جستجوی مقالات مرتبط با کلیدواژه « قیمت سهام » در نشریات گروه « علوم انسانی »

Risk contagion between financial assets indicates the process of transferring information between markets. In this research, in order to manage the risk of investors in the capital market, hedging the risk of stock prices with gold during the outbreak of the Corona disease has been investigated. For this purpose, DCC and ADCC models were used. The data used are the price of Bahar Azadi coin and the company's stock price on a monthly basis during the period of 2017 to 2018. The results show that there is an asymmetric correlation during the research period between the price of Bahar Azadi coin and the stock price of selected companies of the chemical and basic metals group, and the optimal risk coverage ratios have increased significantly in all companies during the Corona period, which means that the cost of coverage It has a higher risk during the Corona period. Also, the findings of the research show that before Corona and during the Corona crisis, the highest risk hedging efficiency is related to Fakhas company, which shows that Fakhas has the highest efficiency in using gold for risk hedging among different companies. The lowest value before Corona and during the Corona crisis is related to the Shiran symbol. This case shows that Sheeran's symbol is the least efficient in using gold to cover risk among different companies. The results of this research provide an opportunity for investors to optimally use risk hedging and asset allocation strategies.

In this study, the effect of key issues of risk-based auditing at the company level and at the account level on the stock price synchronicity with respect to governmental and non-governmental institutional ownership was investigated. For this purpose, 121 companies were surveyed during the period 2012-2021. In this study, the variables were extracted from Rahavard 3 software and summarized, classified and calculated by Excel software, and finally the hypotheses were tested using Eviews and Stata software at a confidence level of 0.95. Findings showed that the key issues of risk-based auditing at the company level and at the account level have a negative and significant effect on stock price synchronicity. The results also showed that institutional ownership and non-governmental institutional ownership have a negative and significant effect on the relationship between key issues auditing and stock price synchronicity. In addition, the results showed that government institutional ownership has a positive and significant effect on the relationship between key issues auditing and stock price synchronicity.

This study investigates the impacts of corporate governance on the interaction between the agency costs and information efficiency of stock prices. In this research, the agency issues measured by the interaction between corporate growth opportunities and its free cash flows. Four corporate governance mechanisms examined in this research are financial expertise and independence of the board of directors, internal audit and institutional shareholders. The sample consists of 130 companies from 1394 to 1400. The results show that agency cost has a significantly negative effect on the information efficiency of stock prices. Moreover, the findings confirm that among the four corporate governance mechanisms, only institutional shareholders significantly moderates the negative effects of agency cost on the information efficiency of stock prices. However, we could not find any evidence on the moderating role of internal auditor, board financial expertise and independence. The results of this research showed that the growth opportunities and free cash flows of companies are among the most important variables affecting the information efficiency of stock prices in the Iranian Stock Exchange.

One of the basic dimensions in the discussion of financial instability is the volatility of asset prices. The reason for the change in asset prices is not only the policies of the central bank, but examining its impact interprets an important part of its changes.
In this research, based on the method of Bayesian averaging and principal component analysis, the monetary policy index effective on the stock price has been determined and through the TVP-FAVAR model, the effect of this variable on the stock price has been investigated in MATLAB software.
According to the results, the changes of one standard deviation in the monetary policy index over time on stock prices have been U-shaped. Changes of one standard deviation in the monetary policy index had a positive and strong impact on stock prices in the beginning to the end of the period.

The spread of the corona virus has led to negative reactions of financial markets in different countries and the fall of indices. The purpose of this research is to investigate the stock price index in selected groups of the capital market in Iran in this period and provide a model to predict the index. In this research, using the panel data regression method using Arch. GARCH has been used to investigate the stock price index in twelve selected groups of stocks with twelve different hypotheses in 189 companies in a two-year period (2008-2018). The results showed that Corona has a negative and significant impact on the price index of all groups except the pharmaceutical group. Also, vaccination has had a negative and significant effect on the price index of most groups. However, this factor did not have a significant effect on transport, communication devices, multi-field holdings, basic metals, and also technical engineering services ultimately. This means that the spread of this disease and its results, such as the number of deaths, have had major consequences on the return of the capital market in Iran.

