hassan hadipour; Seyyed Ali paytakhti oskooe; Yaghoub Alavi Matin; Kamaleddin Rahmani
Abstract
The capital market, especially the stock market, is as risky as any other investment activity and is affected by overflow fluctuations and instabilities from other markets. In the face of other macroeconomic variables, this causes instability in the stock market. In the present study, using conditional ...
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The capital market, especially the stock market, is as risky as any other investment activity and is affected by overflow fluctuations and instabilities from other markets. In the face of other macroeconomic variables, this causes instability in the stock market. In the present study, using conditional turbulence method, the factors affecting on instability index in basic metals industry sector of Tehran Stock Exchange was investigated and presented. For this purpose, monthly data from April 2009 to April 2020 were used. The results of the present study indicate that fluctuations in the industrial sector are caused by factors such as political conflicts and international problems in Iran and are strengthened by fluctuations in parallel markets such as oil, gold and currency. According to the results of the research, factors outside the stock market industry due to the underdevelopment of the stock market in Iran; From the point of view of the analyzes performed, the most important effect and factor causing fluctuations is the section of political tensions and international relations in Iran, which has effects. It is uncontrollable on parallel markets in Iran and ultimately the effect of all of them is reflected in the stock market.
Mohammad Javad Mohagheghnia; Kashi Mansoor; Alireza Daliri; Mohammad Donyaei
Volume 12, Issue 33 , July 2015, , Pages 151-181
Abstract
This paper investigates the presence of long memory in the Tehran stock market, using the ARFIMA, GPH, GSP and FIGARCH models. The data set consists of daily returns, and long memory tests are carried out both for the returns and volatilities of TEPIX series. Results of the GPH, GSP and ARFIMA models ...
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This paper investigates the presence of long memory in the Tehran stock market, using the ARFIMA, GPH, GSP and FIGARCH models. The data set consists of daily returns, and long memory tests are carried out both for the returns and volatilities of TEPIX series. Results of the GPH, GSP and ARFIMA models indicate the existence of long memory in return series. Also, suggest that long memory dynamics in the returns and volatility might be modeled by using the ARFIMA–FIGARCH model. Furthermore, results of this model shoes the strong evidence of long memory, both in conditional mean and conditional variance. In addition, the assumption of non-normality is appropriate for capturing the asymmetry and tail fatness of estimated residuals. These findings suggest that the model based on the Gaussian normality assumption may be inappropriate for modeling the long memory property. Finally, it seems that the Tehran Stock Exchange (TSE) cannot be considered an efficient market in terms of the speed of information transmission. Hence, speculative earnings could be gained via predicting stock prices.
Safar Fazli; Rasool Taghizadeh
Volume 8, Issue 19 , December 2010, , Pages 125-146
Abstract
Portfolio selection problem is an important field of capital assignment and budgeting in managerial finance and had proposed patterns for optimal selection of portfolio from the past. For this purpose we suggest a fuzzy ranking method with mathematical approach. This research is a survey in Tehran stock ...
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Portfolio selection problem is an important field of capital assignment and budgeting in managerial finance and had proposed patterns for optimal selection of portfolio from the past. For this purpose we suggest a fuzzy ranking method with mathematical approach. This research is a survey in Tehran stock exchange. Statistical population inclusive 50 superior companies of Tehran stock exchange in 1387. By survey of financial data of these companies, 20 companies have selected and weekly returns in 1387 have been calculated for them. In first stage, 20,000 random portfolios have been generated by a computer program. Each of these portfolios is composed of 20 companies that quantity of investment in each of them are between 0% and 100% and selected randomly. The uncertainty on the returns of each portfolio is approximated by means of a trapezoidal fuzzy number. A rank index that accounts for both expected return and risk is defined, allowing the decision-maker to compare different portfolios and select best portfolio. Conclusions showed that, according to risk aversion of investor, several optimal portfolios can be selected. In this article we suggested 3 optimal portfolios.