حسین پناهیان

Risk parity is perceived as one of the stock portfolio selection models that have received a lot of attention since the US financial crisis in 2008. The philosophy of this model is to allocate the same amount of portfolio risk between the constituent assets. In the present study, the combined portfolio selection model of relative robust risk parity is introduced, which uses the worst-case scenario approach on the covariance matrix parameter appearing in the robust risk model in portfolio robustness. According to historical data, several scenarios are considered for the covariance matrix. The objective function value of the hybrid model for each portfolio (feasible point) is the worst result (with most volatility) among the set of scenarios.  Finally, the model selects a portfolio for which the worst possible result has the least relative volatility. The research portfolio consists of 8 industries from Tehran Stock Exchange in the period 2011 to 2020. This portfolio has a higher Sharpe ratio than conventional models of mean-variance and weight parity, and is more resilient to market declines than the two models and produces less loss. Therefore, risk-averse investors are advised to use this stock portfolio selection model as a cover to face severe market declines.

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.

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.

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.

There has not been presented any comprehensive index for Corporate Governance in spite of its crucial role in resolution and responsiveness. Although diverse methods have been sought for measuring the Corporate Governance in the previous studies, in this study, a comprehensive index is proposed for measuring Corporate Governance taking other indexes into account through mathematical equations. For testing the designed model’s functionality, a set of data related to 149 firms from 2014 to 2019 has been used jointly, too. To do so, a questionnaire, which asks the experts their idea about the weight and significance of the criteria of measuring the Corporate Governance of the firm, is made and distributed and the weight and significance of the criteria were determined through one method of multi-criteria decision model (Shannon Entropy). To measure the changeable hidden relationship which exists in this study of the Corporate Governance, provided with the measurement items, a confirmatory factorial analysis was taken, too; results show that the supposed model has been a proper index for Corporate Governance. This mixed index includes criteria such as the board of directors' size, independence, meetings, and its education, the CEO's permanence, the size, independence, financial specialty, financial experience, tenure and the meetings of the auditors hip committee, ownership concentration, organizational ownership, governmental ownership, deals with the dependent persons, the quality of the disclosure, proper information and weaknesses if the internal controls,

Background: Many investment decisions are not only affected by economic indicators and rationality, but also categories such as work experience, social responsibility, risk, investor self-confidence, etc. affect investor’s behavior and their decisions. The main purpose of this study is to present a financial behavior model based on psychological and moral factors. Method: The present study was mixed. In the qualitative part, the Delphi technique was used. The statistical population of all experts and managers of brokerages, investment funds, investment portfolios, traders and analysts had professional qualifications and high experience, of which 12 people were considered as snowball technique as a sample. It was an interview tool and finally the data were analyzed by fuzzy Delphi method. In a small part, the descriptive-survey method was used. 287 members of the investor community were considered as a sample. A questionnaire was used to collect data. Finally, using AMOS software, confirmatory factor analysis and structural equation design were investigated. Results: Finally, the analysis model in this study was approved. Based on the importance of investors' behavior and the importance of moral and psychological characteristics in the capital market, it was shown that the expected return formed has a direct effect on the psychological characteristics of optimism, distrust, risk aversion and emotion, as well as ethics. It was also shown that psychological and moral characteristics affected turnover, trading volume, risk sharing, risk appetite. In addition, one of the most important results is a combination of behavioral factors (optimism, overconfidence, emotion, risk aversion and ethics) and market efficiency (trading volume, turnover, risk appetite and risk sharing). Conclusion: The obtained results indicate the effect of emotional and psychological and moral characteristics and characteristics on the behavior of investors and is important to drive the market towards rationality and on the other hand as a basis for designing investment strategies for investment managers.

This study was an attempt to evaluate the progress of capital market efficiency in Iran. Optimal resource allocation and micro and macro investments play a key role in the capital market. The capital market's main task is to circulate capital and allocate resources efficiently and optimally. The main task of this market is to flow capital and allocate resources efficiently and optimally. Is there a regular pattern for determining the stock price? Market efficiency gains significance as it is important to know what factor or factors are effective in determining the price of the stock in the stock market or whether there is a regular pattern for determining the price of a stock. Thus, this study examined the efficiency of the capital market in Iran. In this regard, the researchers used the daily data of the total index of the Tehran Stock Exchange for 2008-2017. Artificial neural network and time series training tests were used to perform the test. The test results showed weak efficiency in the Tehran Stock Exchange and this inefficiency did not change significantly compared to the first period. In other words, in the Tehran Stock Market, one can predict returns using artificial intelligence.

The purpose of this study is to make investment decisions with the approach of data envelopment analysis and making decision on selection of optimal stock portfolio employing meta heuristic algorithms for multi-objective functions subject to real-life constraints. The statistical population of this research in capital decision-making and selection of the optimal capital composition is 183 of the selected companies of Tehran Stock Exchange, which were finally 42 companies as justified investment options. After measuring the risk and return of efficient companies, the real limitations of the budget, requirements and expectations of the investor, determination and composition of the investment were formulated as a multi-objective model. For optimal decision, the modified genetic meta heuristic algorithm and MATLAB software with dual operators were used. Elimination of the risk minimization function in sensitivity analysis improved the level of decision return but also led to more risk. Eliminating the maximizing return function improved decision-making risk but also reduced investment return. Elimination of investment requirements and expectations improved returns and increased investment risk, but more companies became involved in optimal investment.

Enterprise risk management (ERM) represents a new paradigm that supports organizations in identifying, evaluating, and managing risks. Several factors encourage different organizations, especially banks, to design and apply ERM; among them, the possibility of financial problems and related costs, financial performance decline, growth opportunities, and independence of the board of directors can be pointed out. In addition, applying a suitable risk management strategy is a competitive advantage for supporting companies. Following the previous comprehensive studies, the present study was done by integrating a meta-composition approach and multivariate fuzzy network analysis. In this research, to identify effective factors on ERM based on empirical evidence and selection of studies, description and classification of selected articles, analysis of the content of selected research articles, and finally, the importance and refinement of the identified factors based on the Delphi expert opinion polling technique, and multivariate fuzzy network analysis was discussed. The study aimed to identify and rank the factors affecting the effectiveness of ERM of the firms accepted in selected banks in Iran. While reviewing studies, a semi-structured interview was conducted using an Exploratory-Descriptive Qualitative (EDQ) research design to determine the factors that affect ERM effectiveness. The interviewed experts were comprised of 20 university professors, CEOs, financial analysts of investment and brokerage companies, and senior auditors with accounting and management education. The literature review and the results of the interviews indicate five main themes that classify the factors affecting ERM effectiveness in firms. In this study, key factors were identified, and then the fuzzy Delphi technique was employed to rank and find the weight of the factors. The results showed that corporate governance, financial indicators, environmental indicators, company characteristics, and management indicators effectively enhance ERM's effectiveness. Accordingly, capital market analysts and investment companies should take a broader perspective and make decisions based on companies' financial risks instead of paying attention to companies' profitability and stock price changes. The results demonstrated that five factors determine ERM effectiveness: 1. corporate governance indicators (monitoring the board of directors and ownership structure), 2. Financial indicators (return on assets (ROA), earnings volatility, merger and acquisition (M&A) activities, financial deficiency, and capital opacity), 3. Environmental indicators (performance excellence, industry competition, audit firm credibility, environmental uncertainty, and industry), 4. Firm characteristics (financial leverage, firm size, and growth opportunities), and 5. Management indicators (management career and business diversity).