Financial markets are considered to be a kind of complex network due to the interaction and interrelationship of its various actors. Therefore, the development of stock communication networks is an important issue for discovering potential connections between different companies. Therefore, the purpose of this research is to investigate and analyze the communication structure of companies in the capital market based on price, return, and trading volume of stock with a network approach.
 
This study employs a quantitative research methodology, specifically focusing on network analysis. The data were collected in the period between 2017 and 2021 and analyzed using the network analysis method and with the help of Excel 2021, SPSS 26, UCINET 6, and its complementary package NetDraw. In addition to the general analysis and review of the price, returns, and trading volume of the stock correlation network, the performance of each node in the network was also investigated using micro-indices. Centrality is one of the most important micro-concepts of network analysis, which examines the influence and importance of people in the network. Also, it was studied with three indices of degree, betweenness, and closeness of centrality of network nodes.
 
The results indicated that as the correlation basis expands, there is a decrease in communication between different companies along with an increase in irregularity. The higher the correlation basis, the less likely it is to be affected by a common variable or interaction, which will reduce the correlation between stocks. In addition, there are companies in the communicative network that are in a better position than the rest. They may have higher connections, faster access to information, and greater influence over other units' price changes. As a result, these companies can play a decisive and leading role, or in other words, the role of key and influential actors in the governing structure. Also, the results showed that in the second period of 2021 compared to the first period, communication networks were much more erratic and scattered.
 
Considering the importance of capital market communication networks and the decisive role of key companies, relationship pattern analysis helps to increase transparency, reduce risk, and, consequently, improve decision-making, policy-making, portfolio optimization, etc by revealing the network structure of the capital market. The results of this research can lead to a better understanding of the forces that create the communication structure of companies and enable policymakers to make better decisions. Therefore, all users are suggested to pay attention to the communication structure governing the companies in the capital market and the role and impact of their position on the advancement of the company's goals in their decisions and to consider this communication structure and the way power is distributed.

The purpose of the present study is to review the effect of positive information shocks on investors’ behavior and the risk of stock price crash in corporations accepted in Tehran Stock Exchange. This research has a practical objective and it is an ex-post facto study conducted by descriptive-causal method. The research statistical population consisted of all companies accepted in Tehran Stock Exchange from 2009 to 2011. Random sampling method with systematic deletion was used. For data collection and hypothesis testing, the researchers used the audited financial statements of corporations accepted in Tehran Stock Exchange (available in the library of Tehran Stock Exchange). For data analysis, multivariate linear regression technique was used. The research results indicated that positive information shocks have a significantly positive effect on investors’ behavior and the risk of stock price crash. Positive information shocks decrease disclosure of information, increase information risk, and decline information transparency which ultimately accelerate the crash risk in the stock exchange. In addition, the effect of positive information risks on the crash risk is under the influence of corporation features, supervisory mechanisms, and economic environment. According to the obtained results, it is suggested to the analysts to study the impact of information shocks on important variables effective in the performance of corporations and give the results to the users. Furthermore, minor investors should pay special attention to prevent concealment of bad news by the directors because stockholders' attention is oriented to others’ fuss and their non-expert comments which have filled the market environment and increase the crash risk of the stock price.

