حسین جباری

This paper aims to investigate the portfolio optimization under various market risk conditions using copula dependence and extreme value approaches. According to the modern portfolio theory, diversifying investments in assets that are less correlated with one another allows investors to assume less risk. In many models, asset returns are assumed to follow a normal distribution. Consequently, the linear correlation coefficient explains the dependence between financial assets, and the Markowitz mean-variance optimization model is used to calculate efficient asset portfolios. In this regard, monthly data-driven information on the top 30 companies from 2011 to 2021 was the subject to consideration. In addition, extreme value theory was utilized to model the asset return distribution. Using Gumbel’s copula model, the dependence structure of returns has been analyzed. Distribution tails were modeled utilizing extreme value theory. If the weights of the investment portfolio are allocated according to Gumbel’s copula model, a risk of 2.8% should be considered to obtain a return of 3.2%, according to the obtained results.

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.

Corporate integrity is considered as part of the company's development strategies, which in the long run can lead to the increased firms' financial transparency to stakeholders. The purpose of our study is to present a corporate integrity model and, then to investigate its effect on firms' information asymmetry. In this study, to measure the corporate integrity, we use Meta-synthesis and Delphi analysis in the qualitative part. Then, in the quantitative section, the corporate integrity questionnaires were sent to the managers of the sample firms. Subsequently, a total of 138 questionnaires were completed and sent back, which were used as the final samples for analysis. In addition, information asymmetry is measured using the three different proxies, namely bid-ask spread, turnover, and Amihud illiquidity measure. Our findings show a significant and negative effect of corporate integrity on information asymmetry. This results suggest that corporate integrity, by promoting behavioral values based on truthfulness and commitment, the structures will enhance the corporate governance mechanisms and, thereby, firstly motivate the managers to reduce the agency gap and, secondly, implement a more effective level of the supervisions on the firm's performances in front of the stakeholders by accelerating the circulation of information and giving timely and reliable feedback to the stakeholders. This is the first study that presents a corporate integrity model through qualitative analysis and then, investigates the effect of corporate integrity on firms' information asymmetry. Therefore, our study can contribute to the extant literature of this context.