stock market

The purpose of this research is to provide a model of dynamic trading program in the capital market of Iran. Based on this, using theory, the opinions of 16 stock experts in the Tehran Stock Exchange Organization and investment companies as well as university professors were used until the theoretical saturation stage. In this research, open coding was done using the grounded theory approach and line-by-line analysis of the interviews. During the open coding, 58 items were obtained as initial concepts from the text of the conducted interviews, which were categorized into 16 categories. In the following, according to the research results, the research paradigm model was presented in six sections: causal conditions, main phenomenon, contextual conditions, intervening conditions, strategies and consequences. Finally, the validation and screening of the components of the research model was carried out and the fit of the model was confirmed using the questionnaire and the fuzzy Delphi method, and the model of the dynamic trading program in the Iranian capital market was presented.

Volatility analysis is considered a modern and efficient tool for estimating, managing, and hedging risk, valuing and selecting optimal portfolios, and aiding investors in making informed financial decisions. This research aims to present a model for the risk analysis of 30 large companies listed on the Tehran Stock Exchange using the Multivariate Factor Stochastic Volatility Model (MFSVM) within the framework of a non-linear state-space approach. In this framework, the volatility of stock returns is decomposed into two components, "volatility arising from latent factors" and "idiosyncratic risks". The dynamic correlation matrix of the volatility of stock returns is then estimated. In this regard, weekly stock return data from January 10, 2018, to October 7, 2023, were utilized. The results of the research indicate that the first three hidden factors influence the volatility of stock returns. The first factor impacts stocks in the oil products industry, chemical products, basic metals, mining, and investment funds. The second factor predominantly affects banks, while the third factor also influences bank stocks to some extent. Second, the strongest posterior pairwise correlations are observed between “GDIR” and “PTAP” (74%), “PASN” (73%), and “FOLD” (71%). Additionally, “FOLD” shows a 69% correlation with both “PASN” and “PTAP,” and a 66% correlation with “MSMI” and “MADN”. The weakest correlation is between “GDIR” and “BPAS” (-10%). Third- “BPAS” exhibits the lowest correlation within the stock network, whereas “GDIR” shows the highest correlation.

The expansion of technology and the increasing development of global financial markets have transformed cross-border markets (Forex and cryptocurrencies) into one of the preferred markets for investors and traders in Iran. Given the growing interest of investors and traders in cross-border markets, this study examines the relationship between these markets and the stock market, as well as the spillovers of volatility and return among them in Iran during the period from February 2019 to March 2023 using daily data and the DCC-AR-EGARCH method. The results indicate that Forex, cryptocurrencies, and the Tehran Stock Exchange are symmetric, sensitive to crises, and affected by negative news, while the currency market (US dollar) is asymmetric and influenced by positive news. Also, Forex, cryptocurrencies, and currency markets have a correlation with the Tehran stock exchange, suggesting that the growth of one market leads to the growth of another. The results of investigating spillovers of return and volatility between markets under study based on the Diebold & Yilmaz and Barunik & Krehlik indices indicate that there is a spillover of return and volatility from the Forex, cryptocurrency, and currency markets to the Tehran stock exchange during the period under study. The Tehran stock exchange market is the largest return and volatility spillover recipient, while the cryptocurrency market is the smallest recipient of volatility spillover. The results of the Barunik & Krehlik frequency spillover index also suggest that the Forex market is the largest sender of return spillover, and the currency market is the largest sender of volatility spillover to the stock market in Iran. The estimated results of this index indicate the predominance of return spillover from the markets under study to the Tehran stock exchange in the short term.

