حسین پناهیان

Textiles as a forerunner in the industrialization of many current developed countries to the post-industrial stage has fundamental role. Due to the occurrence of many problems in the Iranian textile industry, this industry has faced many hardships. The objective of this article is to develop an evaluation model for sustainable technology in the textile industry. The present research method is applied, in terms of (quantitative-qualitative) mix method. The qualitative sector is consists of the statistical population of elite in the textile industry, which semi-structured interviews were carried out with 10 experts in the textile industry. Their ideas were refined in two stages using the Fuzzy Delphi method and according to the library studies and expert ideas, the evaluating components of technology were identified and a researcher-made questionnaire was developed in the Likert spectrum. Its validity was measured using confirmatory factor analysis and reliability with combined method. The quantitative sector is consist of the statistical population includes all engineers, managers and university professors of the textile industry, of which 200 people were randomly selected as the sample size. Based on the results of the qualitative and quantitative sectors, the technology evaluating model has been drawn including 10 dimensions of economic, cultural, environmental, marketing, legal, organizational, political, technical, managerial and marketing. Obstacles and influential factors in evaluating technology are stated here with their consequences.

As the capital market becomes more competitive, one of the topics that has attracted the attention of many financial researchers in recent years is the liquidity of corporate stocks that because of the dynamics it can create in corporate financing, it is of strategic importance. The purpose of this research is designing a Model of Comprehensive interpretive/structural Mechanism of Effectiveness of Stock Liquidity Tehran Stock Exchange Companies. The one-year study period 2018-2019 in both qualitative analysis and quantitative analysis was conducted with the participation of two members of the panel. In the qualitative analysis section, this research identified through the combination of Delphi and the analysis of three components of the operational mechanism, the structural/governance mechanism, and the investor/mechanism of trading mechanism in the form of the effective statement on stock liquidity. And in the Comprehensive Interpretive / Structural Analysis section, with the participation of four Stock Exchange brokers, members of the panel presented a model based on a spectrum of the most influential statements to the least effective stock liquidity statements. The results show that the Delphi analysis of 25 indicators identified early in the meta-synthesis, 7 Index Remove and 2 indicators have been merged for a total of 16 statements were approved. In the quantitative section, based on a comprehensive interpretive/structural analysis, it was identified that the increase in the number of trading transactions as the component of operational mechanisms was identified as the most influential factor in stock liquidity.

In financial markets such as Tehran Stock Exchange, P/E coefficient, which is one of the most well-known instruments for evaluating stock prices in financial markets, is considered necessary for shareholders, investors, analysts and corporate executives. P/E is used as an important indicator in investment decisions. In this research, harmony search metaheuristic algorithm is used to select optimal variables affecting P/E and then, modelling is done through multivariate regression based on panel data. For this purpose, a sample of 87 companies has been selected from listed companies in the Tehran Stock Exchange during a 10-year period (2006-2015). The results indicate the effect of the variables of stock returns, stock price to book value ratio, price to net selling ratio, return on assets, earnings per share, market value to book value, money volume, operating return margin, return on capital, and current assets, as top ten variables, on P/E ratio, which estimates a total of 86% of the P/E ratio changes.

Efficient financial markets with high degree of transparency do not substantiate the hypothesis that there are differences in the volatility of return. Generally, there are factors rejecting any perfect similarity in the volatility of return in the emerging stock markets, as previous studies in Iran have confirmed the complete difference. On the other hand, the hybrid model PANEL-GARCH has the benefit of high process accuracy, suggesting that the evaluation of the similarity in the volatility of return at the level of market or industry constituent units is better than the simple technique of time series GARCH model for the entire market (instead of evaluation at unit levels). Therefore, the present study intends to investigate complete similarities or differences in the volatility of return in Iran's industries. Results showed that the assumption of complete difference in the volatility of return in the industries did not hold. The results of this process for Iran's industries covering the timespan between 16/2/2013 to 18/3/2017 showed that there are similarities in terms of the y-intercept of conditional mean and variance equations (1.1) PANEL-GARCH between the volatility of stock returns of 23 industries in the Tehran Stock Exchange as confirmed by LRT test. 

The present study aims to model systematic risk using financial and accounting variables. Accordingly, the data for 174 companies in Tehran Stock Exchange are extracted for the period of 2006 to 2016. First, the systematic risk index is estimated using the ARFIMA-FIGARCH model. Then, based on the research background, 35 affective financial and accounting variables are simultaneously used with the help of the backward elimination and forward selection method for modeling. After analyzing and evaluating the variables in Eviews software, the four variables of debt ratio (CL. E), size (SIZE), net profit to sales ratio (NETP. S), and interest rate coverage ratio (ICR) are selected in the backward elimination method. In the forward selection method, in addition to the above variables, operating profit margin (OPM) is also chosen. The estimated model of these variables in both methods shows a low ratio of R2 coefficient that is approximately 7%. In the test case, the model of forward selection method has less error in all four criteria of root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and Tile coefficient (TIC) compared to the backward elimination method.