شاخص بورس

Open Access Article

1 - Investigating effect of mean residence time on herd behavior on Tehran stock exchange index volatility by Heston model
Zahra Shirazian

In this article investigate the herd behavior of stock prices inTehran stock exchange with the Heston model. Basing on parameter estimation of the Heston model obtained by minimizing the mean square deviation between the theoretical and empirical return distributions, w More

In this article investigate the herd behavior of stock prices inTehran stock exchange with the Heston model. Basing on parameter estimation of the Heston model obtained by minimizing the mean square deviation between the theoretical and empirical return distributions, we simulate mean residence time of positive return.Plots of mean residence time of positive return against the amplitude or mean reversion of volatility demonstrate a phenomenon of herd behavior for a positive cross correlation between noise sources of the Heston model. Also, for a negative cross correlation, a phenomenon of herd behavior is observed in plots of mean residence time of positive return (MRTPR) against the long-run variance by increasing amplitude or mean reversion of volatility. From the simulating results of MRTPR, we observe that (i) when MRTPR is regarded as a function of the amplitude of volatility fluctuation c, there is a phenomenon of the herd behavior for a positive cross correlation between two Wiener processes of stock price and volatility (i.e., λ > 0); (ii) when MRTPR is regarded as a function of mean reversion a, there is a phenomenon of the herd behavior for our considered values of b and c under λ > 0; (iii) increasing a or c induces a phenomenon of the herd behavior under λ < 0 when MRTPR is regarded as a function of mean reversion b. Manuscript profile

Open Access Article

2 - Evaluating and study of the Fractal Properties of Capital Markets Based on DE trended Fluctuation Analysis (Case Study: Exchange Market and Stock Index of Tehran)
Arash Azaryoun narges yazdanian seyedalireza mirarab baygi hoda hemmati

In this study, the long-term memory of the stock market index and exchange rate (dollar) was estimated using detrended fluctuation analysis. In order to detrend the data, the GARCH approach was proposed and the long-term memory estimation model was implemented separatel More

In this study, the long-term memory of the stock market index and exchange rate (dollar) was estimated using detrended fluctuation analysis. In order to detrend the data, the GARCH approach was proposed and the long-term memory estimation model was implemented separately for both conventional and GARCH methods. For this purpose, daily data of stock market index and dollar exchange rate in the market during the years 2014 to 2020 were used. The results showed that the conventional method in calculating the detrended fluctuations is not able to estimate the long-term memory of the exchange rate, while the results for the stock index showed the existence of short-term memory. The results showed that the proposed method in detrending data and calculating detrended fluctuations based on Garch model has a higher power in controlling changes in market fluctuations and according to the findings of this method, stock index and dollar exchange rate have long-term memory. The results showed that these two methods provide significantly different estimates of long-term memory of the market and according to the results of the correlation test between the values of long-term memory of data and the value of parameter q in detrended fluctuation analysis; it was observed that the stock market index and exchange rate in Iran have multifractal properties. Manuscript profile

Open Access Article

3 - Speculative Bubble in Capital Market
سید مجید شریعت پناهی هانیه روغنیان

Capital Market as the base of financial interactions is needful to have a perfect supervision. Because defects in basic factors like efficiency, regulation and investors learning, cause Capital Market to wander and emerging the phenomenal in the name of bubble. Bubble i More

Capital Market as the base of financial interactions is needful to have a perfect supervision. Because defects in basic factors like efficiency, regulation and investors learning, cause Capital Market to wander and emerging the phenomenal in the name of bubble. Bubble is the proximate and intensive growth in prices that is result of optimistic expectations. Demand is increasing by following the optimistic expectations and it is the cause of increasing price. At the end, by reverse expectations, we have decreasing price and crisis. This research has first some introduction about bubble and it`s inducements, by following the regime switching model, representation by Brooks and Katsaris (2005), then we recognize Speculative Bubble in the Tehran stock exchange Index and 5 Industrial Index(Felezate asasi, Siman, kani felezi, khodro, kok va tasfieh Faraverde nafti)  in the term of 1383 – 1387. The main variables in this model are relative size of bubble and abnormal volume.   Manuscript profile

Open Access Article

4 - The Effect of Stock Returns and Volatility of Stock Index on Put Option Transactions Volume
Nezamodin Rahimian Ali Khozein Jamal Mohamadi

