جستجوی مقالات مرتبط با کلیدواژه « stock market » در نشریات گروه « مالی »

Initial public offerings (IPO) is considered an important turning point in the investment literature. This type of stock not only removes the constraints of resources and capital limitations faced by companies and provides the necessary basis for providing liquidity to companies, but also offers a wide range of investment strategies to companies. Therefore, the main goal of the present study is to investigate the Granger causality relationship between the number of IPO and macroeconomic variables including industrial production, the interest rate on government debt, stock market, and stock market volatility using the Vector Auto Regression (VAR) model during the period 1991-2019 for the Tehran Stock Exchange. The results showed that the number of IPOs is the reason for the growth of industrial production and the fluctuations of the stock index. It was also found that the number of IPOs was the reason for the growth of the stock index and the fluctuations of the stock market and interest rate due to the government's debt. This means that macroeconomic variables such as industrial production, interest rates on government debt, and the stock market are sensitive to small impulses from the number of IPOs and can have macroeconomic consequences in Iran. Based on the results, it can be acknowledged that the financial resources obtained through the number of IPOs of the companies should not be spent at the expense of the government and it is better to spend the financial resources on the development of these companies and the stock market.

Market sentiment or investor sentiment is investors' general perception towards a particular security or the entire financial market. Investors' emotions are considered one of the critical factors affecting the stock price, but the behavioral tendencies of humans have made the measurement of emotions face challenges; Therefore, the main problem of this research is to extract a quantitative index to measure sentiment tendency in Tehran Stock Exchange. This research is considered to be descriptive-correlational from the point of view of practical purpose and from the point of view of data collection. In this research, using the principal component analysis (PCA) method, an index was extracted to measure the emotional tendency of Tehran Stock Exchange investors based on monthly data during the period of 2019-2018. The emotional variables used in the final index are the number of stock transactions, the net cash flow of mutual funds, the ratio of small investors' transactions, the discount of investment companies' shares, the price-to-earnings ratio, the psychological line index, the relative strength index, and the volume of stock transactions.

One of the most important issues in financial knowledge which is related to portfolio selection, efficiency market and asset allocation is spillover effect between markets and this effect includes return, volatility and shock effect. These days every shock or volatility in one market effect on other markets. Correct identification of spillover effect is very important. This paper aims to measure and analysis spillover effect between stock, currency, gold-coin, oil and housing markets. For these purposes we collect daily data of stock, currency, gold-coin, oil and housing for the time period of 2009 to 2020.we used VARMA-BEKK-AGARCH model for estimation. Results show return spillover from currency to stock and from stock to housing and shock spillover from currency, gold-coin and oil to stock and also volatility spillover from currency and gold-coin to stock and from stock to housing. Besides result show leverage effect of shocks from stock to housing market.so we suggest in order to minimize investment risk we had better to evaluate the spillover effects in selecting markets(assets)for our portfolio.

In behavioral finance and economics’ discussions, financial worry and rumination are mentioned as two types of financial distress for investors. Although both worry and rumination are related to anxiety and other negative emotional status, but rumination focuses on bad feelings and past experiences, while worry focuses on the potential for bad events to happen in the future. Given the relationship between financial rumination and financial worry with people's financial status in the past, present, and future, in this research, was examined the relationship between financial background and demographic characteristics with these two variables. The statistical population is the investors of the Yazd Regional Stock Exchange, and due to the homogeneity of the population, a simple random sampling method was chosen using a questionnaire for 123 people as the sample size. The data collection and the confirmation of the relationship between variables and factors conducted through confirmatory factor analysis and PLS modeling technique. The conducted tests indicated the validity and reliability of the questionnaire. The results suggested that current and future financial status, and current financial and demographic factors have a significant effect on the perceptions and emotions of financial concern. Also, person’s background and demographic characteristics have a significant effect on the perceptions and emotions of financial rumination.

Predicting time series of financial markets is a challenging issue in the field of specialized studies of time series and has attracted the attention of many researchers. Due to the presence of big data, this issue has led to the growth of developments in the field of machine learning models. Due to the importance of this issue, in this study, by using the comparison of different machine learning models such as random forest approaches, support vector machine, artificial neural network and deep learning-based recurrent neural networks to investigate the ability of different machine learning models in prediction. The total index of Tehran Stock Exchange during the period 2013 to 2020 has been discussed. The prediction results of 1, 3 and 6 day courses for the out-of-sample period show that the machine learning method based on the long short-term memory (LSTM) network, a recurrent neural networks, has a better result compared to other models.

