwidth choice for classic GPH and modified GPH are based on Plug-in and ) leads to the rejection of the existence of long memory. Then, using the first approach, the presence of long memory is demonstrated by the GPH. But modified GPH denies its existence, which reflects the impact of level shifts to long-term memory. The second approach about how to volatility infer the existence of long memory is demonstrated by both tests. It followed that the predictability of the market as possible and Weak form efficiency would violate the Tehran Stock Exchange.

Specifying the governing process of stock market’s return with the goal of making optimal decisions and reducing the risk cost has a great importance for investors and policy makers. The importance of market analysis on one hand and the effort for comprehending the markets on the other hand resulted in that, after the assumptions of efficient market were challenged and universal financial facts such as “fat tails” and “volatility clustering” were discovered, analysts leaned toward multifractaly and Levy models and moved away from models with random characteristics and normal distribution. This caused multifractal models to be used in different segments of the market. In this article the multifractal approach that in recent years has been used for forecasting and modeling volatility, will be examined. In the beginning the origin of this approach that stems from turbulent flows in statistic physic will be introduced and in the following sections the details about the specifications and features of multifractal time series models, available approaches for estimating them and empirical uses of this model will be mentioned. The results of this research show that the dynamic nature of capital market has caused the approaches, methods and models of market analysis to be permanently changing. In addition, for volatility clustering of time series, smaller scales are considered.

Current paper is aimed to investigate origin of expected return explanation by volatility of specific volatility through momentum strategy return. In other words, one of the explanations presented for profitability of investment strategy based on specific volatility is tested in this paper that is founded by investors' under-reaction to firm specific information and ultimately momentum appearance. So the relation between momentum and specific volatility of CAPM and Fama-French three factor model is tested using portfolio study approach and Fama-Macbeth regression.This research that is performed in sample composed of 130 listed firms in Tehran Stock Exchange, shows return of investment strategy based on specific volatility is higher for stocks having high momentum. If momentum effect is included, explanatory power of the specific volatility is not omitted. So it cannot be claimed that the origin of relation between model specific volatility and expected return is momentum.

One of the innovations of the last decade in the field of Islamic monetary and financial debates is the publication of a variety of Islamic securities which is designed on the basis of Islamic agreements and appropriate alternative to the unlawful profit securities particularly is counted the bonds.This article discusses the publication of interest securities, explanation of types of the risks associated with its securities and its stakeholders and also provide the management strategies, reduce and or coverage of these securities risk. It is expected to provide disseminate and improve these more financial instruments in Islamic countries with explaining the interest bonds ways and especially the risk of these securities. The interest securities is negotiable securities which is published based on Islamic agreements and represent its holders condominium on future interests and durable assets that transferred in lieu of payment of specified sum and the holders of the securities may benefit financial same and equivalent of the interests. As per findings, this research and model provided shall be operated for these securities, stakeholders of debts including sponsor (durable asset owners), real customers and investors and the interest securities shall be the instrument for return and coverage of the risk of each of the stakeholders. The sponsor of the securities covered the risk of future interests' sale for its asset, safe return investors, real customers, beneficial right from future interests for basic asset with publication, investing and or purchasing the interest securities. Although the interest securities in the frame of the research is a financial tool to gain the return and coverage of the risk, but like any other financial asset is exposed to various risks such as interest rate, liquidity and operational risks and also etc. which is presented as much as possible coverage guidelines of these risks.

Continuous rebalancing and optimization of the portfolio in a way that always leads to tracking the index accurately is a complex issue. Moreover, considering the transactional costs are inevitable. This paper proposes a model for optimization of the index tracking problem and a solution based on Genetic Algorithm. The proposed model is a bi-objective model that is aimed to minimize the tracking error and transactional costs. Due to complexity of the model, the Non-dominated Sorting Genetic Algorithm and the Non-Dominated ranked Genetic Algorithm are used and evaluated to solve the model. The basic metals Index of Tehran Stock Exchange at the year of 2012 is used in this research as the desired index for tracking and rebalancing.

There are different ways of system analysis. System thinking is a novel and very effective method in this field. Actually, system dynamic approach offers a dynamic model of a process in the real world and systematically analyzes related factors. In other words, it indicates the influence of changes on each effective factor on the entire system in each moment. In this paper, primarily, a dynamic model is proposed by investigating different effective factors on future price of assets which include known income and the way of arbitrage opportunities with non-correct prices are simulated. Thus, scenarios are offered to illustrate the changes of different effective factors on system and arbitrage opportunities made as a result to these changes.

The main goal In this paper is to merge fuzzy DEA with Markowitz model to construct optimized portfolio of efficient companies in Tehran bourse. So, first using of DEA we choose efficient companies as efficient group, although we choose two type of efficient companies with adding controlling relative weight constraints for two type of investors ( risk aversive & risk taker ), Then, using of Markowitz model with regarding of the level of risk aversion, we construct efficient portfolio from efficient group.The large number of criteria is one of the MCDM model's problems for solving this problem we can use of factor analysis to reduce a complex data set to a lower dimension. In this paper with respect to experts's opinions,firstly the main variables corresponding to company's efficiency were assigned ( 15 financial ratio ) and then using of factor analysis we reduse the number of these variables to eight, after that with adding controlling relative weight constraints to DEA model, we construct efficient groap for two type of investors ( risk aversive & risk taker ). due to relativeness of risk and return in terms of investors, whit imputing investors to type( risk aversive & risk taker), efficient groaps were constituted. Finaly investor can, with regarding of the level of his risk aversion, using of Markowitz model, construct optimal portfolio from efficient groaps. Though in final step optimal portfolios were choosed from efficient groaps, thus one of the main problem of Markowitz model that is nonregarding other criterion except risk and return, will be soleved.

Forecasting GDP, is one of the most important economic issues and due to its practical applications has attracted a lot of attentions. Methods of time series analysis and nonlinear methods such as neural network models as long as are used to forecast such variables. GDP's time series is variable that after the decomposition, with wavelet - a powerful tool for processing data- and analyzing the hidden layers, at some levels, has linear behavior and at other levels, has nonlinear behavior.Therefore, the proposed method would be thus that the time series of quarterly GDP for the period 1367 to 1389 using wavelet techniques are decomposed into different scale components. Next, the approximation level (trend) and cycles with linear behavior have predicted with ARIMA model, and cycles with the nonlinear behavior have predicted with neural network model.The results show that the performance of the proposed method is better than the neural network (NARNET) and ARIMA models.