The aim of this study is the examination of put option transactions volume increasing in Tehran Stock Exchange as result of increasing in stock returns and volatility of stock index. A put option is an option contract giving the owner the right, but not the obligation, More

The aim of this study is the examination of put option transactions volume increasing in Tehran Stock Exchange as result of increasing in stock returns and volatility of stock index. A put option is an option contract giving the owner the right, but not the obligation, to sell a specified amount of an underlying security at a specified price within a specified time. 32 companies which issued put option during the 1391 to 1394 have been selected as the study population were studied. Based on the findings and results of statistical analysis in case of desired and positive stock returns due to high investment risk, investors are more willing to trade put option. Therefore, despite the expected positive returns, Shareholders insure their equity investment against the risk. But if the volatility index was high, it means the risk would be high. In such a case, despite the high risk Shareholders do not tend to enter the market of put option. Manuscript profile

Open Access Article

5 - تحلیل اثرات متقابل نا اطمینانی بورس اوراق بهادار تهران در میان بورس‌های منطقه و جایگاه آن: کاربرد الگوی MGARH (BEKK)
بهزاد فکاری سردهایی محمدرضا کهنسال سمیه ربانی

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Open Access Article

6 - Stock portfolio optimization using prohibited search algorithms and itinerant trader
fatemeh samadi fatemeh khosravi Hossein Eslami Mofid Abadi

In this thesis, modeling and forecasting of stock market fluctuations using the combination of neural network and conditional variance patterns (case, Tehran Stock Exchange) have been used from April 2008 to April 2012. According to the predicted results, this hypothesi More

In this thesis, modeling and forecasting of stock market fluctuations using the combination of neural network and conditional variance patterns (case, Tehran Stock Exchange) have been used from April 2008 to April 2012. According to the predicted results, this hypothesis is confirmed, but its accuracy is not as large as the composition of the neural network and the conditional variance pattern. In the return time series, both GRACH and ARCH conditional variances are rejected, but in the GRACH time series, ARCH is rejected. Given the artificial neural network simulation and conditional variance, the error value of the least squares is the return value of 18, that is, an error is required to estimate future returns. An important parameter of the opacifying factor is the dependence of our input and output at each stage, which indicates a number close to 1 and shows a complete dependence. According to the artificial neural network simulation and conditional variance, the least squares risk error value is 0.001, that is, to estimate the returns for the future, this error is error. Another important parameter of this regression table is R, which shows the dependence of our input and output in each stage, where 0 means a random relationship and 1 means dependence. Manuscript profile

Open Access Article

7 - Does oil price uncertainty affect the Tehran Stock Exchange index? Quantile regression approach based on wavelet transform
Ali Sargolzaei Narges Salehnia Massoud Homayounifar S. Mohammad Qaim Zabihi

10.30495/fed.2023.707978

Abstract Investigating the effect of oil price uncertainty on the Tehran Stock Exchange index is of great importance because with increasing uncertainty in oil prices, the systematic risk of the stock market index increases. On the other hand, in oil exporting countrie More

Abstract Investigating the effect of oil price uncertainty on the Tehran Stock Exchange index is of great importance because with increasing uncertainty in oil prices, the systematic risk of the stock market index increases. On the other hand, in oil exporting countries such as Iran, oil revenues are among the most important and influential factors in macroeconomic variables and, consequently, financial market indicators. The present study investigates the effect of oil price uncertainty on the Tehran Stock Exchange index using the wavelet-based quantile regression model (MODWT-MRA) during the period April 2011 to April 2021 in Iran.. The results of the model estimate showed that with the increase in oil price uncertainty, the Tehran Stock Exchange index will decrease. According to the results, the effect of oil price uncertainty on the Tehran Stock Exchange index in the first few quantiles has a smaller coefficient than the final quantiles, so the negative effect of oil price uncertainty on the Tehran Stock Exchange index in recent months is more than the first months. Also, the coefficient value on the component 5 scale is much higher than the component 1 scale; Therefore, the negative effect of oil price uncertainty on the Tehran Stock Exchange index in the short run is much greater than the value of this effect in the long run. This is because in the long run, the investor will adjust to the uncertainty. Manuscript profile

Open Access Article

8 - Modeling and Estimating the return of Tehran Stock Exchange using dynamic models
Zhila Rostami Shahram Fattahi Kiomars Sohaili

10.30495/fed.2023.700132

AbstractSince the creation of the stock market in the nineteenth century, many researchers have focused on research into stock price forecasting models and market returns. Statistical prediction models such as Arma, Arima, Arch, have been widely used but none of them ha More