The present research aimed to investigate the relationship between Iran’s stock price index and nine macroeconomic variables during 1996–2019. Three methods were employed to reduce uncertainty, namely three Bayesian averaging methods (BMA, BMS, BAS), weighted average least squares (WALS), and Vselect. The experimental results of the Bayesian methods and WALS showed that the exchange rate and the consumer price index are the most important variables among the nine macroeconomic variables considered in the model. Moreover, the results revealed that the exchange rate has a minor impact on the stock price index, while the stock price index exerts a substantial effect on the exchange rate. The findings of Vselect validated the conclusion that these two variables are the primary drivers of stock price estimation and are present in nearly all predictive modelsIntroductionThe harmonization of financial markets with the macroeconomic sector is crucial for stabilizing the economy and achieving the adopted policies. In recent years, several significant studies have been conducted on financial markets, particularly the stock market, highlighting their pivotal role in allocating capital resources efficiently in advanced economies. Empirical evidence supports the view that financial markets have evolved in tandem with all sectors of the economy. Therefore, it can be argued that financial markets constitute one of the most vital components of any country’s economy. Throughout history, major economic crises have resulted from the collapse of financial markets, which underscores their critical significance. The financial market comprises several components, with the stock market being a crucial part. Economists view it as a barometer of a country’s economic health due to its ability to reflect macroeconomic asset prices more accurately than other markets. The uncertainty surrounding stock prices in stock markets is a significant aspect of the entire economy, capable of generating and disrupting unsustainable growth. For investors, the risk of participating in an investment is a crucial consideration. To comprehend total risk, it is beneficial to examine two aspects: systematic and non-systematic risk.The present study aimed to examine the impact of economic factors on stock market prices in Iran with the high degree of risk involved. There is a consensus among economists that asset prices are responsive to economic news, and that stock prices and economic factors are strongly interconnected. Thus, this research investigated the potential impact of macroeconomic factors on the Iranian stock price index from 1996 to 2019 using Bayesian averaging methods, followed by an analysis of the effect size of each variable through the weighted average least square method (WALS).Materials and MethodsResearchers often draw conclusions based on the assumptions of their selected model, assuming that it can accurately predict real-world situations. However, this approach may overlook true uncertainty, leading to non-conservative conclusions. Statistical models comprise two parts: variables and assumptions, and the model selected based on these assumptions to estimate the variables. Uncertainty exists at both levels. For instance, a researcher estimating the impact of influential factors on an independent variable may choose a model based on their assumptions and report their estimates. But is this the best answer? Another researcher with different assumptions may opt for a different model with lower variance and error. In other words, numerous models may fit the sample data equally well but with different coefficient estimates and standard errors. Bayesian model averaging (BMA) is a robust method that aims to remove uncertainty. It assesses the robustness of results to alternative specifications by computing posterior distributions for coefficients and models. This study employed three models of BMA, BMS, and BAS, using various averaging methods to verify the reliability of the results. Moreover, two non-Bayesian methods, namely WALS and Vselect, were used to select the best variables for predicting the optimal models.ConclusionThis study tried to investigate the relationship between Iran’s stock market index and nine macroeconomic variables during 1996–2019 by using the models that identify and limit uncertainty. The models selected include three Bayesian averaging models as well as WALS and Vselect which were used to verify the results obtained. The results indicated that only two variables, the exchange rate and consumer price index, are statistically significant when assuming a uniform distribution of the prior distribution function, which is the assumption of the BMS method. The remaining variables are not statistically significant. Furthermore, the estimates derived from the BMA and BAS models were quite similar, with the exception of less important variables. However, the similarity decreased in the BAS method. Moreover, WALS and Vselect confirmed the results obtained from all the three methods.

The capital market is growing rapidly. This has led to higher demand for information, more effort to predict and invent new models for predicting the future of the market. Predictive models can be classified into three categories. The first group uses technical analysis, the second group uses fundamental analysis, and the third group uses data mining and machine learning. In the present study focusing on data mining method to compare the accuracy of selected machine learning models including neural network, logistic regression, k nearest neighborhod, support vector machine and cross validation to predict stock prices for 12 selected companies of Tehran Stock Exchange that They have been selected through systematic deletion method in the form of machine learning models and the results of this paper showed that among the machine learning algorithms, the support vector machine algorithm has the highest predictive power in stock prices.Keywords: stock exchange, forecast; Stock prices, algorithms, machine learningJEL Classification Code: C8, G1

Predicting stock prices is complicated; various components, such as the general state of the economy, political events, and investor expectations, affect the stock market. The stock market is in fact a chaotic nonlinear system that depends on various political, economic and psychological factors. To overcome the limitations of traditional analysis techniques in predicting nonlinear patterns, experts over the last two decades have used intelligent techniques, especially networking. They have used artificial neural networks and genetic algorithms to improve stock price forecasting. This study, considering the increasing development of forecasting methods in financial markets and also, since stock price is one of the most important factors influencing investment decisions and its forecasting can play an important role in this field, In this research, an attempt has been made to provide a model based on which the movement of the stock price can be predicted with high accuracy. Accordingly, a hybrid model for predicting stock price movement using artificial neural network is presented. For the statistical sample, the top companies of the stock exchange in the second quarter of 1399 have been selected. Then for each purpose, 32 variables were calculated. These variables are the input of the model and are optimized using the artificial neural network algorithm. The results show that the model performs much better in predicting the movement of stock prices and has a higher accuracy compared to traditional methods.

