Having an investment portfolio with minimum risk and maximum return is the Pareto optimum that all investors in the financial markets are looking for. Achieving this goal is not possible due to its idealistic nature, and people can only approach this optimal situation. However, the basic principle in the composition of the investment portfolio is to have financial assets with minimal correlation in the portfolio. This is followed to reduce the yield spillover effect among the portfolio assets. This is the reason for this study to investigate the yield spillover between assets such as gold, dollar, euro and major stock market indices such as automotive, banking and chemical group index for the daily period from 2018 August 26 to 2023 March 15, using the ARCH approach. Results of EGARCH(1,2), EGARCH(1,1), GARCH(1,2), GARCH(2,2) and AR(2) models for banking group index, chemical group index, gold, euro and dollar respectively which are based on the selected information criteria, showed that the spillover effect of the studied assets is different from each other.

In this study, the effect of exchange rate fluctuations on abnormal returns of companies listed on the stock exchange were studied. In this study, Auto Regressive Distributed Lag model due to its ability to explain this connection was used for short-term and long-term. To evaluate the effect of exchange rate fluctuations, the banks' legal deposit at the central bank, GDP, inflation, current account and capital account on the stock market according to the designated filters, exporting companies that gained the research conditions, were determined. In this study, the company's export price index was calculated at the end of each quarter and abnormal returns were calculated for the group of companies. After calculating abnormal returns of firms, independent and control variables in the ARDL model imported and the effects of the explanatory variables investigation revealed abnormal returns. The results show that the exchange rate fluctuations variable has a positive and significant impact on exporting companies are abnormal returns.

Target leverage is one of the challenges of capital structure studies. Some researches proof targeting behavior, while some others estimate random financial leverage. This study examines the leverage behavior based on four leverage measures for 108 companies in Tehran stock exchange over the observation period 1380–1393 (2001-2014). In this paper, we analyze four different models, including stationary target ratios model, time-varying target ratios model and target zone model to study the stochastic nature of leverage behavior. We apply simulation methods with leverage measures to evaluate the ability of random financing and a variety of leverage targeting models. Target leverage models with little or no emphasis on staying near a particular target provide a good explanation for leverage behavior. Target leverage models explain total book leverage more precise than other leverage measures.

The basic rational for the theory of the efficient market model stems from the assumption that in such a market prices promptly adjust to reflect all knowable information. The future price changes, although a true reflection of changes in the earning power of firms, reach the market randomly and are independent of the past information. Future price changes, therefore are random and a function of future, not past events. The efficient market model describes that the return on and hence the price of securities depends on multiple forces. The first major force is the impact of market movements on price changes of individual securities, which is outside the control of the investors and of the firms in the market. Other forces such as industry impact, firm"s influence, etc cause the rest of the variations in prices. The industry and firm"s factors can be eliminated by appropriate portfolio diversification. Consequently only the market compensates the risk associated with market movements. Diversifiable and the non-diversifiable components together comprise the total variance or standard deviation of return for a particular security. The aim of this paper is to study the efficiency of Tehran stock exchange (TSE). In doing so, with the use of the OLS and capital asset pricing model (CAPM), we have estimated the characteristic line and the security market line. The results show that there is a linear relationship between risks and return; in other word, the result is not strong enough for rejecting the efficiency of TSE, but according to estimated equation there are over (under) valued securities in the market.

This article has tried to simulate the behavior of national Iranian copper industries company’ s stock in the stock exchange market and help the share holder and policy makers to analyze the fluctuations and forecast the future of the stock. For the aim of simulation, first we have identified the influential factors in stock price in the stock exchange market and the influential factors incopper price in the market. The relation between different variables is shown by causal diagrams using system dynamics approach. Then the research model is simulated and analyzed by Vensim DSS. The results showed that production costs and copper’ s world price are the most important factors in shaping the fluctuations of the stock price. At the end different scenarios such as reducing the subsidies in the second phase are examined and the results are discussed.

There are different strategies for selecting stocks, and different investors use different strategies according to their risk tolerance or their expected rate of return. In this study, the profitability of a broad range of stock selection strategies in Tehran stock exchange over the period 1370-1383, has been examined, and it has been investigated whether the successful strategies in other countries are also successful in Iran or not. Although a lot of comprehensive studies have been done in the developed and in a considerable number of emerging markets, and successful strategies have been well documented in those countries, such studies have never been done in Tehran stock exchange. The sample is all the companies in Tehran stock exchange in the aforementioned period. Also, in order to evaluate different strategies, various portfolios have been formed for each year according to each strategy. Then, computing the return of winner portfolios, those strategies generating the maximum return in excess of market return, are presented. The evaluation of the performance of the strategies has been done regarding various diagnostics criteria like risk and return. The results show that value strategy is the most successful strategy in Iran and generates significant excess return, in contrast to growth, size, price momentum and fundamental strategies. In other words, the most successful strategy in Iran is the multivariate strategy which selects the stocks with high E/P, B/P, C/P, S/P and D/E. Moreover, as apposed to the developed markets and a considerable number of emerging markets, size and momentum strategies are not profitable ones in Tehran stock exchange and can not distinguish between profitable and unprofitable stocks.

