فصلنامه دانش مالی تحلیل اوراق بهادار
سال پنجم شماره 15 (پاییز 1391)

The Objective of this paper is evaluation the management to control the liquidity money by central bank in Iran from 1378:3 to 1387:2. For this purpose the volatility index is drive GARCH model. The result indicate that central bank management in control the liquidity money for monetary policy, early years of 1378 and 1379 inputs to improve and central bank’s application planning organized in determining the amount of liquidity money in the final period has led to reduced liquidity money instability and its tendency toward to a stable level and has been optimized.

This article deals with study of the relationship of Underpricing with IPO Aftermarket liquidity in listed companies on TSE. To achieve this purpose, required financial data from 80 qualified initial public offering in nine-year period between the years 2001 and 2009 were gathered. After determination of initial return period, relationship between underpricing and aftermarket liquidity, have been examined in sections 30, 120, 240 trading days after initial public offering by multivariable regression tests. Significant of model and coefficients have been examined by F fisher and T-student tests. The results indicated that underpricing (initial return) is positively related to turnover ratio and negatively related to illiquidity measure. These relation are significant after controlling for other factors.

In today's competitive environment, financial managers are trying to consider the capital structure and financial decision in order to increase their companie's value. This research is aimed at studying the ability of pecking order theory in explaining the capital structure pattern of companies in Iran’s capital market. The statistical population of this research includes all industrial companies listed in Tehran Stock Exchange (TSE). 127 industrial companies have been selected in duration of 2000-2009 as samples based on a systematical method. Library method have used for data collecting, and balanced panel data and panel analysis model have used for examining the research hypothesis. The obtained results suggest that there is a positive significant relation between financing deficit and net long-term debt issued as well as between maturing long-term debt issued and long-term debt issued. This shows that the Iranian companies follow pecking order theory in their capital structure pattern.

This study empirically investigates the effects of business strategies on the relationship between financial leverage and the performance of firms. The research data is collected from 45 firms in the Tehran Security Exchange (TSE) during 1381-1387, using by Rahavard software and TSE site. The statistical technique used for examining the assumption is regression coefficients. For testing of the assumptions, firms divided to 2 sections: firms with cost leadership strategy and firms with product differentiation strategy. The results indicate that in the 2 types firms, there is positive relationship between leverage with performance. The results also suggest that in the firms with cost leadership strategy, dividend pay out have positive relationship with performance and in the firms with product differentiation strategy, there is positive relationship between firm’s size with performance, but relation between dividend pay out with performance is negative.

By using the time series models, we can analysis financial data(in last and future time). In financial discussions, because of heteroskedastic observations, we can not use the classical time series models. We focus on popular practical models for financial time series, GARCH- type models, that were introduced for the first time by Bollerslev(1986). These models represent a very wide class of heteroskedastic econometric models. Time series models(GARCH models too), like regression models, have random errors. These errors have specific distributions. Since that, the GARCH models variability is not clear, thus, we use the Bayesian model selection methods to estimate the parameters of the model. In this method, by using the prior distributions on the parameters, we find the posterior distribution which has integral. Then, we can inference about the parameters. To explore the role of the posterior distribution, the most powerful technique is to use Markov Chain Monte Carlo (MCMC) computing methods such as the Gibbs sampler and the Metropolis Hasting (MH) algorithm. These algorithms enable to estimate the posterior distribution, but, they don’t readily lend themselves to estimate aspects of the model probabilities. The most widely used one is the group of direct methods, such as the harmonic mean estimator, importance sampling and bridge sampling. Chib(1995 and 2001) proposed an indirect method for estimating model likelihoods from Gibbs sampling output. This idea has recently been extended to the output of the MH algorithm. We use a reversible jump MCMC strategy for generating samples from the joint posterior distribution based on the standard MH approach.