Health care systems around the world are facing significant challenges following the emergence of the Quid 19 virus and the development of acute respiratory syndrome. The changes that have taken place in the daily lives of the people after this epidemic have led to many challenges and changes; Iran's stock market has not been immune to the effects of this crisis. Therefore, the present study examines the impact of Covid 19 crisis on stock market turmoil in seven stock exchange industries of financial intermediaries, automobiles, rubber and plastics, chemical products, base metals, sugar and mineral products in the period from February 11, 2017 to February 20, 2017. April 1400 uses the Markov nonlinear model of switching in two modes before Covid 19 and after Covid 19. The results show that with the increase in the number of deaths due to covid virus 19, the total risk turmoil in the metals industry has increased and in the intermediary financial institutions, automobiles, rubber and plastics, chemical products, sugar and mineral products has decreased. Also, with the increase in the number of deaths due to covid virus 19, systematic risk fluctuations have increased in the industries of financial intermediaries and sugar and sugar, and in the industries of automobiles, rubber and plastics, chemical products, basic metals and mineral products have decreased. Improved covid 19 virus has a positive effect on the overall risk of intermediate financial institutions, rubber and plastics industries, a negative impact on the overall risk of basic metals, automotive, chemical products, sugar and mineral products, a systematic risk of intermediate financial institutions, automobile , Rubber, plastics, sugar and sugar have a positive effect and have a negative effect on the systematic risk of chemical, base metals and mineral products industries. 

The financial development of companies is a factor in attracting capital and a way for regional economic prosperity. Many indicators can be used to check the financial performance of companies. One of these indicators is the asset turnover index, which emphasizes both sales and company assets. This article aims to examine the financial development of important companies in creating the largest share of the added value of the province in terms of the growth of the industry's output. BACE and bootstrap quantile approach have been used to investigate the importance of the financial development of these companies on the output growth of the province's industry. In fact, modelling means choosing the model and variables which are uncertain. Therefore, the Bayesian averaging approach was initially used to reduce the concept of variable selection uncertainty to an acceptable level. After identifying the top 5 models and the most important effective variables, the quantile method was used to examine the effects of selected variables in high, medium and low growth on the model and their importance coefficients. To make estimates, the data of the desired industries were collected from 1389 to 1398 on a semi-annual basis. Based on the presented results, financial development with the help of improving asset turnover of Isfahan Pegah, Isfahan Zob Ahan, and Isfahan Polyacrylic companies affects the growth of Isfahan industry output. The financial development of Pegah Isfahan Company has had a negative relationship with the growth of the industrial output of Isfahan province. However, the financial development of iron smelting and polyacrylic companies have a positive relationship with the growth of the Isfahan province industry.

Policymakers often create significant economic uncertainty when they fail to reach consensus on addressing a problem or when they repeatedly change economic policies. This process, known as economic policy uncertainty (EPU), can affect various sectors, including the stock market and its returns. Conversely, given that policymakers seek the results of their actions in the stock market and may be influenced by political pressures and vested interests, stock returns can also induce EPU. Therefore, investigating the relationship between EPU and stock market returns can reveal important policy implications. Accordingly, this study uses continuous wavelet transform and time-series data from 1990-2023 to analyze the relationship between EPU and stock market returns in Iran within the time-frequency domain. The results indicate that, in the short-run, medium-run, and long-run, stock market returns have historically caused EPU. However, this effect has ceased after the 2010s. Since the 2010s, an increase (decrease) in the effect of EPU has led to a decrease (increase) in stock returns.

The purpose of this research is to identify and ranking the factors affecting the dynamic trading program in the Iran's capital market. In order to achieve the goals of the research, the opinions of 16 stock experts in the Tehran Stock Exchange Organization and investment companies as well as academic professors were used by using the non-random judgmental method until the theoretical saturation stage. The current research was conducted using the mixed exploratory research method in qualitative and quantitative sections. At first, in the qualitative section, open coding was done using the content analysis method and line-by-line analysis of the interviews. During the coding section, 34 items were obtained as primary concepts from the text of the conducted interviews, which were categorized in the form of 9 sub-categories and three main categories including underlying, interventionists and strategies factors. In the quantitative section of the research, the views of the experts were analyzed through the questionnaire tool, and then the data was analyzed using the fuzzy hierarchical process, and the priority of the components in each of the main categories was determined. The results showed that the underlying factors include time analysis, pattern analysis (trend) and price analysis, respectively; Intervening factors include trading psychology, focus on short-term or long-term returns, and liquidity; And the strategies also include comprehensive risk and profit management, comprehensive analysis and precise identification of entry and exit points.

