درخت حوزه‌های تخصصی

In order to evaluate the companies of the cement industry active in the Tehran Stock Exchange (Iran), it is first necessary to identify the indicators of the industry. Note that some of the indicator values provided by the Exchange Organization to the users may have been lost for any reason. All of the existing models for calculating the efficiency of such units calculate the weight of the indicators related to each unit under evaluation independent of the weight of other units. So, in this study, researchers decided to develop a common set of weight models and propose a model that evaluates efficiency in the presence of heterogeneity decision-making units (DMUs) based on the common set of weight models. Finally, the proposed model evaluates 25 cement industry companies in the presence of heterogeneity DMUs of indicators and the results are being analyzed.

In this research, we have tried to investigate the effect of successive news of dis-tributed profits, negative adjustment and late announcement of an adjustment on the market reaction process in Iran. Following the design of the market response evaluation indicators, the transaction information was collected from the Stock Exchange in the five-year period of 2011-2015. The statistical sample consists of 125 companies selected by systematic elimination method and a total of 625 years-firms. To investigate the research hypotheses, linear regression and correlation have been used and for analyzing the data and testing the hypotheses, Eviews software has been used. What is summarized in the overall conclusion of the test of research hypotheses is that the successive news of distributed profits has an effect on the market reaction process;moreover, negative adjustment has a negative impact in the forecast of earnings per share and late announcement of the adjustment of the earnings per share on the market reaction process.Finally, the results indicated that the market's negative reaction to the late negative adjustment of the earnings per share forecasting is not lower than timely negative adjustment of earnings per share forecasting, and the positive reaction of the market to adjust the timely positive or zero forecast of earning per share, compared to adjusting the late positive or zero forecast of earnings per share is also no greater. the results obtained in this study are partly consistent with the documentation referenced in the theoretical framework of the research and financial literature

The accounting software is considered to be of the most critical components of accounting information system, with particular significance as of accounting and financial systems. the most important problems with accounting education systems is that students do not adequately learn the financial software required by the accounting profession, which, in turn, reduces the credibility and position of the accounting profession. That the main objective of accounting software education is to educate skilled and expert accountants to enter the accounting profession, which is considered as of the success factors of country’s economy. In this study, employ data mining techniques to investigate the accuracy, precision, and recall performance measures and to predict the rate of financial software learning based on accounting students’ emotional intelligence (EI), gender and education level. Accordingly, a machine-learning-based multivariate statistical analysis is performed on 100 Iranian accounting students. The results show that emotional intelligence has the most impact on the rate of financial software learning among the variables. Gender and education level were influential. Also, among the five algorithms, the highest precision and recall are achieved by both Decision Tree and XGBoost and are presented as the most appropriate models for the prediction rate of financial software learning.

In the research, pattern explanation of micro and macro variables on return on stock trading strategies has been dealt with. Based on data collected, existence of momentum and contrarian strategies in Tehran Stock Exchange market has been studied. To collect data and make statistical analysis, Excel Spread Sheet software, and statistical SPSS and R software packages have been used. Through usage made of various statistical models, the relationship between variable of return on stock and other variables added has been studied so that based on which stock trading strategy would be predicted, for the next 12 months. To do so, three statistical models of autoregressive time series (with no auxiliary variable), linear regression, and Markov-switching have been applied. Using the model’s fit criteria, these three models have been compared and best of them has been selected. Based on selected model, stock trading strategy for the next 12 months has been predicted. Markov model showed that within next 12 months, using contrarian strategy i.e. selling previous winners and purchasing previous losers can be profitable. According to the research findings, from among micro variables (base volume, trade volume, institutional investment, and free float) and from among macro variables (currency and inflation rates), only three variables of the first (base volume, institutional investment, and free float) are effective on stock trading strategy; and, they can be used as auxiliary variables to predict return on stock and to specify stock trading strategy in future as a result.

stock market is considered as the most profitable and valuable areas of investment in any country. In this regard, high return depends on the correct choice of stock portfolio.That’s why today different methods of mathematical planning and decision-making have been proposed to solve such problems. Aiming to present a new method, the study designates 10 criteria for selecting the best stock portfolio options among the 21 most viewed options in the stock market. The method is a combination of fuzzy SAW and experimental design (2k factorial design). Analysis of variance results for the response variable is calculated . The value of R2 obtained from the response variable of 70% value, shows that this model has selected suitable options by removing ineffective criteria and analysing the results and discovering the relationships between criteria and ranking the criteria and presenting simpler solutions in addition to high accuracy. As a result, by considering and comparing the real values of the stock market in one-month and quarterly intervals, the model presents more capabilities for providing accurate ranking and higher portfolio returns than fuzzy TOPSIS in the capital and stock markets. The response surface method and the regression equation obtained in the proposed method are used to rank the options. In addition, Pareto method, which ranks the criteria based on the effectiveness of the criteria in the final result and regard to the surfaces of experiments and weights of capital market and stock market experts, is used for ranking the factors (criteria).