Herding behavior among security analysts is described as similar behavior by analysts when forecasting main finance ratios of public companies and when giving investment recommendations. This type of behavior can be divided into two categories based on the different driving forces behind the analysts’ herding behavior. . For instance, if one analyst lacks the ability to research and provide recommendations, he or she may follow, or even copy, a famous analyst’ s reports. Such action will result in herding behaviorIn this paper, we build undirected weighted Networks to study herding behavior among analysts and to analyze the characteristics and the structure of these Networks. We then construct a new indicator based on the average degree of nodes and the average weighted clustering coefficient to research the various types of herding behavior. Our findings suggest that every industry has, to a certain degree, herding behavior among analysts. Furthermore, we relate the two types of herding behavior to stock price and find that uninformed herding behavior has a positive effect on market prices, whereas informed herding behavior has a negative effect.

The indices of stock market are one of the most important factors affecting the decisions of investors in financial markets. Most investors in the Tehran stock Exchange pay attention to TEIPX index, which includes all companies is listed to the stock exchange. This study introduces a new index using a complex Network method. Complex Networks provide price correlation in stock market and thus creating a better understanding of market performance for investors. In this study, the stock market Network with data from 246 stocks of Tehran stock Exchange during the first trading day of March 2016 to the last trading day of March 2017 was created, in which the WTA approach was used to connect two nodes or stocks. The results of the degree distribution of the stock market Network indicate that the stock market Network of Tehran stock Exchange is a scalefree Network, which clearly shows that stock market price changes are highly influenced by a relatively small number of stocks. Hence, by using the complex Networks analysis in stock market, a new index based on the degree and the inclusion of selected stocks is presented and compared with the TEPIX index. According to the results, the new index has a significant correlation with the TEPIX index and can reflect well stock market behavior.

This article has tried to simulate the behavior of national Iranian copper industries company’ s stock in the stock exchange market and help the share holder and policy makers to analyze the fluctuations and forecast the future of the stock. For the aim of simulation, first we have identified the influential factors in stock price in the stock exchange market and the influential factors incopper price in the market. The relation between different variables is shown by causal diagrams using system dynamics approach. Then the research model is simulated and analyzed by Vensim DSS. The results showed that production costs and copper’ s world price are the most important factors in shaping the fluctuations of the stock price. At the end different scenarios such as reducing the subsidies in the second phase are examined and the results are discussed.

This paper is focused on the prediction of a stock market price behavior by an innovative model with combination of artificial neural Network (ANN). For this purpose, three types of data that reports daily in Iranian stock market have been used. The structure of this hybrid model consists of two-levels: base predictors in the first level, are responsible for forecasting daily data with different characteristics of a stock i.e. three independent neural Networks for prediction of stock price, option volume and rate of return are used. On the second level, the other Networks, as a Combinator, the final prediction and analysis of predictive information of the first level will be done. Experimental results on one set of Iran’s stock data showed the superior performance of the suggested model in comparison with current predict model.

Target leverage is one of the challenges of capital structure studies. Some researches proof targeting behavior, while some others estimate random financial leverage. This study examines the leverage behavior based on four leverage measures for 108 companies in Tehran stock Exchange over the observation period 1380–1393 (2001-2014). In this paper, we analyze four different models, including stationary target ratios model, time-varying target ratios model and target zone model to study the stochastic nature of leverage behavior. We apply simulation methods with leverage measures to evaluate the ability of random financing and a variety of leverage targeting models. Target leverage models with little or no emphasis on staying near a particular target provide a good explanation for leverage behavior. Target leverage models explain total book leverage more precise than other leverage measures.

One way to help investors, companies and others are involved money market and capital, prediction models about the overall prospects of companies and so that investors can make good decisions [12]. Presented theories and models to predict abnormal returns indicate that there is no absolute consensus. One of the methods is very good at forecasting the financial variables such as stock prices, stock returns, stock market crash and the use of neural Network approach. The major advantage of this method over other methods can be found in this issue that better data consistency is maintained [54]. In this research to predict abnormal returns stock of two artificial neural Network and fuzzy neural Network approach was used in this way accurately predict abnormal returns are examined by this tool. Input variables to predict abnormal returns include earnings forecast, the degree of financial leverage, return on investment, accounting transparency, conservative accounting, the value of the brand and management have been too confident. For this purpose were examined, 452 companies-year screening method for a period of five years (2017-2012) of the companies listed in the Tehran stock Exchange. Findings demonstrate the power of predictive artificial neural Network, fuzzy neural Network to predict abnormal stock returns was more than that with the margin of error is less.

The stock market plays an important role in the economic development of countries. Network analysis is one of the latest methods in analyzing the stock market. It is a new concept for a macro view of the whole market in quantitative science literature. Therefore, this research analyzes the available Shareholder Network in the Tehran stock Exchange from 2013 to 2017. This research is based on a type of data collected and analyzed is quantitative research. And, its’ type is Network analysis. The research results indicate that many of shareholders are connected to each other, although a class structure governs their relations. Some of the shareholders, in comparison with others, have a better position. Having a better position caused them to encounter fewer mediators in gaining access to other shareholders, and also easier access to available resources. The shareholders’ ability in gaining access to information through the cluster of Network members enhances too. Therefore, it is claimed that these shareholders can play the role of key actors in the governing structure. Also, the results of the Pareto distribution indicate that the distribution of power among the Shareholders is approximately 25/75, that is, 75 per cent of the strength in the hands of 25 per cent of the Shareholders.