Stock Price

this world; though all the discussions are focused on the causal relationships in allthe scientific arguments. One of the methods to study the designed causal relationshipsobjectively is Granger causality test. This paper aims to investigate the longtermcausal relationship between the stock price and dividends. The statisticalpopulation includes 180 active companies in Stock Exchange of Tehran during2010-2014. In order to analyze the achieved data statistically, the used specifiedmodel has been the regression model using the econometric data panel techniquesand to test the research hypotheses and find the specific relationships among thevariables, the descriptive-inferential statistics and Eviews software were used.Results indicated that the stock price is not due to the dividends; however, thedividends are the Granger causality of stock price. Also, the type of industry, firmgrowth index, and systematic risk index are of impact on the relationships betweenthe stock price and dividends.

Stock prices in each industry are one of the major issues in the stock market. Given the increasing number of shareholders in the stock market and their attention to the price of different stocks in transactions, the prediction of the stock price trend has become significant. Many people use the share price movement process when com-paring different stocks while investing, and also want to predict this trend to see if the trend continues to increase or decrease over time. In this research, stock price prediction for 1170 years -company during 2011-2016 (a six-year period) of listed companies in stock exchange has been studied using the machine learning method (Chaid rule-based algorithm and Particle Swarm Optimization Algorithm). The results of the research show that there is a significant relationship between earnings per share, e / p ratio, company size, inventory turnover ratio, and stock returns with stock prices. Also, particle swarm optimization (pso) algorithm has a good ability to predict stock prices.

  The main purpose of present study is to analyze the relationship between stock and exchange markets in two Asian countries, Iran and South Korea. A monthly time series of stock price and exchange rate are used over the period 2002: 05 - 2012: 03. The data is collected from the Central Bank of each country and WDI. The calculated stock return and real exchange rate change are used in analysis. An econometric multiple generalized autoregressive conditional heteroscedasticity (MGARCH) BEKK method and the Rats software are applied to analyze a dynamic relationship between two markets in each country. The estimated results show a bidirectional relationship between two markets in South Korean economy and only a unidirectional relationship from exchange market to stock market in Iranian economy. The persistence of volatility transmission effects of each market on its own is also found in each economy. In the exchange market, this effect is in opposite direction in Iran compared to Korea, whereas in the stock market both effects are positive and almost the same in two economies. The policy implication of finding is clear. The financial policymakers should watch both stock and exchange markets in two economies to prevent the bidirectional volatility effects between two markets in Korea and the unidirectional volatility from the exchange market to sock market in Iran.       JEL Classification : F31, G10  

The most common starting point for investors when buying a stock is to look at the trend of price changes. In recent years, different models have been used to predict stock prices by researchers, and since artificial intelligence techniques, including neural networks, genetic algorithms and fuzzy logic, have achieved successful re-sults in solving complex problems; in this regard, more exploitation Are. In this research, the prediction of stock prices of companies accepted in the Tehran Stock Exchange using artificial intelligence algorithm (non-sensory-parametric support vector regression algorithm in linear and nonlinear mode) has been investigated. The results of the research show that the PINSVR algorithm in nonlinear mode has been able to predict the stock price over the years, rather than linear mode.

The purpose of this study was to investigate the effect of peripheral uncertainty on the relationship between the herd behavior of investors and the price of negative stock shock. Because the capital market is one of the main pillars of the country's economic growth and development, the incidence of any disturbance and deviation in prices causes problems with allocating and equipping funds. Reducing stock prices is an example of the disorders created in the capital market. On the other hand, one of the factors that lead to the fluctuations of return and instability of financial markets is the herd behavior of investors. If investors lack sufficient information about environmental factors, environmental uncertainty occurs. Such a situation also affects the organization. Environmental uncertainty overshadowed the use of financial statements. Changes in stock prices and corporate status are cases that can not speak in full confidence in their occurrence. As a result, environmental uncertainty is part of the economic environment. To test the research hypotheses, 156 companies from the Tehran Stock Exchange during the years 2010 to 2020 were selected by screening method. To test the hypotheses, multivariate regression models were performed using STATA14 software. The results indicate that herd behavior has a significant effect on price shock and environmental uncertainty has no effect on the relationship between the two.

Stock prices are influenced by economic, technological, psychological and geopolitical factors. A review of the literature in this field shows that stochastic approaches, trend analysis and econometrics have been used to demonstrate stock market dynamics and price forecasting. However, these techniques cannot provide a comprehensive overview of market dynamics. Because they ignore the temporal relationship between these factors and are unable to understand their cumulative effects on prices. By integrating chaos theory and continuous data mining based on price acceleration, this study has eliminated these gaps by inventing a new price forecasting method called dynamic stock market recognition simulator and combining two methods: one is delay structures. Or gives time intervals to the data set, and the other is the method of selecting new variables for the market environment. The results showed that the method used can be used to predict the long-term stock price using a small data set with small dimensions.