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This paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored.

The agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step, traders decide on which market to participate in and which trading strategy to follow. Traders can follow technical trading strategy, fundamental trading strategy or abstain from trading. The time-varying weight of each trading strategy depends on the current and past performance of this strategy. However, technical traders are loss-averse, where losses are perceived twice the equivalent gains. Market makers settle asset prices according to the net submitted orders.

The proposed framework can replicate important stylized facts observed empirically such as bubbles and crashes, excess volatility, clustered volatility, power-law tails, persistent autocorrelation in absolute returns and fractal structure.

Artificial models linking micro to macro behavior facilitate exploring the effect of different fiscal and monetary policies. The results of imposing Tobin taxes indicate that a small levy may raise government revenues without causing market distortion or instability.

This paper proposes a novel approach to explore the effect of loss aversion on the decision-making process in interacting financial markets framework.

Asset pricing dynamics in a multi-asset framework when investors’ trading exhibits the disposition effect is studied. The purpose of this paper is to explore asset pricing dynamics and the switching behavior among multiple assets.

The dynamics of complex financial markets can be best explored by following agent-based modeling approach. The artificial financial market is populated with traders following two heterogeneous trading strategies: the technical and the fundamental trading rules. By simulation, the switching behavior among multiple assets is investigated.

The proposed framework can explain important stylized facts in financial time series, such as random walk price dynamics, bubbles and crashes, fat-tailed return distributions, absence of autocorrelation in raw returns, persistent long memory of volatility, excess volatility, volatility clustering and power-law tails. In addition, asset returns possess fractal structure and self-similarity features; though the switching behavior is only allowed among the asset markets.

The model demonstrates stylized facts of most real financial markets. Thereafter, the proposed model can serve as a testbed for policy makers, scholars and investors.

To the best of knowledge, no research has been conducted to introduce the disposition effect to a multi-asset agent-based model.

Since the beginning of 2020, economies faced many changes as a result of coronavirus disease 2019 (COVID-19) pandemic. The effect of COVID-19 on the Egyptian Exchange (EGX) is investigated in this research.

To explore the impact of COVID-19, three periods were considered: (1) 17 months before the spread of COVID-19 and the start of the lockdown, (2) 17 months after the spread of COVID-19 and the during the lockdown and (3) 34 months comprehending the whole period (before and during COVID-19). Due to the large number of variables that could be considered, dimensionality reduction method, such as the principal component analysis (PCA) is followed. This method helps in determining the most individual stocks contributing to the main EGX index (EGX 30). The PCA, also, addresses the multicollinearity between the variables under investigation. Additionally, a principal component regression (PCR) model is developed to predict the future behavior of the EGX 30.

The results demonstrate that the first three principal components (PCs) could be considered to explain 89%, 85%, and 88% of data variability at (1) before COVID-19, (2) during COVID-19 and (3) the whole period, respectively. Furthermore, sectors of food and beverage, basic resources and real estate have not been affected by the COVID-19. The resulted Principal Component Regression (PCR) model performs very well. This could be concluded by comparing the observed values of EGX 30 with the predicted ones (R-squared estimated as 0.99).

To the best of our knowledge, no research has been conducted to investigate the effect of the COVID-19 on the EGX following an unsupervised machine learning method.

This research is built up upon exploring the concepts of building information modeling (BIM) adoption and strategy formulation with the aim to develop a strategy for implementing BIM in the Egyptian construction industry.

The development of the BIM implementation strategy was based on two pillars, namely the literature review and results of the survey questionnaire and interviews. First, the review of literature helped investigating the BIM challenges and international strategies developed to implement BIM worldwide.

The research presented recommendations to assist policymakers in Egypt to facilitate BIM implementation.

Although multiple frameworks have been proposed to aid in BIM implementation, a practical strategy to implement BIM in Egypt is still lacking. Moreover, current market scale studies neglect nonsoftware aspects of BIM adoption, do not identify market gaps or reflect market-specific criteria. As such, it cannot be used by policymakers to facilitate BIM diffusion.

Despite the active role of continuous learning on improving organizational performance, the construction industry generally and architectural design firms (ADFs) in particular are criticized for their inability to properly use learning to improve their performance. This paper aims to develop a business improvement framework based on post occupancy evaluation (POE) as a learning tool for improving the performance of ADFs.

To achieve the above-mentioned aim, a twofold research strategy, namely, theoretical and practical, is used to achieve four objectives. The theoretical approach is used to conduct thorough literature review to investigate three main topics: building performance, organizational performance and learning organization. The practical approach is used to present and synthesize two relevant field studies to examine the role of POE toward improving the performance of ADFs and evaluate the perception and application of ADFs in Egypt toward improving their performance through POE. Based on the results gleaned from the objectives, the research developed a framework to facilitate the adoption and application of POE as a learning tool for improving the performance of ADFs. Finally, research conclusions and recommendations useful to ADFs and future research are outlined.

The construction industry is a fragmented business that is characterized by low performance compared to other industries. This is because the separation between design, construction and end-users results in missing the opportunity to provide designers with learned lessons, feedback and suggestions for design improvement that ultimately obstructs the performance of ADFs. POE is an effective tool adopted to measure building performance and provide learning environment to improve the performance of new projects and ADFs. The research realized that there is a need to fill the gap in construction literature concerning improving ADFs’ performance through POE and to develop a framework to facilitate the adoption and application of POE as a learning tool for improving the performance of ADFs.

The research focused on improving the performance of ADFs only.

The framework developed by this research establishes organized procedures to enable AFDs to implement POE and use its benefits toward improving their performance.

This paper presents a business improvement framework integrating POE as learning tool for improving the performance of ADFs. This ideology has received scant attention in construction literature. The developed framework represents a synthesis that is novel and creative in thought and adds value to the knowledge in a manner that has not previously occurred.

This paper aims to evaluate the technical efficiency of the health-care systems in 21 selected middle-income countries during the period (2000–2017) and determine the source of inefficiency whether it is transient (short run) or persistent (long run).

The study uses the stochastic frontier analysis technique through employing the generalized true random effects model which overcomes the drawbacks of the previously introduced stochastic frontier models and allows for the separation between unobserved heterogeneity, persistent inefficiency and transient inefficiency.

Persistent efficiency is lower than the transient efficiency; hence, there are more efficiency gains that can be made by the selected countries by adopting long-term policies that aim at reforming the structure of the health-care system in the less efficient countries such as South Africa and Russia. The most efficient countries are Vietnam, Mexico and China which adopted a social health insurance that covers almost the whole population with the aim of increasing access to health-care services. Also, decentralization in health-care has assisted in adopting health-care policies that are suitable for both the rural and urban areas based on their specific conditions and health-care needs. A key success in the implementation of the adopted long-term policies by those countries is the continuous monitoring and evaluation of their outcomes and comparing them with the predefined targets and conducting any necessary modifications to ensure their movement in the right path to achieve their goals.

Although several studies have evaluated the technical efficiency both across and within countries using non-parametric (data envelopment analysis) and parametric (stochastic frontier analysis) approaches, to the best of the authors’ knowledge, this is the first attempt to evaluate the technical efficiency of selected middle-income countries during the period (2000–2017) using the generalized true random effects stochastic frontier analysis model.