The role of financial development as a prerequisite for economic growth and development on environmental pollution has remained ambiguous. Also, the direct impact of financial development on climate change has merely been investigated in a limited number of foreign studies. Therefore, the current study delves into examining the joint influence of financial progress and pollution on climate change. Data encompassing economic, environmental, and climate change aspects from 1990 to 2021 across 99 developed and developing nations have been collected, and a regression model has been employed utilizing the unbalanced panel technique. The results validate the robust positive impact of the evolution of the banking sector and the relatively weaker positive influence of the growth of the stock market on the mean temperature, serving as an index for climate change. Furthermore, the affirmative correlation between pollution metrics and the mean temperature is affirmed. The differentiation in this association across diverse climates, according to the De Martonne climate classification, highlights its intricate nature. There is a more immediate impact in arid and semi-arid climates compared to the delayed effect observed in humid and semi-humid climates. Consequently, the progression in financial sectors is likely to increase the mean temperature both directly and indirectly (via pollution), particularly in these specified climates. Effective policies need to be implemented to guide the financial sector towards allocating capital to socially responsible industries, environmentally sustainable projects, and eco-friendly production. This should also include avoiding financial support for polluting ventures.

In the last 10 years, we have witnessed two major crises in the Iranian exchange market. During this period, Rial has lost substantial part of its value against USD and led into many economic problems. Another observation that exists during this period is the increase in herd behavior in the stock market. The simultaneity of these phenomena brings to mind the question of whether the exchange rate can be the cause of herd behavior in the stock market. In this study, an attempt has been made to provide a suitable answer to this question by using comprehensive modeling approach based on FAVAR. In order to achieve this goal, seasonal data of 62 economic variables of Iran and the world have been used between 1380 and 1400; Also, since herd behavior is an unobservable variable, in the first step, using the method proposed by Huang and Salmon (2004), this variable was quantified, and then in the second step, the effect of the exchange rate on this variable was examined in the form of the FAVAR model. The results show that herd behavior has intensified in the stock market in the last decade and reached its peak in 2018 and 2019. Also, the results of Impulse Response Functions and Variance Decomposition analysis show that the exchange rate has a relatively high contribution in explaining the forecast error variance and for several periods it significantly leads to the increasing of herd behavior. The sensitivity analysis regarding the number of factors indicates the robustness of the results and emphasizes the positive effect of the exchange rate on the herd behavior of the stock market.

The herding behavior in financial markets is perceived as one of the behavioral biases among investors, and investigating the influential factors effecting it, can have significant impact on investors' decision-making and efficiency in financial markets. Given the importance of this topic, in this study, herding behavior in the Tehran Stock Exchange is examined using the Cross-Sectional Absolute Dispersion (CSAD) method during the period 2005 to 2021. Furthermore, the impact of changes in exchange rates and interest rates on herding behavior in both bullish and bearish market conditions is tested. The results indicate that in the overall period, an increase in the exchange rate and interest rate lead to herd behavior in the market, while with a decrease in the exchange rate and interest rate, no herd behavior is observed. Similarly, the obtained results in bearish markets are similar to the estimates in the overall period, while this phenomenon is not observed in bullish markets. Therefore, it can be concluded that the effects of increasing exchange rates and interest rates on herding behavior are mainly evident in bearish markets.

