سجاد فرازمند

Although herding behavior is mainly defined on the basis of imitation and repetition in existing theories, it is difficult to provide a mathematical model which is able to identify this phenomenon.

Therefore, in this paper, using the Monte Carlo method and stock price data of Tehran Stock Exchange and OTC companies, during the years 2011 to 2019, the herding behavior among the sample companies is investigated. Given that the Iranian capital market is facing the phenomenon of closure, and this can affect the values ​​of price herding, the results are examined with the New York Stock Exchange as a market developed.

The first finding indicates the presence of herding behavior in 29.6% of possible cases in the sample. The second finding indicates the presence of herding at an average of 4.07%. The third finding reflects the increase in the amount of herding along with the increase in absolute return, which shows that as the stock prices change, the values of herding also increases.Also, the results show that the herding behaves almost symmetrically with increasing the absolute amount of stock returns, the amount of herding behavior is first decreasing and then increasing. Accordingly, when price change are slight, the amount of herding is small; But with drastic increases, the average herding behavior also becomes positive and upwards, reaching 16%. This means that as the price of one stock rises, the prices of other stocks also tend to rise, and the sharper the price increase, the greater the amount of imitation of price behavior. A similar trend is observed when prices fall. As prices fall and negative returns intensify, the average rate of herding behavior also increases, and the higher the decline in prices, the higher the rate of herding behavior. As a result, the higher the absolute amount of price return, the greater the amount of herding behavior.The fourth finding of the study indicates the possibility of a relationship between the herding and trading volume. To examine this relationship in the face of a sharp increase in the volume and number of trades, price data are classified into 20 groups based on the number and volume of trades. Then the amount of herding behavior of each group was calculated. The results show that the relationship between both indices of trading and herding is positive and significant and with a sharp increase in the number or volume of trades, herding measure approaches its maximum value. Similar results on the New York Stock Exchange are described below.

Evidence of herding behavior in the New York Stock Exchange also shows that this phenomenon occurs almost twice as much as in the Iranian capital market. Faster and more coordinated dissemination of news and faster reactions to them in NYSE can be the main reasons for this. These results are consistent with a study by Hwang and Salmon (2004) in which the amount of herding behavior in the US market was higher than the South Korean stock exchange. It seems that due to the less trading halts in NYSE, the significant values ​​of price herding in that market mean that the values ​​obtained for the Tehran market are probably not affected by the trading halts and the results can be reliable.

The stock market agents should increase their prediction accuracy to maximize their returns, and it needs some advanced tools. In this article stock price of 50 companies in the Tehran Stock Exchange have been modeled using feedforward artificial neural networks. In this way, the daily stock prices are used from December 1384 to December 1394. The predictions accuracy are evaluated with four statistical indicators. The results show that accuracy of ANN predictions is very high. In some cases, although the prediction accuracy is higher, the correctness is lower. Therefore, in the assessment of the prediction, evaluation of the correctness has a significant contribution

One of the industrial problems is the phenomenon of wax precipitation of oil fractions. One of the ways to prevent wax precipitation is the use of chemical inhibitors to reduce the pour point. Studying the effect of additives on specific oil samples could help in solving the wax precipitation problems at various oil processing stages. Despite the studies that have been done, finding materials that can decrease the pour point and be economically acceptable from one view, and well optimized and acceptable from other chemical and environmental view is still an unsolved problem. So it’s important to use effective materials that can act on preventing wax precipitation,or on decreasing the amount of precipitate, thus as a result it can decrease the pour point temperature. In this study, the effect of industrial polymer additives and surfactants which are economically acceptable on the decrement of pour point temperature was studied using three oil products samples: light diesel, heavy diesel and the fuel oil, products of Esfahan Oil Refining Co. In this study, optimized type and concentration of additive was studied before additive was chosen. The results showed that the copolymer ethylene vinyl acetate 28% at a concentration of 500 ppm reduced the pour point temperature by 12°C for diesel, also the copolymer ethylene vinyl acetate 19% at a concentration of 300 ppm reduced the pour point temperature by 9°C for fuel oil. Also results of this study showed ethylene vinyl acetate copolymer can be used as the pour point depressant of diesel and fuel oil instead of kerosene and other refinery products.

Stock market prediction is important because successful prediction of stock prices may promise attractive benefits. This task is complicated and very difficult. In this paper, the predictability of stock market index with Adaptive Network-Based Fuzzy Inference System (ANFIS) is investigated. The goal is to determine whether an ANFIS algorithm is capable to predict stock market return and trying to find best architecture. We attempt to model and predict the return on stock price index of the Tehran Stock Exchange (TEDPIX) with ANFIS. We use six macroeconomic variables as input variables. The experimental results reveal that the model successfully forecasts the daily return of TEDPIX Index. ANFIS can be a useful tool for economists and practitioners dealing with the forecasting of the stock price index return.

The purpose of this paper is investigating the impact of servant leadership on followership effectiveness. Model variables are selected through experimental and theoretical study. The data are obtained by survey method, based on surveying the managers and employees of 22 selected governmental organizations, then the hypotheses are investigated. This research is distinguished from other similar researches in that, instead of individual correlation analysis and ignoring other circumstances, using advanced methods, the variables have been simultaneously and two-way analyzed. In order to do this, Eviews 7.1 and spss19 are used for estimating parameters by the use of OLS method; Since there is a possibility for existence of two-way relation between the variables, three stages simultaneous equations (3SLS) is used to estimate simultaneous impacts of servant leadership on organizational effectiveness. The quantitative results of quantitative estimation of the models by the Structural Equation Modeling showed that servant leadership has positive impact on followership effectiveness.