Herd behavior is each similarity in behavior of people because of their relations. Institutional investors are among the main players of capital market and stock market, These investor’s activities on the one hand affect directly on indices, and on the other hand on transactions of other players in the stock market. In case the transactions of stockholders are not based on sufficient information and merely based on imitation of other investors, herding behavior would be taken place within them. Herding is one of the most pervasive behaviors in financial markets which are able to explain some volatility in the Stock Exchange. In this study, using Lakonishok (1992) methods, we consider herding behavior amongst the investment companies of Iran stock market. In this method, monthly data are used for testing herding behavior. The results of this study confirmed the existence of herding behavior among the managers of investment companies.

This paper looked at the relationship between Stock Markets and Foreign Exchange rates, and determined whether movements in exchange rates had an effect on stock market in Iran. The Exponential Generalized Autoregressive Conditional Heteroskedascity (EGARCH) model was used in establishing the relationship between exchange rate volatility and stock market volatility. It was found that there was positive relationship between exchange rate volatility and stock market returns. Additionally, there is volatility persistence in most of the macroeconomic variables. It was also revealed that an increase (decrease) in trade deficit and expectation in future rise in trade deficit would decrease (increase) stock market volatility. In addition, the consumer price index has a significant relationship with stock market volatility.

To obtain reliable model for auto correlated and time series data, robust approach should be considered because outliers and contaminations can have bad effect on parameter estimation of these models. Since most finance data are auto correlated and they are affected by the previous data, they can be modeled by time series regression models. In this paper, the autoregressive (AR) model is investigated and novel robust procedure based on filtered S-estimator is proposed to estimate the parameters of AR model. This model is used to obtain robust forecasting procedure. We present 148 data gathered from a firm which are related to profit as a numerical example and show the efficiency of the proposed estimation approach. The robust model can forecast more accurate than classical model in presence of outlier.

The aim of this study is to introduce an efficient nonlinear model for predicting the return of Tehran Stock Exchange (TSE) Price index. For this purpose, the daily time series of price index from Farvardin 1388 to Aban 1390 is used. This study includes 616 observations; 90% of which used for estimating coefficients and the remaining 60 observation are deduced for out of sample forecasting. By comparing the results of a nonlinear dynamic artificial neural network (NNAR) and a nonlinear regression model (autoregressive fractional integration moving average «ARFIMA»), we found that NNAR models have better performance in out of sample forecasting based on mean square error criteria (MSE) and root mean square error criteria (RMSE) than the nonlinear regression models (ARFIMA).

Global stock exchange markets¡ counter acceptance between firms in different markets¡ besides¡ increasing trading tools¡ brokers and also online trading ¡make increase work hours at big markets of the whole world .Today¡ it caused¡ stock exchanges by help of advanced technology¡ go to increasing meetings ¡trading time¡ and probably 24-hours trading. Supplying varied financial markets in the country and trading in other countries’ stock exchange markets (online trading)¡ make new choices for the investors; Inattention to international and environmental changes ruins Tehran Exchange of competition. Exchanges and brokers by diversification trading performance methods try to informed their costumers’ demands ina best way .The competition of demands procedure among different trading systems is so intensive that make exchanges and online networks advance their demands performance systems according to uniformity with current rules changes. Tehran Exchange follows increasing trading and market’s value by using opportunities of introducing online trading systems (DMA) and more time for trading through welcoming to universal trends and adopting long term guidelines .Otherwise¡ There would be the risk of lacking most of the market investors who will choose another investment opportunities .In this research we studied six effective indexes of investors usage of online trading systems ¡including :developing infrastructural hardware (Hardware)¡ security of online systems (Security)¡ technology of software systems(Technology)¡internet trading rules(Rules)¡acculturation of online purchasing stock(Culture) ¡and training investors (Education)¡ and their effectiveness on increasing transaction size by using experts’ theories and gathering investors and experts opinions by distribution of questionnaires in Tehran Stock Exchange Organization¡ and by helping fuzzy inference system¡ it explained condition and importance of each index on volume of transaction.