EXTENDED ABSTRACTINTRODUCTIONAlthough herding behavior is mainly defined on the basis of imitation and repetition in existing theories, it is difficult to provide a mathematical model which is able to identify this phenomenon.METHODOLOGY Therefore, in this paper, using the Monte Carlo method and stock price data of Tehran stock exchange and OTC companies, during the years 2011 to 2019, the herding behavior among the sample companies is investigated. Given that the Iranian capital market is facing the phenomenon of closure, and this can affect the values ​​of price herding, the results are examined with the New York stock exchange as a market developed. FINDINGS The first finding indicates the presence of herding behavior in 29.6% of possible cases in the sample. The second finding indicates the presence of herding at an average of 4.07%. The third finding reflects the increase in the amount of herding along with the increase in absolute return, which shows that as the stock prices change, the values of herding also increases.Also, the results show that the herding behaves almost symmetrically with increasing the absolute amount of stock returns, the amount of herding behavior is first decreasing and then increasing. Accordingly, when price change are slight, the amount of herding is small; But with drastic increases, the average herding behavior also becomes positive and upwards, reaching 16%. This means that as the price of one stock rises, the prices of other stocks also tend to rise, and the sharper the price increase, the greater the amount of imitation of price behavior. A similar trend is observed when prices fall. As prices fall and negative returns intensify, the average rate of herding behavior also increases, and the higher the decline in prices, the higher the rate of herding behavior. As a result, the higher the absolute amount of price return, the greater the amount of herding behavior.The fourth finding of the study indicates the possibility of a relationship between the herding and trading volume. To examine this relationship in the face of a sharp increase in the volume and number of trades, price data are classified into 20 groups based on the number and volume of trades. Then the amount of herding behavior of each group was calculated. The results show that the relationship between both indices of trading and herding is positive and significant and with a sharp increase in the number or volume of trades, herding measure approaches its maximum value. Similar results on the New York stock exchange are described below. CONCLUSIONEvidence of herding behavior in the New York stock exchange also shows that this phenomenon occurs almost twice as much as in the Iranian capital market. Faster and more coordinated dissemination of news and faster reactions to them in NYSE can be the main reasons for this. These results are consistent with a study by Hwang and Salmon (2004) in which the amount of herding behavior in the US market was higher than the South Korean stock exchange. It seems that due to the less trading halts in NYSE, the significant values ​​of price herding in that market mean that the values ​​obtained for the Tehran market are probably not affected by the trading halts and the results can be reliable. 

This paper investigates stock valuation in Tehran stock exchange with regard to behavioral heterogeneity and bounded rational behavior of agents. The related information of the fundamental factors are disseminated and become available for the public, and investors assess the stock with different models, for example Gordon model. However, because of different expectations of investors, the amount of deviation from the fundamental value and the lengthiness of this deviation are viewed important. In this study, behavior of two groups of investors is examined. First, the fundamentalists who believe in movement of stock prices toward the fundamental value. Second, the chartists who think stock prices should be different from the fundamental value. Results by monthly data show that investors switch between different strategies and sometimes the chartists force the fundamentalists to change their strategies and coexist with them.

This paper investigates the linkage between total index of Tehran stock exchange (TSE) and non-official exchange ratesby daily data from May 2011 to May 2013. In this period, exchange rate tripled and TSE doubled. Johansen Cointegration test and VECM model applied to specify this linkage. exchange rates and TSE found to be Co-integrated. TSE has positive response to exchange rates but exchange rate has negative response to TSE and it is weak exogenous.

The stock market is one of the main components of the economy, and various factors cause fluctuations in it, one of which is the effect of investors' behavior. Therefore, present study seeks to answer the question of whether the feelings and sentiments of investors might intensify the fluctuations in the Tehran stock exchange or not. To answer this question, at first, in order to quantify sentiments, as non-abstract variables, the Equity Market Sentiment Index (EMSI) was used that investors are classified in 5 categories of completely risk-averse, risk-averse, neutral-risk, risk-taking and completely risk-taking. Using GARCHi-in-Mean, results indicate that the sentiments of investors will result in greater fluctuations in the Tehran stock exchange. Hence, if fluctuation is considered an indicator of market risk, the excitement associated with an abnormal rise in volumes will increase that risk.