In recent decades, the role of the capital market and the expansion of financial markets had a relatively high relationship with the economic growth of countries. Countries such as America, Japan, England, South Korea, Singapore and other developed countries have made great use of these financial markets and especially the stock exchange for economic development and growth. In this research, by conducting the ARDL test in the period of 1991-2020, we investigated the long-term and short-term relationships between the variables, with a one percent change in the variables of liquidity and consumer price index, respectively, 26. 4.0.34% will be added to the stock price index of Tehran Stock Exchange. the coefficients of all variables can be interpreted at a significant level of 5%. with a one percent change in the variables of liquidity volume and consumer price index, 1.46, 0.52 percent will be added to the stock price index of Tehran Stock Exchange, respectively. Meanwhile, with a one percent change in monetary base variables and financial development, the stock price index of Tehran Stock Exchange will decrease by 0.86% and 0.67%, respectively. it can be said that with increasing liquidity and after that, the price level increases, the value of assets and production inputs of companies and economic institutions increases. If the price increase of listed companies' products is more than the growth of production costs, the profits of companies will increase and inflation from the future cash flow channel of earnings can have a positive effect on stock prices.

The stock price is one of the most important factors that motivates investors to buy or sell stocks. However, the presence of challenges in explaining stock price behavior due to the influence of various internal and external factors is an inevitable issue in any capital market. Among the most important fundamental analysis tools in pricing discussions are dividends and the price-to-earnings ratio. This study evaluates their informational role in the form of dividend payout patterns and lagged price-to-earnings ratio. Data from 113 profitable companies listed on the exchange over eight-years (2014-2021) were collected. In this study, a multiple linear regression model (with fixed effects panel data) was used and three variables of size, growth, and financial leverage were also controlled. The findings indicate that cash dividends, particularly dual dividends with a majority cash component, have a significant positive impact on the end-of-period stock price. However, bonus shares, whether individually or combined with cash dividends as dual dividends with a majority bonus component, do not significantly influence the end-of-period stock price. Furthermore, an increase in the lagged price-to-earnings ratio corresponds to a significant rise in the current period's stock price. These results highlight the stronger informative role of cash dividends compared to bonus shares, even in the case of dual dividends where a stronger price reaction to the cash majority is observed. Additionally, the study confirms the informational content of the price-to-earnings ratio and provides evidence for its predictive power in forecasting changes in stock price in subsequent periods. Overall, this research contributes to the understanding of stock price behavior in the Tehran Stock Exchange and highlights the importance of considering dividend payout patterns and the price-to-earnings ratio in investment decision-making.

The purpose of this study is to provide a model for stock prices on the Tehran Stock Exchange. Statistical population: The statistical population of this research consists of all companies listed in the Tehran Stock Exchange Organization from 2006 to 1398. In this research, the sampling method is applied externally. 

First, it was performed in the qualitative section using fuzzy Delphi analysis. In the second stage, EViews software was used for causal effects. Research data are analyzed using generalized torque or GMM technique in panel data.

showed that the variables of accounting information, stock market characteristics, quality of accounting information, specific characteristics of the company, corporate governance mechanisms, characteristics of managers, characteristics of auditors and macroeconomic variables affect stock price changes. Achieving long-term and continuous economic growth requires the equipping and optimal allocation of resources at the level of the national economy, and this is not easily possible without the help of financial markets, especially large and efficient capital markets. Investing in stocks offered on the stock exchange is one of the most lucrative options in the capital market.

This paper aims at investigating the asymmetric impact of long-term and short-term macroeconomic variables on the capital market prices of Iran.Macroeconomic variables are inflation, exchange rate, non-oil trade balance and crude oil prices. In order to investigate these relationships, the quantile autoregressive distributed lag (QARDL) method introduced by Cho et al. (2015) has been used. For this purpose, monthly data related to Iran's economy in the period 2008: M9-2021: M6, have been used. Findings show that in the short run, the macro variables used except the trade balance and oil prices have an asymmetric effect on the capital market price index. In the long run, all variables except oil price have an asymmetric effect on the stock price index and the effect of oil price is symmetrical and significant. This conclusion shows that in situations where the stock market price index is in a state of prosperity, recession or normal, except for oil prices, the effect of research variables on this index is not the same and even this effect is different in the short and long term.

