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This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms.

The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model.

The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners.

This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies.

In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

Oil and gas sector has more disruptions regarding its logistics management than any other industry. It is critical to understand which external security threats disrupt the oil and gas supply chain (OGSC). Recently, the time interval between these disruptions became frequent. the purpose of this paper is to identify key logistics elements that lead to such disruptions which would greatly benefit the oil and gas industry in developing more effective mitigation measures and resilient practices in the future.

This research develops the theoretical framework through a critical review of all theories related to resilience, logistics disruptions and mitigation methods in the oil and gas industry. Afterward, semi-structured interviews were conducted with executives in the Egyptian oil and gas industry to develop a conceptual framework. Finally, an empirical study was conducted through questionnaires with managers in the Egyptian oil and gas sector to develop the applied framework.

This research revealed that achieving an elevated level of flexibility, redundancy, visibility and collaboration in the Egyptian OGSC will significantly increase the level of resilience in the sector and consequently help in mitigating probable logistics disruptions.

This research contributes to academia by providing a conceptual framework for the most common logistics disruptions in the Egyptian OGSC and providing practitioners with the best resilience practices that are feasible and effective in mitigating logistics disruptions.

Previous research studied disruptions in OGSC from different perspectives: economic, social, political, technical, safety, legal and environmental perspectives, but no research highlighted the logistics perspective in the Egyptian context, to the best of the authors’ knowledge.

This research assesses the present condition of Circular Economy (CE) practices in green hotels situated in two leading Middle Eastern nations, Saudi Arabia and Egypt. Furthermore, it investigates the crucial impact of Industry 4.0 innovations in facilitating the relationship between the adoption of CE initiatives and hotel performance (HP).

Data from 59 green hotels in Saudi Arabia and 76 in Egypt were collected online, resulting in 400 complete forms, providing a diverse sample size suitable for PLS-SEM analysis using JASP software as a leading machine learning application.

The results demonstrate a notable correlation between CE strategies, including redesign, production, reuse and recovery in hotels and HP. Additionally, the impact of Industry 4.0 performance on the relationship between CE and HP is facilitated by four distinct CE loops.

This study offers practical insights into implementing CE practices in the hotel industry, aiding policymakers and establishments in enhancing sustainability and performance. Key stakeholders can use these findings to develop strategies that integrate CE initiatives, improve operational outcomes and align marketing efforts with sustainable practices.

This study is groundbreaking in its exploration of the relationship between CE and the transition to Industry 4.0 in the MENA region, analyzing the specific CE practices that contribute to high HP. The tested model expands on the Natural Resource-Based View and stakeholder theory by incorporating the critical role of Industry 4.0 innovations in the CE–HP nexus.

This paper aims to tackle an important question related to women’s economic empowerment in highly patriarchal societies like Egypt. The paper discusses individual, household, wealth and location factors determining women empowerment, as measured by two dimensions: decision-making power and mobility.

Using the “Egypt Labor Market Panel Survey” (ELMPS) 2012, a Multiple Indicators Multiple Causes (MIMIC) model was estimated to study the main economic resources and social constraints that determine women empowerment as measured by the power of women over household decisions and her freedom of movement in Egypt.

Three key messages could be delivered. First, women’s own economic resources as captured by her employment status are an important source of her empowerment. Second, contrary to theoretical prediction education is not playing its expected role in developing awareness and transforming ideas concerning gender roles in Egypt. Third, the importance of social local context is fundamental for Egyptian women empowerment.

This study is an attempt to address some of the gaps in the literature for the Egyptian case, where there is a lack in rigorous studies measuring women empowerment and examining its determinates. This is done by first, tackling multiple dimension of women’s empowerment, decision-making inside households and freedom of mobility. Second, using MIMIC model, which is a modeling approach that allows for studying the relations between several causes of a given latent variable, such as “Empowerment” in our case, and a number of its possible indicators, without a directly observable measure of the latent variable. Third, using the most recent set of data; the ELMPS 2012 which has a special focus on women’s resources and agency that permits greater content validity of the multidimensional setup. Forth, the macro level differences in women’s status are tackled through using location dummy variables. Finally, given the important correlation between wealth level and women empowerment, the paper is considered a first attempt to analyze such impact by including a variable that captures the wealth level of the woman’s household as one determinant of empowerment.

India is a developing economy along with an increasing population estimated to be the largest populated country in about seven years. Simultaneously, its power consumption is projected to increase more than double by 2020. Currently, the dependence on coal is relatively high, making it the largest global greenhouse gas emitting sector which is a matter of great concern. The purpose of this paper is to evaluate the environmental impacts of the natural gas electricity generation in India and propose a model using a life cycle assessment (LCA) approach.

LCA is used as a tool to evaluate the environmental impact of the natural gas combined cycle (NGCC) power plant, as it adopts a holistic approach towards the whole process. The LCA methodology used in this study follows the ISO 14040 and 14044 standards (ISO 14040: 2009; ISO 14044: 2009). A questionnaire was designed for data collection and validated by expert review primary data for the annual environmental emission was collected by personally visiting the power plant. The study follows a cradle to gate assessment using the CML (2001) methodology.

The analysis reveals that the main impacts were during the process of combustion. The Global warming potential is approximately 0.50 kg CO₂ equivalents per kWh of electricity generation from this gas-based power plant. These results can be used by stakeholders, experts and members who are authorised to probe positive initiative for the reduction of environmental impacts from the power generation sector.

Considering the pace of growth of economic development of India, it is the need of the hour to emphasise on the patterns of sustainable energy generation which is an important subject to be addressed considering India’s ratification to the Paris Climate Change Agreement. This paper analyzes the environmental impacts of gas-based electricity generation.

Presenting this case study is an opportunity to get a glimpse of the challenges associated with gas-based electricity generation in India. It gives a direction and helps us to better understand the right spot which require efforts for the improvement of sustainable energy generation processes, by taking appropriate measures for emission reduction. This paper also proposes a model for gas-based electricity generation in India. It has been developed following an LCA approach. As far as we aware, this is the first study which proposes an LCA model for gas-based electricity generation in India. The model is developed in line with the LCA methodology and focusses on the impact categories specific for gas-based electricity generation.