Predicting Volatility Index According to Technical Index and Economic Indicators on the Basis of Deep Learning Algorithm

The Volatility Index (VIX) is a real-time index that has been used as the first measure to quantify market expectations for volatility, which affects the financial market as a main actor of the overall economy that is sensitive to the environmental and social aspects of investors and companies. The VIX is calculated using option prices for the S&P 500 Index (SPX) and is expressed as a percentage. Taking into account that VIX only shows the implicit volatility of the S&P 500 for the next 30 days, the authors develop a model for a near-optimal state trying to avoid uncertainty and insufficient accuracy. The researchers are trying to make a contribution to the theory of socially responsible portfolio management. The developed approach allows potential investments to make decisions regarding such important topics as ethical investing, performance analysis, as well as sustainable investment strategies. The approach of this research allows to use deep probabilistic convolutional neural networks based on conditional variance as a linear function of errors with the aim of estimating and predicting the VIX. For this purpose, the use of technical indicators and economic indexes such as Chicago Board Options Exchange (CBOE) VIX and S&P 500 is considered. The results of estimating and predicting the VIX with the proposed method indicate high precision and create a certainty in modeling to achieve the goals

Evaluation of Strategies to Improve the Corporate Social Responsibility Performance in Food and Pharmaceutical Industries: Empirical Evidence from Iran

Since there is a lack of a strong CSR platform in the Iranian corporate environment, especially in the food and pharmaceutical industries, the proposed and existing strategies can be utilized to empower employees and the general public, improve executive processes, improve quality and customer satisfaction, and finally, organize financial and accounting reports. Hence, this study seeks to determine and evaluate strategies to improve the concepts and domain of corporate social responsibility (CSR) of food and pharmaceutical industries in Iran based on the requirements of sustainable development theory. This study analyzes the data of 22 companies active in the food and pharmaceutical industries listed on the Tehran Stock Exchange (TSE) over a period of six years (2014–2019). Eighty-three indicators were used to evaluate the performance of social responsibility based on economic, social and environmental dimensions. The Pareto approach has been used to determine the most important unmet current requirements. Then, using the Delphi method and content validity, appropriate strategies were determined. Finally, a correlation matrix was used to determine the most important and comprehensive strategies. Findings showed that 14 requirements in the field of corporate social responsibility are of great importance and with six strategies, the necessary measures to meet those requirements can be covered

Evaluation of Strategies to Improve the Corporate Social Responsibility Performance in Food and Pharmaceutical Industries: Empirical Evidence from Iran

Since there is a lack of a strong CSR platform in the Iranian corporate environment, especially in the food and pharmaceutical industries, the proposed and existing strategies can be utilized to empower employees and the general public, improve executive processes, improve quality and customer satisfaction, and finally, organize financial and accounting reports. Hence, this study seeks to determine and evaluate strategies to improve the concepts and domain of corporate social responsibility (CSR) of food and pharmaceutical industries in Iran based on the requirements of sustainable development theory. This study analyzes the data of 22 companies active in the food and pharmaceutical industries listed on the Tehran Stock Exchange (TSE) over a period of six years (2014–2019). Eighty-three indicators were used to evaluate the performance of social responsibility based on economic, social and environmental dimensions. The Pareto approach has been used to determine the most important unmet current requirements. Then, using the Delphi method and content validity, appropriate strategies were determined. Finally, a correlation matrix was used to determine the most important and comprehensive strategies. Findings showed that 14 requirements in the field of corporate social responsibility are of great importance and with six strategies, the necessary measures to meet those requirements can be covered

Predicting Volatility Index According to Technical Index and Economic Indicators on the Basis of Deep Learning Algorithm

The Volatility Index (VIX) is a real-time index that has been used as the first measure to quantify market expectations for volatility, which affects the financial market as a main actor of the overall economy that is sensitive to the environmental and social aspects of investors and companies. The VIX is calculated using option prices for the S&P 500 Index (SPX) and is expressed as a percentage. Taking into account that VIX only shows the implicit volatility of the S&P 500 for the next 30 days, the authors develop a model for a near-optimal state trying to avoid uncertainty and insufficient accuracy. The researchers are trying to make a contribution to the theory of socially responsible portfolio management. The developed approach allows potential investments to make decisions regarding such important topics as ethical investing, performance analysis, as well as sustainable investment strategies. The approach of this research allows to use deep probabilistic convolutional neural networks based on conditional variance as a linear function of errors with the aim of estimating and predicting the VIX. For this purpose, the use of technical indicators and economic indexes such as Chicago Board Options Exchange (CBOE) VIX and S&P 500 is considered. The results of estimating and predicting the VIX with the proposed method indicate high precision and create a certainty in modeling to achieve the goals

An Employee Competency Framework in a Welfare Organization

Purpose—The aims of the current study were as follows: (1) to develop a framework of the job competence of employees to examine the recruitment of employees in their specialized fields, and (2) to answer the question of whether the development of that framework of the job competence of employees in attracting and employing people or their appointment and promotion was effective in achieving the goals of the organization. Design/methodology—Researchers examined the work environment of a branch of the Welfare Organization of Iran and the most important duties and responsibilities of the employees of the specialized spheres of the organization. Afterward, researchers listed the essential knowledge, skills, and attitudes necessary for the employees. Then, the authors asked 70 experts working in this organization to express their views on the importance of each of these items and the status of each of the proposed items in terms of their relevance, clarity, simplicity, and ambiguity. Accordingly, the competencies were categorized and titled. The identified competencies were classified into four main groups: needs assessment, planning, implementation, and evaluation. The authors used the pairwise comparison method and the hierarchical analysis process approach to weighting. Findings—The authors set and presented critical competencies related to each specialized field of healthcare, social, educational, and cultural affairs in separate frameworks. The research results allowed theoretical and practical approaches to modeling to consider transversal and disciplinary competencies for a social, economic, or environmental commitment based on the sustainability concept. Value—Developing the employee competency frameworks of the health service is essential for sustainable development. The validated employee competency frameworks in the Welfare Organization can be applied in other provinces of Iran and other sectors of the healthcare system

Exploring UTAUT Model in Mobile 4.5G Service: Moderating Social–Economic Effects of Gender and Awareness

The current study aims to examine how students’ intentions to use 4.5G mobile phones are affected by the social-economic factors of performance expectancy, cost, effort expectancy, and social influence. This study is based on the perspectives of the unified theory of acceptance and use of technology (UTAUT). The central assumption of this study is that when students use 4.5G mobile services to source information at university, their academic performance is likely to improve. From eight private and public universities in Malaysia, 2117 students were enrolled in this study. We investigated the effects of gender and awareness as moderators on the relationships among the variables of interest. The findings showed that social influence and performance expectancy positively affected university students’ intentions to use 4.5G mobile phones. The researchers conducted a multigroup analysis to confirm the moderating effect of gender among the underlying relationships in the model. Structural equation modeling analysis indicated that, unlike awareness, gender did not moderate social influence, effort expectancy, performance expectancy, or the cost of students’ intentions to use 4.5G mobile phones. The implications of the proposed approach, considering the digital transformation concept, could be a topic for future research