Similar to other main markets in an economy the stock market is also affected by various shocks that can negatively or positively affect the stock market and subsequently the national economy. Despite the fact that the economies of the Middle East are generally dependent on oil revenues, the change in the views of their politicians in recent decades and moving towards the replacement of incomes from other economic activities, such as capital market incomes instead of traditional income channels, has caused to pay special attention to the impact of different economic variables on these markets. In the meantime, the way the stock markets of the Middle East economies respond to various shocks, especially the shocks of competing markets such as the gold market and the foreign exchange market, has gained so importance. Therefore, the aim of the present study is to investigate the response of stock market returns of selected Middle Eastern countries to the shocks of parallel markets such as the foreign exchange market and the gold market as two competing markets. The estimated panel vector autoregression (PVAR) approach was used to estimate the model and impulse response functions (IRF) to extract the effect of shocks in the period from 2010 to 2021 using monthly data. The results show the positive response of the Middle East stock markets to the currency shock and the gold price shock. Increase in the exchange rate and the gold price lead the returns of the stock markets in the Middle East economies to increase at the first month. Moreover, the results of the variance decomposition (VDC) reveal that the most explanation in the variation of the forecast error variance is caused by the gold price variable and the exchange rate stands as the second.

In this study, the effect of government and stock market expenditures on income distribution has been investigated. The countries studied in this study are the selected countries of Mena (Iran, Algeria, Egypt, Tunisia and Jordan). 2018. The data panel method has been used to estimate the research model. The results of this study show that the logarithm of the ratio of the volume of transactions in the stock market to GDP with a coefficient of -0.02 has a negative and significant effect on the Gini coefficient. The volume of exchanges in the capital market increases the working capital of companies and ultimately their production. With the increase and prosperity of production, the total production increases and this issue increases the national income and as a result the government can Improve income distribution. Also, the logarithm of the ratio of consumption expenditures to GDP with a coefficient of 0.03 has a negative and significant effect on the Gini coefficient. Increasing government spending can have different effects on income distribution in two ways. In the direction of transfer payments to the people, so the distribution of income is improved No, they are not transferable, so the distribution of income does not improve.

Liquidity in the stock market expresses how close the stock is to cash. Since the Tehran Stock Exchange is included among the world's non-cash stock exchanges and the issue of stock liquidity is one of the main concerns of investors, therefore, in this research, an attempt is made to predict the liquidity of the stock exchange using deep learning models. The statistical population includes companies active in the Tehran Stock Exchange in the years 1394-1400, and 23 companies were studied as a sample. The transaction volume and value, stock turnover ratio, Amihood, the difference between the bid and ask prices (spread) and the relative spread were measured as liquidity measure and a fully connected neural network based on multilayer perceptron (MLP), mixed deep learning (MDL) model and classical linear regression (LR) model was tested. To measure the predictive power of the models, the mean squared error (MSE) and mean absolute error (MAE) measures were calculated, and t-test was used to compare the accuracy of forecasting methods. According to the results, the prediction error rate of MDL model is lower than the other two models, and the statistical tests also confirm the significance difference in the prediction accuracy of the models at the 95% confidence level, which shows the proper performance of the proposed hybrid model compared to two other models.

The daily observations of the total index of the Tehran Stock Exchange show that in the last few years, stock prices have been very volatile. This volatility can harm the economic environment of Iran. Modeling and predicting price volatility in this market can provide important information about uncertainty and risk to actors and thus help with managing possible unwanted changes in the field of financial investments. This, along with the rise in the value of the stock market in recent years, has caused the investigation of the issue of volatility to become increasingly popular among academicians and financial policymakers. Volatility and risk measurements are essential parameters in risk management programs and can affect a country’s economic activity and public confidence. These are also key parameters in studies that examine the relationship between the stock market, economic growth, and other financial variables. Tehran Stock Exchange markets have been volatile in recent years. Controlling the negative effects caused by stock price volatility has made it necessary to predict and model price dynamics for participants in this market. In this paper, the class of Parameter-Driven volatility models (stochastic volatility models) is used to predict the price volatility and calculate the risk of the price index in the Tehran stock market. Therefore, four stochastic volatility models were used. To make a comprehensive review, the asymmetry in the volatility (leverage effect) and the heavy tail of the stock price return distribution (with t- student distribution and Skew normal) have been included in the models. To estimate the models, the Gibbs sampling method was used, and to accurately compare the models, the test based on the posterior distribution of the models and the Bayesian factor was used. The results indicated that the canonical stochastic volatility model with Skew normal distribution (SNSV) is more effective than other stochastic volatility models in predicting the price of stock market volatility based on the Bayesian factor. Therefore, to analyze stock market risks using stochastic volatility models, there is no need to include the leverage effect in the state space of the Volatility equation. The SNSV model makes it possible to observe volatility and make predictions related to it, thereby improving market transparency and ultimately making diversification and risk management easier to implement. Also, the backtests of VaR and CVaR market risk assessment using Kupiec and DQ tests do not show evidence that the estimation is over or under the risk limit. As a result, the calculation of volatility and pricing with this model will lead to more precision risk management for professionals, especially fund managers who intend to include Tehran Stock Exchange stocks for asset allocation.