Economical growth and development require having vast and efficient financial markets, which from an economical perspective, are divided into two parts: money and capital markets. The exchange market is the main principle of a capital market. In investigating the function and efficiency of the exchange market, the price index of the stock shows the status of the stock market. In general, the factors affecting the stock price index of companies could be divided into two categories: macroeconomic and microeconomic variables. This research zeroes in on the macroeconomic factors that affect the stock price index. The present study will, first, introduce the variables that affect the stock price index, and then, examines any nonlinear relationship between the exchange rate and Tehran Stock Exchange Index from 1991 to 2020 in Iran. In this regard, information was collected from the Statistical Center of Iran (SCI) and economic reports of the Central Bank of Iran and Hansen’s Threshold Regression Technique (1996 and 2003) was utilized. The results of the Threshold Model show a nonlinear and significant relationship between the exchange rate and Tehran Stock Exchange Index, in such a way that the threshold exchange rate is estimated at 12068.071 Tomans. It means, therefore, when the exchange rate drops under the estimated threshold, this variable (because the foreign currency will also be attractive for investors in such a situation) will have a more positive and significant effect, compared to when the exchange rate is over the estimated threshold.

Technological advances indicate a new era of analysis and data-driven decision-making in capital markets. Blockchain, which is the main technology of digital currency markets, is being introduced as a disruptive technology, especially in the stock market. Today, there is a broad consensus among the majority of stock market actors about the significant power of blockchain technology in the position of a new approach to managing and exchanging data in financial markets, and especially maintaining reliable and transparent data records in stock markets, considering the huge volume of transactions that are carried out in them. has it. On the other hand, the two main factors that cause representation problems in the corporate governance of traditional stock exchanges are the difference in the interests of investors and representatives and the complexity of the investment chain. The purpose of this study is to investigate the benefits of implementing blockchain technology as a basis for stock transactions in order to improve the corporate governance of publishers through a two-stage review consisting of a systematic review and an online Delphi. The findings of the research show that compared to traditional stock trading systems, blockchain technology has several advantages, including high transparency of information, improvement of stock liquidity, high monitoring by different parties, availability of real-time information, reduction of price divergence from stock value, provision of accounting comparability. And it is preventing profit management, the final output of which will be the creation of better corporate governance. The implementation of blockchain technology will be able to reduce the information asymmetry in the relationship between the manager and the owner, which in turn can improve the quality of corporate governance due to transparency, accountability and high trust between all parties involved in the network

Monetary policies affect macroeconomic variables and financial indexes through changes in importance parameters of macroeconomic. Therefore, it is necessary to test the effects of changes in these parameters in a simulation environment. In present paper, the effect of monetary policies on Iran’s financial market has been simulated with a system dynamics model and using data from 2011 to 2021 (and simulated until 2031). The results showed that some monetary policy variables, such as the increase in interest rate, have an effect on the distribution of financial assets between the bank, the stock market, and non-productive assets, and reduce the capital market size, and others, such as increasing in bank credit, foreign asset in central bank and government debts will increase all financial markets. The increase in the bank interest rate, despite the increase in deposits in the bank, will reduce investment in the stock market and increase the trend towards non-productive assets such as dollars and gold. Also, government financing through borrowing from the central bank increases propensity to non-productive assets and increases the inflationary cycle.

Following the spread of the Covid-19 virus at the end of 2019, governments have used a series of strict policies and non-pharmaceutical interventions (NPI) such as social distancing and mandatory quarantines to deal with the increasing spread of this virus, and therefore the trading process of global markets, Including the stock market, it was severely affected. In the following, the start of public vaccination affected various industries and groups and led to changes in stock market transactions of countries, including the Tehran Stock Exchange. The present study, by applying the Smooth transmission regression (STR) nonlinear model and daily data during the period of the Covid-19 epidemic (30 February 2018 to 10 January 2018), investigates the role of public vaccination in the relationship between the interventionist policies of the government and the stock market. In the present study, relying on tests to detect nonlinear behavior, the existence of a nonlinear relationship between government interventions and the stock market index was confirmed, the variable of the number of vaccinated people was selected as a suitable transition variable, and the nonlinear smooth transition regression model with a two-regime logistic transfer function With one transfer (LSTR1) was considered as the proposed model for this relationship. The results of the estimation of the research model show that the increase of strict measures of the government in the form of a two-regime structure with a threshold level of 20857, the number of vaccinated people in the first regime (that is, when the number of vaccinated people is less than the threshold value (20857)) had a positive and significant effect on the stock market index, but after crossing the threshold level and entering the second regime (that is, when the number of vaccinated people is greater than its threshold value (20857)), the mentioned variable had a negative and significant impact on the stock market index.

