Search Results for Raghad M. Najim

Abstract

Homes bias stock is considered a confusing phenomenon in the financial literature, due to its increase over time, as well as the absence of any justification for this increase. This study aims to analyze the determinants of local bias, especially during the study period, which went through many economic, financial and health crises. The intellectual argument from the study lies in the basic question whether the country's bias towards stocks is affected during the Karuna pandemic, as well as an attempt to know the determinants that affect the country's bias during the study period. A portfolio analysis of economies was conducted for the period (2005 to 2020).

In light of the data obtained for the study sample, represented by portfolios of foreign and local stocks and the market values of those portfolios, as well as several other determinants. The study sample consisted of (64) foreign portfolios. The duration of the study included (16) years for the sample studied as a whole, and it started in the year (2005) until the year (2020). Using many financial and statistical methods, the study reached many conclusions and recommendations, the most important of which are: According to the theory of portfolio selection and the country’s bias in light of uncertainty corona pandemic, In addition, the study proved that there are several factors, not a single factor, to explain the determinants of the country’s bias, among which are institutional and economic factors that bear the responsibility of reducing foreign investment returns, which are led by the cost-benefit input such as reserves, trade flows, gross domestic product, the number of listed companies and market capitalization. The other explanation is related to behavioral factors that focus on investor behavior, such as familiarity, conservatism, and overconfidence. This proof confirms the acceptance of the main hypothesis. The study concluded in the most important recommendation: the need for investors to be interested in diversifying their investments in order to reduce the country's bias towards local stocks. And to take advantage of external opportunities to achieve the best exchange between efficient returns and risks.

Abstract

This study aims to examine the impact of both green accounting and artificial intelligence on environmental sustainability by analyzing the role of modern accounting practices and intelligent technologies in enhancing the environmental performance of institutions. The research was based on the descriptive analytical approach, where data was collected from a sample of employees at Warith Al-Anbiya University using a questionnaire consisting of (15 items), which included three main axes; (5 items) were allocated to artificial intelligence indicators, (5 items) to green accounting systems, and (5 items) to environmental sustainability. The data were analyzed using the statistical program (SPSS). The research adopts a descriptive-analytical approach, where data were collected from a sample of academic and economic institutions and analyzed using appropriate statistical methods to test the study hypotheses. The results reveal a significant positive impact of green accounting on environmental sustainability, highlighting the importance of adopting environmentally oriented accounting practices to improve environmental performance. Furthermore, the findings indicate a strong influence of artificial intelligence on environmental sustainability, as AI technologies contribute to enhancing resource efficiency, reducing pollution, and supporting sound environmental decision-making. The study concludes that integrating green accounting with artificial intelligence is essential for achieving environmental sustainability goals. It emphasizes the need to develop accounting systems and adopt modern technologies within institutions. The study also recommends increasing awareness of these practices and providing institutional and regulatory support to ensure their effective implementation. Keywords: Green Accounting, Artificial Intelligence, Environmental Sustainability, Environmental Performance, Sustainable Development.