درخت حوزه‌های تخصصی

Forming a portfolio of different stocks instead of buying a particular type of stock can reduce the potential loss of investing in the stock market. Although forming a portfolio based solely on past data is the main theme of various researches in this field, considering a portfolio of different stocks regardless of their future return can reduce the profits of investment. The aim of this paper is to introduce a new two-phase approach to forming an optimal portfolio using the predicted stock trend pat-tern. In the first phase, we use the Hurst exponent as a filter to identify stable stocks and then, we use a meta-heuristic algorithm such as the support vector regression algorithm to predict stable stock price trends. In the next phase, according to the predicted price trend of each stock having a positive return, we start arranging the portfolio based on the type of stock and the percentage of allocated capacity of the total portfolio to that stock. To this end, we use the multi-objective particle swarm optimization algorithm to determine the optimal portfolios as well as the optimal weights corresponding to each stock. The sample, which was selected using the systematic removal method, consists of active firms listed on the Tehran Stock Ex-change from 2018 to 2020. Experimental results, obtained from a portfolio based on the prediction of stock price trends, indicate that our suggested approach outperforms the retrospective approaches in approximating the actual efficient frontier of the problem, in terms of both diversity and convergence.

Today, health tourism is a growing phenomenon, especially in developing countries, which has been greatly affected by the Covid 19 crisis. After overcoming this crisis and re-competing in this industry, our country also needs to develop capabilities. Take your marketing step and move towards being financially agile. Agility, or the ability to adapt quickly and in a timely manner to changing international markets at low cost and high added value, is therefore cost-effective. The present study was conducted with the exploratory mixed research method and the purpose of designing and validating the agile financial marketing capability model in the health tourism industry in Iran. The statistical population includes health tourism industry experts and prominent professors in the field of finance who have worked in medical centers that provide health tourism services. The research findings led to the identification of 14 main concepts that were presented in the form of a paradigm model and the central category of agile financial marketing capabilities (specialized and structural capabilities) was empirically examined with real data and confirmed.

One of the most critical indicators of one-factor productivity is the labor productivity index. This index has various and broad applications in the dimensions of economic policy. On the one hand, the labor productivity index determines per capita income levels and living standards. On the other hand, efficiency combined with other factors such as capital stock can make technical changes. The issue of how monetary and exchange rate variables affect labor productivity has been of particular importance in recent international studies. Due to the importance of the issue, this study investigates the asymmetric effect of exchange rate and bank facility rates on labor productivity in Iran in 1971-2018. The results of model estimation by self-explanatory method with nonlinear autoregressive distributed lag (NARDL) indicate that in the short and long run, the effect of free-market exchange rates and bank lending rates on labor productivity is asymmetric so that reductions in exchange rates have a significant direct effect on labor productivity and increases are not significant. In addition, increases in bank lending rates have a direct effect and reductions have an adverse effect on labor productivity.

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.

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.