One of the important problems in examining economic growth is equating the frequency of variables affecting production, which approach leads to the loss of information on explanatory variables that have a high frequency compared to economic growth. In this study, by using the MIDAS and MIDAS-VAR method, the effect of Tehran Stock market performance on the economic growth of Iran during the period of 2011 to 2021 has been investigated for quarterly and monthly data. The results of the estimates indicate the in the first month of the seasons when the number of stock market booms was high, stock returns had a very positive and significant impact on economic growth. This positive effect decreased with the increase in the number of recessions in the third months and in the second month of every quarter when the number of recessions in the stock market was high, and this had a negative effect on economic growth. Also, the empirical findings indicate that the intensity of the influence of stock returns was stronger in periods of recession than in periods of prosperity. Therefore, the most important policy recommendation of the current research is that in order to increase economic growth, it is necessary for economic policy makers to provide the basis for the growth and development of the stock market and increase its efficiency by adopting appropriate policies, and also prevent recession and its prolongation in the stock market.

The concept of this study is related to different stakeholders which include policy makers, financial market participants, asset managers and company managers. First, policymakers and government agencies should be aware that uncertainty related to economic policies (monetary policies, fiscal spending, regulations, changes in taxes, national debt, etc.) can have decisive effects on investor sentiment and asset prices. Government agencies should provide uninterrupted communication with elected officials to help clarify the government's policy stance on economic matters. This limits uncertainty about the future direction of certain government policies, which should lead to less volatile and better priced financial assets. Second, financial market participants and asset managers should be aware of changes and direction of change in policy uncertainty and position their portfolios in response to changes in policy uncertainty, which can have short-term and long-term effects on the stock market. Asset managers and investors with long-term mandates and time horizons should be equally concerned about policy uncertainty and its asymmetric effects. Finally, corporate managers may use policy uncertainty to help them time their capital raising activities. Therefore, by taking advantage of policy uncertainty, firm managers may time their firm's capital raising activities to minimize their firm's cost of capital and maximize firm value. Therefore, considering the importance of the capital market in the country's economy on the one hand and the occurrence of numerous shocks in the field of monetary policy, the main purpose of this study is to investigate the effect of monetary policy uncertainty on stock market uncertainty in Iran. In this study, according to the type and purpose of the research, it is applied and in terms of controlling the variables, it is a non-experimental and descriptive-correlation research. Conducting research in the form of inductive analogy and its information is event type. In this study, information on Iran's economy during the years 1368-1400 has been examined. The source of the data used is the database of the World Bank and the Central Bank of the Islamic Republic of Iran, and ARDL and NARDL methods have been used to estimate the desired models. It should be mentioned that Eviews11 software was used to estimate the model. The research model with Elha is specified from the two studies of Battabial and Killins (2021) and Fu and Lu (2021). The results of the ARDL linear model show that in the long term, the uncertainty of monetary policies causes the intensification of uncertainty in the stock market. On the other hand, in the non-linear ARDL model, the results showed that monetary policy uncertainty shocks do not have an asymmetric effect on stock market uncertainty in the first place, and secondly, negative shocks have a positive effect on stock market uncertainty in the long term. Other results showed that government size and economic growth do not play a role in stock market uncertainty. While, the exchange rate, inflation rate and interest rate have an effect on the uncertainty of the stock market. This research was conducted with the aim of investigating the effect of monetary policy uncertainty on stock market uncertainty in Iran using the NARDL approach. In this study, in addition to using the NARDL approach, the ARDL approach has also been used, and in some ways, the linear and non-linear approach has been compared. In the nonlinear model, the positive and negative shocks of monetary policy uncertainty were calculated using the NARDL approach and included in the model. The results of linear and non-linear models showed that the uncertainty of monetary policies affects the uncertainty of the stock market in the long term. In other words, the uncertainty in the application and implementation of monetary policies increases uncertainty in the stock market. The results in the non-linear approach showed that; First, the effect of negative and positive uncertainty shocks is not asymmetric. Second, only the negative shock of monetary policy uncertainty has an effect on stock market uncertainty. In other words, with the occurrence of a negative shock in the uncertainty of monetary policies, the uncertainty in the stock market increases. The results of other variables showed that economic growth and government size, despite their importance in the formation of trust in the capital market, have an insignificant effect on stock market uncertainty. On the other hand, the occurrence of inflationary conditions in the country, both due to the inflation rate and the increase in the exchange rate, causes an increase in uncertainty in the stock market. However, an increase in interest rates will reduce uncertainty in the stock market.