AbstractSince the creation of the stock market in the nineteenth century, many researchers have focused on research into stock price forecasting models and market returns. Statistical prediction models such as Arma, Arima, Arch, have been widely used but none of them have had the desired result. Therefore, many researchers have recently considered the stock market as a nonlinear dynamic system. The application of nonlinear models as well as advanced techniques, although not many years have begun, but in a short time has been able to open its place in various sciences. The purpose of this study is to predict the stock index using the dynamic model averaging DMA and also the method of the dynamic model selective DMS and the use of quarterly data for the years 1380-1399. The main advantage of the model used in the present study is the introduction of a large number of independent variables for its dynamics without the usual problem of overfitting appearing in the model. In this paper, the effect of some macroeconomic variables on the process of modeling and forecasting stock returns on the stock exchange was investigated. The results of the article showed that the probability of entering the variables of money supply growth, quasi-money growth, inflation, land price index growth in large cities is more than other input variables. Manuscript profile

Open Access Article

9 - Designing a Model for Forecasting the Stock Exchange Total Index Returns (Emphasizing on Combined Deep Learning Network Models and GARCH Family Models)
Mehdi Zolfaghari Bahram Sahabi Mohamad javad Bakhtyaran

Given the development of machine learning models in predicting financial data in recent years, this study introduces a combination of Deep Learning Network and selected GARCH family models to predict short-term daily returns of the Tehran Stock Exchange Index. The most More

Given the development of machine learning models in predicting financial data in recent years, this study introduces a combination of Deep Learning Network and selected GARCH family models to predict short-term daily returns of the Tehran Stock Exchange Index. The most important feature of the deep learning network is that it can adapt and adjust itself to the volatility of market variables without being limited to specific models. In this study, short-term and long-term memory based neural network (RNN-LSTM) models are used for deep learning network models and GARCH and EGARCH models are used in its structure. Also, the two independent variables of oil price and dollar rate in the structure of the hybrid model help to predict the financial data more accurately. Comparison of the results of hybrid model prediction error with individual models shows that the RNN-LSTM-EGARCH hybrid model has higher prediction accuracy than competing models. competing models. Manuscript profile

Open Access Article

10 - Predicting the daily index of the Tehran Stock Exchange using the selection of appropriate features for the Long Short-Term Memory neural network (LSTM)
Somayeh Mohebi Mohammad Esmaeil Fadaeinejad mohammad osoolian Mohammad reza Hamidizadeh

The stock market index is one of the effective features in investment because it can well reflect the health status and macro change trend of a country’s economic development. Various features affect the stock index. The various combinations of these features crea More

The stock market index is one of the effective features in investment because it can well reflect the health status and macro change trend of a country’s economic development. Various features affect the stock index. The various combinations of these features create a wide state space. Hence, it is impractical to provide a data set containing all these combinations to train the stock index prediction model. in this research, an attempt has been made, after collecting a significant number of effective features on the index, to provide a method for selecting appropriate features for the stock index prediction model with aim of increasing prediction accuracy. For this purpose, the mRMR algorithm is used as the basic algorithm. Also, to select the appropriate model, a number of the most applicable artificial intelligence models for predicting the stock index were compared and according to the results, the LSTM network was selected to predict the stock index. The results of this study show that using the LSTM network and the proposed method in selecting features, with 8 selected features, high accuracy can be achieved in the daily prediction of the Tehran Stock Exchange Index. So that MPE is calculated to be about 2.66, Manuscript profile

Open Access Article

11 - "Analysis of the dynamic effect of oil, gold and stock market index on Iran's economy: a new approach with the SVAR-DCC-GARCH model"
tara heidari chavari mirfeyz fallahshams hashem ninoomaram Frydoon Rahnamay Roodposhti Gholamreza zomorodian

In the global economy, oil prices have been considered a key indicator of exchange rate fluctuations. This significance arises from the fact that international oil transactions are largely conducted in US dollars. Emphasizing this importance, the present article examine More

