List of Articles شاخص صنعت

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  1 - Predictive Power Stock Market IndicesFor The Future Economic Activity,In The Frequency Domain
  Amir Mohammad zadeh Parisa Karim khani
  Financial markets are among the influential markets in the economy of every country. Stock market booms and crashes in some countries not only influence their national economies but also have impacts on the global economy. Study of performance of stock market and stock More

  Financial markets are among the influential markets in the economy of every country. Stock market booms and crashes in some countries not only influence their national economies but also have impacts on the global economy. Study of performance of stock market and stock price index and their effects on economic factors are among issues increasingly being focused by economic and financial researchers.Up to now many studies has been conducted on the causal relationship between stock market indices and economic variables in various countries. These causal relationships have confirmed in some studies and they have rejected in other ones. The innovation of present research is study of Granger causality in frequency domain about which there are no comprehensive studies especially in Iranian context. Present study addresses the causal relationship between gross domestic product (GDP) and stock market variables including stock price index, financial index and industry index in Iranian context and explores if predictive power is concentrated on lower frequencies or higher ones. Main goal of present study is to employ stock market indices to develop a model for prediction of GDP. Results from present study showed that in Iranian context there was no causal relationship between GDP and selected variables related to stock market in frequency domain and stock market indices cannot be used to predict GDP. Manuscript profile
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  2 - Long-run Relationship between the Volatility of Effective Real Exchange Rate and Industrial Return Index in Tehran Stock Exchange Market (Multivariate GARCH Approach)
  Esmaeil Aboonouri AmirMansour Tehranchian Mostafa Hamzeh
  This paper, empirically, analyzes dynamic relationship between real effective exchange rate and industrial index in Tehran Stock Exchange market using VAR and Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH), by monthly time series data du More

  This paper, empirically, analyzes dynamic relationship between real effective exchange rate and industrial index in Tehran Stock Exchange market using VAR and Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH), by monthly time series data during 2001-2011. The results represent that there is no long-term significant relationship between effective real exchange rate and industry index. Furthermore, the paper examines the cross-volatility effects between foreign exchange and stock markets. There is a bidirectional volatility spillovers effects between two markets. This indicates that previous innovations in stock market affects on the future volatility in foreign exchange market, and vice versa. Manuscript profile
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  3 - Optimization on ELM network using Particle swarm Optimization Algorithms and OSELM to predict the industry index in Tehran Stock Exchange
  , benyamin hakimzadeh ehsan Taiebysani Mahdi Saeidi Kousha
  There have always been two approaches to forecasting in financial markets: traditional and intelligent approaches. In the traditional method, this forecasting is based on statistical models and in the intelligent method is based on artificial intelligence models. Tradit More

  There have always been two approaches to forecasting in financial markets: traditional and intelligent approaches. In the traditional method, this forecasting is based on statistical models and in the intelligent method is based on artificial intelligence models. Traditional methods mainly use linear patterns to model market behavior, while the main advantage of smart models is the ability to learn and model nonlinear behaviors in the market. It has always been a question of which methods can better model market behavior, and despite the many models that have been proposed for forecasting, there is still an attempt to build a model that can use more effective variables for forecasting. Continues to be able to take into account factors such as time, risk and return. In this research, we have used the neural network to predict the industry index. This is done by ELM neural network using two optimization methods OSELM and PSO. The results show that the prediction accuracy of these two methods is not significantly different from each other, but in terms of execution time, the OSELM neural network algorithm has performed much better and faster. Manuscript profile