This study measured the investor sentiment to understand its role in the behavior of the Tehran Stock Exchange and its relationship with the stock price during a monthly period from March 2016 to March 2022. The returns on some TSE-listed indices were compared to analyze the relationship between the investor sentiment and the stock price. This study improves our understanding of the price formation process at purchases or sales caused by sentiments acting as the major determinant of the stock price change. Statistical evidence indicates the impact of sentiments on the price formation process in specific TSE-listed indices. The autoregressive integrated moving average (ARIMA) was employed to estimate parameters and the behavior induced by the investor sentiment. According to the results, they respond asymmetrically to sentiment changes and trade more aggressively when sentiments reduce. There are also significant relationships between some of the market variables and the investor sentiment. Finally, comparing the sentiment index with each price index of the market will lead to the conclusion that there is a positive significant relationship between the sentiment tendency and the price formation process at the Tehran Stock Exchange.

The current research aims to answer the question as to by which channels the oil shock is transmitted to the stock market and how it spreads in the stock market. In this regard, the Factor-Augmented VAR approach, and also the global price index of commodity groups, macroeconomic variables, indices of large listed industries, and the total index of the Tehran Stock Exchange (for the period 2004-2016) have been used. Hence, the action-reaction functions are extracted in order to study the transmission mechanism of the oil price shock and its propagation in the stock market of Iran as an oil-exporting country. The findings show the impact of the oil shock on the stock market through three channels: (a) direct channels (b) global commodity price channels and (c) macroeconomic channels. Furthermore, based on the effects of the oil shock, the way of reaction from these channels is different. Accordingly, in the process of spreading the oil price shock in the stock market, the index of the group of petroleum products, chemical products, basic metals, metal ore extraction, multidisciplinary industrial, banking, and cement sectors, and consequently the total stock market index, increases significantly and steadily.

This study investigates the connectedness between the dynamic fluctuations of the exchange rate uncertainty and the fluctuations of various industries in the stock market in the time and frequency range (short-term, medium-term and long-term) during the monthly period of 2013-06-2023:05 in Iran. Using the time-frequency connectedness approach developed by Diebold and Yilmaz (2009, 2012) and Barunik and Krehlik (2018), the results show a significant connectedness between macroeconomic variables uncertainty fluctuations and fluctuations of various industries in the stock market in the short and medium term, while this connectedness decreases in long term. In the time domain, exchange rate uncertainty acts as a transmitter of shocks to different sectors of stock market. In the frequency domain, fluctuations of the net uncertainty spillover of the exchange rate in the medium term are more than in the short term and long term. This shows that exchange rate uncertainty in the medium term transmits more shocks to sectors of different industries of the stock market. Also, in the short term, the chemical, cement, basic metals, investment, and pharmaceutical sectors, in the medium term the petroleum products sector, and in the long term, the petroleum and chemical products sectors are more vulnerable and more affected by the exchange rate uncertainty spillover. It is recommended that the policy maker adopt macro policies and the investor to gain profit in the stock market should pay more attention to the sectors that are more vulnerable in the conditions of the exchange rate uncertainty in a different range of frequency