Contagion in financial markets takes place both because of fundamental or non-fundamental reasons like herd behaviors that can increase market risk levels and even end in inefficient allocation of financial resources. Thus, understanding the contagion and its dynamics will be critical for the participants of financial market. Hence, using network-based epidemic modeling, the study examined the dynamics of contagion in the Iranian stock market from 2011 to 2019 and short-term and long-term scales. To this end, first the correlation network of 46 Iranian stock market groups was developed and then analyzed using short-term and long-term contagion dynamics simulations. The results showed that the extent and speed of contagion is much higher in the short-term than in long-term and in long-term a significant number of groups can be immune to the contagion. However, in long-term the rate of return to pre-contagion status is shorter than in short-term

Trade and relations between countries are a necessary condition for the financial convergence. With this approach, the present study investigated the impact of the Iranian stock market on the stock market of Iran's trading partner in the MENA region. for this purpose, information about the total stock market index of selected countries was collected during September 2015 to June 2022 and index fluctuations calculated using wavelet analysis. Next, the VAR model was estimated and the Granger causality test was performed. Finally, the effect of the news on the country's stock market was investigated using GARCH models. The results of wavelet analysis showed that, the range of stock market fluctuations in MENA countries has increased. Based on the results of the VAR model and Granger causality test, Iran's stock market is unilaterally affected by fluctuations in the stock market of trading partner and OPEC member countries, including Kuwait, Oman, Qatar, Saudi Arabia, the United Arab Emirates, and Lebanon. However, there was no sign of Iran's stock market being affected by fluctuations in the stock market of Jordan, Bahrain, Egypt, Tunisia, and Morocco. Finally, the results of the conditional variance models indicate that the selected countries follow a similar asymmetric fluctuation pattern.

Identification and quantification of spillover effect in financial markets is one of the most important topics of financial knowledge. recognizing the spillover channels and measuring spillover effect in financial market will help us to prevent disorder and disruption in markets and by settling stable situation in markets will provide economic growth and welfare. this paper investigates and measure spillover effect at different economic sanction periods. Due to the history of economic sanction imposed by western country at some periods such sanctions were raised and at some periods such sanction declined so for more precise appraisal of sanction role in return spillover, we divided research periods into four sub periods included two periods of severe sanction and two periods of no severe sanction. In this order we collect daily data of sock, currency and gold coin markets for the periods of 2008 to 2022 by applying the VARMA-AGARCH model for measuring and analyzing. Results presents unidirectional volatility spillover effect from currency and gold coin market into stock market and bidirectional volatility spillover effect between currency market and gold coin market. by dividing research periods into four time periods, volatility spillover effect has been seen for second and fourth time periods which are proxy for severe sanction periods but volatility spillover effect has not been seen for first and third time periods which are proxy for no severe sanction periods. results showed direct and positive relation between intensity of economic sanction and volatility spillover effect in parallel markets.

Forecasting the stock market in each period has been difficult and the value of stock trading has been affected by various factors. Among these factors have been the oil and gas sector, especially in countries that depend on the revenue from their sales. On the other hand, the outbreak of Covid-19 pandemic has led to profound changes in both areas. Therefore, in this article, the relationship between oil prices and the Iranian stock market is examined with respect to the prevalence of the Corona pandemic. Understanding the behavior dynamics of the stock exchange value index can help to more accurately predict the future behavior of this variable. Accordingly, the main purpose of this paper is to analyze the asymmetric relationship between oil prices, as well as the simultaneous impact of the corona pandemic news and the rate of corona virus infection on the value of the Iranian stock exchange. In the present paper, nonlinear Autoregressive Distributed Lag (NARDL) in the time period (1399: 05: 15-1398: 11: 30) has been used. The results show that in the long run, rising oil prices have a significant asymmetric effect on stock exchange value. On the other hand, in the short run, the rate of Covid-19 patients has a positive relationship without meaning and the Covid-19 media index has a negative relationship with meaning with the value of stock exchange trading.

The aim of this study was to »identify and prioritize the features of Online Advertisements for Persuade Investors to the Stock Market«. in the first step, using qualitative content analysis method was used to identify the features of Online advertising in the stock market to investors. The content validity and reliability of the model were assessed by the content validity ratio index and kappa index respectively. In the next step, dimensions, components and indices of the model were prioritized by 8 experts using best-worst method. Finally, the identified indicators were classified into 2 dimensions, 10 components and 27 indicators. The results of the prioritization indicate the importance of content component in comparison with structural component. Moreover, among the dimension of the content features, the components of emotional attractions, quality, rational attractions, validity and ethical attractions and among the structural features of the quantity components, accessibility and user friendliness, apparent features, easy use of users and communication were recognized in the order of significance.

