اسماعیل علی بیکی

اسماعیل علی بیکی

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فیلتر های جستجو: فیلتری انتخاب نشده است.
نمایش ۱ تا ۲ مورد از کل ۲ مورد.
۱.

Sustainable Reporting Function and Green Accounting Strategic Consequences (Cross-matrix analysis)(مقاله علمی وزارت علوم)

کلید واژه ها: Green Accounting Strategic Consequences Sustainable Reporting Function Interpretive Prioritization Ranking

حوزه های تخصصی:
تعداد بازدید : 814 تعداد دانلود : 164
The purpose of this study is to evaluate the most effective strategic implica-tions of green accounting based on the function of sustainable reporting. In this study, theoretical screening based on similar studies was used to identify the components (strategic consequence of green accounting) and research propositions (themes of sustainable reporting function). Then, in order to determine the reliability of research components and propositions through the participation of 12 experts and experts in the field of accounting and financial management, Delphi analysis was used. In the quantitative part, the identified components and propositions in the form of matrix questionnaires were evaluated by interpretive analysis by 17 managers of the top 50 companies in 2009. The results showed that the proposition of sustainable responsibility as the most influential theme of the sustainable reporting function causes the effectiveness of the value consequence in green accounting. This result shows that by developing the dimensions of social responsibility in sustainable reporting, the level of inclusive values in the value functions of green accounting is strengthened and builds trust and confidence in the company's performance.
۲.

Predicting Optimal Portfolio by Algorithm Analysis Systems(مقاله علمی وزارت علوم)

کلید واژه ها: Predicting Optimal Portfolio Growth and value stocks Optimization Algorithms

حوزه های تخصصی:
تعداد بازدید : 365 تعداد دانلود : 146
Choosing the proper investment mechanism is one of the main tasks of any investor that requires careful analysis and research on all available information. Since no investor exactly knows whether his or her expectations for a particular stock return will be met, they need to build their strategy in such a way as to eliminate as much damage as possible in the event of an adverse outcome. This study aims to predict the optimal portfolio using Algorithm Analysis Systems. In this regard, 98 firms listed on the Tehran Stock Exchange were examined in 2015-2019. Then, random portfolios were selected to test the research hypotheses by separating value stocks and growth stocks. For analysis, two algorithms of Support Vector Machines and an Adaptive Neuro-Fuzzy Inference System were used to select the most desirable portfolio. According to the support vector machine algorithm analysis, the results confirm the difference between the Sortino and Marquitz portfolios. To build their portfolios, decision-makers often rely on growth stocks which can boost their expected returns. Therefore, recognizing the analytical nature of portfolio formation in specialized areas can help improve investment analysis and pave the way for higher returns.

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