جستجوی مقالات مرتبط با کلیدواژه « Stock Price » در نشریات گروه « مالی »

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 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

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.

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

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.

In the financial literature, a bubble is a situation in which the market price of a commodity is traded at a significant difference from its intrinsic value. Determining the true value is difficult, usually after a sudden drop in price and the so-called bubble burst; They notice the bubble. The bubble is characterized by its temporary nature. In this case, investors rush into the market without considering the real market value of a commodity, which causes the market price of a commodity to be separated from its real value. Given the importance of this issue; the present study is based on the study of factors affecting the prediction of stock price bubble irregularity. In this study and in order to achieve the objectives of the research; Data of 99 companies were extracted for a period of ten years from the beginning of 2009 to the end of 2020, the research variables were calculated and the necessary statistical tests were performed. The method of this research is descriptive-correlational and its design is Fundamental using post-event approach. The findings indicated that among the proposed variables; Financial performance and Market Performance are effective in predicting stock irregularities.

The main reason that people invest in the stock market is to earn profits that require having accurate market information and stock changes and predicting its future trend. Therefore, the investor needs the powerful and reliable tools needed to predict stock prices. In this regard, the present study investigates stock price forecasts based on MSE mean square error, mean absolute deviation MAE and root mean square error RMSE. Finally, the methods investigated in this study are compared and identify the top method to predict stock prices. For this purpose, the data of the top 50 stock exchange companies, which are quarterly presented by the stock exchange organization, were used during the period 2012-2018. In order to test the research hypotheses, linear regression method, Brownian method and ARIMA method were used. The research findings show that the Brownian model predicts stock prices more accurately than the ARIMA method. It was also observed that linear statistical ARIMA models are less efficient in the financial markets than the brownian methods.

The aim of current study was to provide a model for predicting stock price reaction delay based on grounded theory. In the present study,semi-structured interviews have been used as data collection tools and snowball or chain sampling methods and purposeful sampling has been used to select the sample which based on the principle of theoretical adequacy The research data were analyzed using open, axial and selective coding. Results In this study, based on 42 conducted interviews, a total of 607 interview codes, 101 sub-categories (concepts) and 11 main categories were extracted. Then the qualitative model of the research is designed and based on the analysis of data (interviews) the link between the categories in the form of causal conditions, contextual conditions, intervening conditions, strategies and consequences has been conducted. The results indicated that macro factors and market shareholders are effective in predicting the stock price reaction delay.On the other hand, according to these affecting factors, strategies to improve the stock price reaction delay prediction, including the establishment of corporate information and financial statements, corporate information, market performance criteria, management and corporate control which are aroused in the context of affecting factors and interferers, are presented.

The purpose of this paper is to evaluate the two behavioral biases, overreaction and underreaction, of investors in the Iran stock market and to express their relationship with another bias called herd behavior.Meaning of behavioral biases in the capital market is situations in which a person with rational or irrational reasons, ignores his personal information and his influence on news, economical and political evolutions, etc.,won’t lead to an on time or sufficient or both reaction to the market evolutions and will cause a gap in the efficient market. In this study, Barberise et al.'s model was used to evaluate overreaction and underreaction, and the Laconishok model to examine the behavioral biases of investors. For this purpose, the daily data of stock prices and stock trading volume of companies listed on the Tehran Stock Exchange during the years 2013 to 2019 have been executed. The results of the this study show that the over and under reaction of investors and also herding in the Tehran Stock Exchange has existed during the review period.

According to capital market research, the negative shock of stock price in any market is a function of environmental factors and specific characteristics of the company and any insight into how to describe and predict the shock can influence the decisions of investors and stakeholders. In this study, based on the data related to 96 financial ratios of 140 companies listed on the Tehran Stock Exchange during a period of 9 years between 2010 and 3012, we have predicted a negative shock of stock price based on the meta-heuristic approach. In this research, in order to extract the optimal financial ratios, genetic algorithms and particle swarm optimization have been used. The proposed model is then tested using these extracted features by a support vector machine with a radial core and an artificial neural network. The results showed that the variables extracted from the particle swarm optimization algorithm, together with the support vector machine learning algorithm, create better results for predicting shocks (temporary and permanent) and their number.

The main purpose of this paper is to predict financial crisis in stock exchange market along with designing warning syetem by data analysis and then to present to financial policy makers for preventing the outbreak or decreasing the effects of crisis. Due to this purpose, it’s used the weekly datas during the years from 10.03.1997 to 03.22.2019. The mean of crises in this present paper is the falling more than 15% of stock price rather to last three months. Hence, it’s been used the dummy variable for operating the dependent variable. It’s been used the residual of autoregressive integrated moving average (ARIMA) for measuring the shocks caused by the index of stock price, exchange rate, price of oil and gold. Based on the results by the different data showed the financial crisis outbreak is the most important variable for predicting the crisis of Tehran stock exchange in weekly data in the last periods. Hence it’s claimed that falling the stock index is affected the value of index in the last periods more than external shock including shock of exchange rate as well as gold and oil. As it’s determined the detection accuracy of crisis is 81.82%, the same for all trees. It means that 36 crisises have been predicted and recognized from the 44 ocurred crisises during the mentioned periods (1121 weeks).