We investigate the impacts of the order book events on the prices of the 30 largest stocks in the Tehran Stock Exchange in the year 2020. The purpose of this article is to measure the price sensitivity to changes in supply and demand volumes and identify the factors affecting this sensitivity. Similar to the method proposed by Cont et al. (2014), by performing about thirty thousands of OLS regressions, we show that in a low-depth market, the mid-price returns are explained significantly by the order flow imbalance that represents the net change in demand, namely the difference between the volumes of events on two sides of the order book [7]. We also show that the use of the Order Flow Imbalance in the first three levels of the order book can increase the explanatory power of the model for stock mid-price changes, indicating that higher levels of the order book also affect stock price changes significantly. Moreover, the results confirm that the market depth has a negative effect on the power of events to move the stock price. In addition, we show that our results are robust to changes in months or stocks.

The purpose of this study is to investigate the relationship between changes in new communication technologies and fluctuations in the Tehran Stock Exchange index (or market risk). The issue is important because the development of financial markets and its impact on cyberspace and the field of information and communication technology, on the one hand can cause fluctuations and consequently abuse for some, and on the other hand can raise awareness and provide Proper information to reduce fluctuations, and therefore the subject requires proper management and the establishment of correct and efficient laws in this area. To answer the main question of the research, data of information and communication technology and stock price index fluctuations. have been used as the main variables and price fluctuations in the goods and services market, exchange rate fluctuations, gold price fluctuations and crude oil price fluctuations as control variables during The time period of 1991-2020 has been used. the results obtained from the ARDL method indicate that the ICT index has a negative and significant effect on the stock market fluctuations. In other words, the improvement of the ICT index over the past years has reduced the fluctuations of the stock market. Therefore, by properly managing and improving the performance of ICT indicators, the risk of financial markets can be reduced and fluctuators in the market can be prevented from being abused. The effect of control variables has been evaluated.

Although a positive mean-variance relation is a cornerstone of traditional finance theory, empirical evidence supporting it is controversial and mixed. According to behavioral finance theory, the mixed risk-return tradeoffs attributes to investor sentiment in the financial market. In this paper, we investigated the effect of individual investor sentiment on the mean-variance relationship in 103 Tehran Stock Exchange firms using the BSI index. Meanwhile, we examined the relationship between small and large companies, high and low-priced firms, and growth and value stocks. The conditional volatility of stocks was calculated with GARCH models, and the research hypotheses were examined using a panel data method. The results show that the risk-return relationship in the total sample, growth stocks, and high-priced entities are less affected by sentiments, but sentiments strengthen the positive mean-variance relation in value stocks, low capitalization, and low-priced firms. However, sentiment does weaken the positive relation in high-capitalization firms. According to the research results, in constructing portfolios based on variance, investors should consider not only the sentiment of investors but also the features of the share in terms of value and growth, the market value of the company, and the stock price of the companies.

Nowadays, financial derivatives play an important role in the development of financial markets and risk management. Financial derivatives markets are not only a tool for risk management but also a secondary market to attract small capital for implementing large projects. Countries with extremely volatile financial markets need some novel financial risk management tools. In this paper, the spread option is used as a tool for investment and risk management. First, we obtain a model by using stochastic differential equations and partial differential equations. Then the model derived from a stochastic differential equation with a jump term is transformed into a risk-free integral partial differential equation which represents the spread option price on the Libor interest rates since the model does not have a closed-form solution or analytical solution, the solution is estimated at discrete points by using the alternating direction implicit (ADI) method. The stability of the method is also proved. In the next step, the pricing model is implemented in MATLAB software and the results are illustrated. Finally, it is concluded that the ADI method is an efficient and appropriate method that solves the problems in pricing models caused by jumps.

The impact of international financial markets on development as well as their impact on economic growth in developed economies has caused emerging financial markets to become one of the topics of concern for researchers and economists, especially during the recent economic crises in most countries. The capital market as an economic institution plays the main role in increasing the efficiency and optimal allocation of capital, as well as providing the possibility of financing new projects and development plans and other operations in companies and the government; Therefore, studying the relationship between the capital market and economic growth is of particular importance. Therefore, the present study examines the existence of an inverted U relationship between the capital market and economic growth, as well as the effect of the stock market on Iran's economic growth during the period of 1975-2018 using autoregression with distributional lag (ARDL).The results obtained from the above model show that in the short and long term, the inverted U relationship between the financial sector and economic growth has the correct sign and is significant, but it does not have a necessary and sufficient condition; It was also found that the capital market had a positive and significant effect on Iran's economic growth in the short and long term. In addition, capital stock, education and government spending have had a positive and significant effect on Iran's economic growth.