In the global economy, oil prices have been considered a key indicator of exchange rate fluctuations. This significance arises from the fact that international oil transactions are largely conducted in US dollars. Emphasizing this importance, the present article examines the dynamic relationship between oil prices, gold, and the stock index in the Iranian economy during the period from 1370 to 1401 using the SVAR-DCC-GARCH model. The results indicate that an increase in the growth of the stock index may lead to an increase in the price of gold, while having no significant impact on the oil market. Furthermore, increases in the gold and oil markets do not notably affect the Iranian stock market, and interestingly, there is no distinct correlation between the oil and gold markets. These findings vary throughout temporal fluctuations. Ultimately, employing the SVAR-DCC-GARCH model, this article analyzes the dynamic relationship between oil prices, gold, and the stock index in the Iranian economy, revealing that this relationship changes under different conditions over time. This contributes to a better understanding of the effects of fluctuations in these indicators on the Iranian economy. Manuscript profile

Open Access Article

12 - پیش بینی بازده شاخص بورس اوراق بهادار با استفاده از مدلهای شبکه ها عصبی مصنوعی شعاع پایه
رضا تهرانی سعید مرادپور

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Open Access Article

13 - Forecasting Volatility & Risk Management in Tehran Stock Exchange through Long memory impacts
ehsan Taiebysani Madihe Changi Ashtiani

In  this  paper  we explored  the  relevance  of  asymmetry  and  long  memory  in  modeling  and  forecasting  the  conditional volatility and market risk of equity market in Iran capital M More

In  this  paper  we explored  the  relevance  of  asymmetry  and  long  memory  in  modeling  and  forecasting  the  conditional volatility and market risk of equity market in Iran capital Market (Tehran Stock exchange(TSE) and Iran Fara Bourse(IFB)). A broad set of the most popular linear and nonlinear GARCH (generalized autoregressive conditional Heteroskedasticity)-type models is used to investigate this relevancy of asymmetry and long memory. Our in sample and out-of-sample results  displayed  that volatility  of commodity  returns can be  better described  by  nonlinear volatility models accommodating the long memory and asymmetry features. In particular, the FIAPARCH (Fractionally Integrated Asymmetric Power ARCH) model is found to be the best suited for estimating the VaR forecasts for both short and long trading positions. This model given a risk exposure at the 99% confidence interval level have Several implications for equity market risks, policy regulations and hedging strategies can be drawn from the obtained results of this paper. Manuscript profile

Open Access Article

14 - Analysis of Most Important Variables Affecting TEPIX and Modeling Them with Artificial Neural Networks and Comparing Results with Technical Analysis and Elliott Waves
Mohammad Kamravafar S. Zabihollah Hashemi

The main goal of this research is to studying an identifying the main influencing variables on the TEPIX (Tehran Stock Price Index) and modelling them using artificial neural networks and comparing results with technical analysis and Elliot waves. Independent variables More

The main goal of this research is to studying an identifying the main influencing variables on the TEPIX (Tehran Stock Price Index) and modelling them using artificial neural networks and comparing results with technical analysis and Elliot waves. Independent variables used are dollar exchange rate, inflation, GDP, unemployment and liquidity and dependent variable is TEPIX. In this study, artificial neural networks (NLP and GMDH), technical analysis tools (Elliot waves and regression channel) are used that they show between independent variables in GMDH, unemployment is unneeded variable and have low influence, but others have high influence in the model. Further the study shows that technical analysis and artificial neural networks may have same results, but ANN have more power to predict the TEPIX Manuscript profile

Open Access Article

15 - Iran Stock Market Prediction Based on Bayesian Networks and Hidden Markov Models
Zohreh Alamatian Majid Vafaei Jahan

Stock market behavior is one of the most complex mechanisms, considered by researchers. Financial markets are influenced by the external and internal factors. External factors such as political and social factors are not measurable, so prediction the trend of stock mark More

Stock market behavior is one of the most complex mechanisms, considered by researchers. Financial markets are influenced by the external and internal factors. External factors such as political and social factors are not measurable, so prediction the trend of stock markets is focused on internal factors. This study suggests a hybrid approach based on Bayesian Networks and Hidden Markov Models to predict trend of stock market. The used variables are 6 index of Tehran Stock Exchange, which have the most correlation coefficient with target stock, and 22 technical indicators. Bayesian networks are utilized to find the relationships between variables, and the effect of each variable in prediction considered from conditional probability tables. Hidden Markov Model is designed for sets of extract from Bayesian networks. The proposed model tested on four company’s stock names Mobarakeh Steel, Iran Khodro, Mellat Bank and Iran drug. The average accuracy of the proposed system is 83.26 %. The experimental results show that the suggested procedure has higher performance for prediction of stock markets in comparison with other previous methods. Manuscript profile

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