The purpose of this research is to manage downside risks and upside risk with exchange rates and stock prices. This research is descriptive in nature and in terms of its purpose. The statistical population of the research is the companies accepted in the Tehran Stock Exchange and the sample of the companies accepted in the cement and pharmaceutical industry, which can be extracted from the research data. The research period is from 1391 to 1396. This research has a theoretical model and the self-regression model was used to test the hypotheses. In the cement industry, the exchange rate variables are combined with adverse risk combination and optimal risk. But the stock price variable does not have this capability. Also, in the pharmaceutical industry, the exchange rate changes with the combination of undesirable risk and optimal risk of correlation. But the stock price variable does not have this capability.

The present study has designed and explained the stock price forecasting model using stochastic processes. The statistical population of the study is all companies of the mass real estate industry in the Tehran Stock Exchange from 1390 to 1398. Data were analyzed in Eviews10 and MATLAB software. Predicting stock price behavior and the whole industry index by autoregressive methods and moving average in terms of random processes showed that the explained pattern can not be used to predict stock price behavior but in some random steps, the forecasting error was negligible. Regarding the forecast of stock price behavior, the last three steps of the process, winter have a significant effect on stock price forecast; But the first step has a significant effect on predicting the behavior of the industry index. In the first steps, the error of predicting the behavior of the industry index is very small and the explained model can be used to predict the behavior of the index in the first months of the year.

This study is intended to investigate the effect of shareholder diversity and value of controlling shareholders on order imbalances and stock price effects concerning the moderating role of shareholder trust-based behavior. The statistical population of the research included companies listed on Tehran Stock Exchange during 2014 to 2018, of which 109 companies were selected as a statistical sample through systematic sampling method by elimination. The present applied study follows correlational research method for the data collection of which financial statements, explanatory notes and stock exchange monthly were employed. In order to analyze the data, the researchers first examined the normality of the data by pre-test (Jark-Bra test), and then, they used the multivariate regression test to confirm or reject the research hypotheses through EViews software. The results indicated that shareholder confidence-based behavior moderates the effect of shareholder diversity and shareholder value on controlling imbalances and stock price effects.

The purpose of this study is to investigate the factors affecting the stock price crash risk of listed companies between the years 2009 to 2016. Based on this, the researcher first calculated the stock price crash risk based on two different criteria and then tested the internal and external factors of the company on the crash risk. The statistical model used in this study is regression model and data type is panel data type. Dependent variables include calculating stock crash risk based on the first criterion, the Hutton model, which considers the monthly price fall in a fiscal year for a company when that company experiences a monthly return of 3.2 standard deviations below the company's average specific monthly return for the entire fiscal year and second criteria, Chen model, Based on the negative skewness criterion in the company's stock returns, Independent variables include in-company factors (financial ratios) and external factors. Based on the findings of the present study, there is a significant relationship between dependent variables and factors within the company and the organization external factors, can help in predicting stock crash risk.

In efficient markets, the release of announcements and news is effective in stock prices and the price is valuable information. Hayek has defined price as the point where information has been gathered for decision or access to knowledge associated with the decision-making problem through the price. Based on this view, the hypothesis of efficient markets has been raised that it, flow of information from inside to outside company affects shareholders' decision making, but based on managerial learning hypothesis there are flow of information from outside to Inside Company, where managers learn about price information and this learning affects decision making and company management activities. In this paper, the effect of the price on the behavior of earnings management was investigated by reviewing 623 data from 89 firms listed in Stock Exchange for the period of 7 years from 2009 to 2015. The results show that the price is effective on the behavior of earning management and Its relation with profit management based on accruals is significant and  negative And with earnings management based on actual activities, significant and positive. Also, each per share, size, percentage of institutional stakeholder's concentration, asset returns and leverage ratio have been effective on the behavior of earning management.

Much evidence suggests that time series such as stock market prices are complex and random, which makes their changes unpredictable. However, these time series are likely to be a nonlinear dynamic or, in other words, a chaotic process and can therefore be predictable. Therefore, in this study, stock prices and stock returns of Tehran Stock Exchange companies during the period 2014-2018 and monthly intervals were tested to determine whether these variables have fractal properties in their behavior. To achieve the above objective, our model estimation is used to explain the mass fraction of moving average. The findings of the above tests indicate that stock prices and stock returns experience a turbulent and definite process. This implies that the capital market is inefficient, and because of its long-term memory, it can be useful in predicting long-term performance and may have a guide to better understanding market failure factors such as the lack of transparency of information flow and action to address it.