This study aims to investigate how and to what extent stock price crash risk is affected by various factors and tries to design a model to predict this relationship in the Tehran Stock Exchange. Accordingly, Besides reviewing the literature on stock price crash risk, 12 experts were selected from the statistical population of the capital market for the qualitative part using the theoretical sampling method and the gradual selection rule. Once the data were collected by referencing documents and interviews, the target model was extracted by MAXQDA18 software. The systematic random sampling was used to select 100 companies from a statistical sample of listed companies on the Tehran Stock Exchange over 2009-2019 to test the research hypothesis. Then, a quantitative model derived through the qualitative method was tested as structural equations using the PLS software.The results indicate that it is possible to achieve a model for predicting the stock price crash risk using a mixed (qualitative and quantitative) approach and providing a method to assess its fitness accuracy. and respectively the seven variables obtained from the qualitative part; Financial variables, business strategies, managerial ability and information asymmetry as internal factors and macroeconomic variables, political relations and social responsibility as external factors affect the stock price crash risk.

Causal relationships and dynamic interactions between macroeconomic variables and stock price index are very important in formulating macroeconomic policies and political policies of a country. In this article, the effect of economic variables on the behavior of the stock price of the company admitted to the stock exchange was investigated. The research method of this article is based on the purpose of the article (examining the effect of economic variables on the behavior of the stock price of a company admitted to the stock exchange) and is of an applied type. An attempt was made in this article; By explaining the theory and designing a model and using the econometric methods of the TAR threshold self-return model, the effect of economic variables on the behavior of the stock price of the company admitted to the stock exchange was investigated. In reviewing and compiling the subject literature, statistical tables and global databases were used to collect the required statistics and quantitative information. The statistical population of this research is all the economic variables of the research, which are the data of the research variables from 1370 to 1398 (a period of twenty-nine years). The results of the analyzes using the econometric methods of the TAR threshold self-return model, there is a significant relationship between the economic variables and the stock price index in the stock exchange, but in the regime of the behavior of the variables on the stock price index in the stock exchange. Is different.

This study has conducted to assess the role of customer pressure on the company's stock price response to the announcement of participation in green supply chain programs. The present study is applied in terms of purpose and is a survey in terms of how it is performed. The data used in the present study is the available financial information of companies that were listed on the stock exchange during the years 1390 to 1398 and have not been removed from it. The research data were analyzed using control charts and estimation of logistic and linear regression models. The results showed that, as a measure of customer pressure, the type of industry affects the participation of companies in green supply chain management. There will be a reduction in the stock prices of companies by announcing the participation of them in management of the green supply chain. The results also show that in indirect industries where the pressure from customers is more on announcing the company's participation, announcing the company's participation in green supply chain activities will have a lower reducing effect on its stock price

The present study represents an attempt to examine the main macro determinants of stock prices in OPEC oil exporting and importing countries in the study period 1996 to 2016 using Bayesian model averaging (BMA). The oil importers in this study are the United States, Britain and Japan, and three countries, Iran, Saudi Arabia and Kuwait, have been selected as oil exporters. The findings of this study are that to predict and evaluate the stock price index for oil-importing countries, the three variables of exchange rate index, consumer price index and economic growth should be given more importance than other variables, while for oil-exporting countries, The three variables of broad money growth, exchange rate and import are the most important variables that should be considered. For oil-importing countries, among the macro variables studied, OPEC oil prices have a completely negative relationship with the stock price index of those countries, but in oil-importing countries, the gold price has a completely inverse relationship with the stock price index.

If the asset market is information-efficient and people behave rational, asset prices reflect available information about expected events. On the other hand, choosing the right policy is important in economic stability.Since traditional models have failed to identify correct and appropriate policies, using time varying methods, can select the policy according to the current situation.Based on Bayesian averaging method, the variable total government expenditures was determined as the most fragile variable affecting the asset prices. so, in the TVP-FAVAR model, the effect of this variable on the price of each asset in different time periods in MATLAB has been investigated.According to the results, expansionary fiscal policy, in the long run, has had a positive effect on the price of each asset. This effect on the exchange rate variable has intensified in recent years. It should be noted that based on the results in recent years, fiscal policy has had a negative impact on housing prices and the stock market and has reduced the price of these assets. Also, the fiscal policy shock in short run, on the stock market, in medium term, on the housing index and in long run, has the highest impact on the exchange rate.