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Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.

Artificial neural networks (ANNs), which represent computational models simulating the biological neural systems, have become a dominant paradigm for solving complex analytical problems. ANN applications have been employed in various disciplines such as psychology, computer science, mathematics, engineering, medicine, manufacturing, and business studies. Academic research on ANNs is witnessing considerable publication activity, and there exists a need to track the intellectual structure of the existing research for a better comprehension of the domain. The current study uses a bibliometric approach to ANN business literature extracted from the Web of Science database. The study also performs a chronological review using science mapping and examines the evolution trajectory to determine research areas relevant to future research. The authors suggest that researchers focus on ANN deep learning models as the bibliometric results predict an expeditious growth of the research topic in the upcoming years. The findings reveal that business research on ANNs is flourishing and suggest further work on domains, such as back-propagation neural networks, support vector machines, and predictive modeling. By providing a systematic and dynamic understanding of ANN business research, the current study enhances the readers' understanding of existing reviews and complements the domain knowledge.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

This study applies VAR and ANN techniques to make ex-post forecast of U.S. oil price movements. The VAR-based forecast uses three endogenous variables: lagged oil price, lagged oil supply and lagged energy consumption. However, the VAR model suggests that the impacts of oil supply and energy consumption has limited impacts on oil price movement. The forecast of the genetic algorithm-based ANN model is made by using oil supply, energy consumption, and money supply (M1). Root mean squared error and mean absolute error have been used as the evaluation criteria. Our analysis suggests that the BPN-GA model noticeably outperforms the VAR model.

As part of an exploratory study on the nature of global leaders' power, we interviewed 23 global leaders to address the question: “How do the task, culture, and relationship complexities of global leadership shape the way global leaders exercise power and influence their followers?” We identify five complicating factors that shape the use of power by global leaders: Language, culture, time zones, physical distance, and matrix organizational structures. When compared with domestic leaders, these five factors make the use of power more complex for global leaders and require global leaders to invest substantially more time and energy into building relationships, sharing leadership, and prioritizing communication to ensure common understanding of vision and goals. We highlight a sixth factor, high-quality relationships, as an enabling resource for global leaders to succeed despite contexts of global leadership complexity. We provide a conceptual model summarizing how global leader influence attempts are complicated and enhanced and offer implications for future research and practice.

This study documents the role of relational trust in an afterschool organization and its influences on young people’s experiences.

Through a 10-month ethnographic study of one afterschool program that teaches teens how to make documentaries, I demonstrate that the confluence of blurred organizational goals; weak relational trust among staff; and funding pressures may have the unintended consequence of exploiting students for their work products and life stories.

The study finds that, while not all organizations function with student work at its center, many afterschool organizations are under increasing pressures to document student gains through tangible measures.

Implications from these findings reveal the need for developing strong relationships among staff members as well as establishing transparency in funding afterschool programs from within the organization and from foundations in order to provide quality programming for young people.

This study informs organizational theory, specifically in terms of measures of variation in relational trust within an organization and its influence on young people. This chapter includes student accounts of experiences with staff to enhance the significance of relational trust.

Much of the discussion surrounding the antivaccine movement focuses on the decision of parents to not vaccinate their children and the resulting danger posed to others. However, the primary risk is borne by the child left unvaccinated. Although living in a developed country with high vaccination rates provides a certain amount of protection through population immunity, the unvaccinated child is still exposed to a considerably greater risk of preventable diseases than one who is vaccinated. I explore the tension between parental choice and the child’s right to be free of preventable diseases. The chapter’s goal is twofold: to advocate for moving from a dyadic framework – considering the interests of the parents against those of the state – to a triadic one, in which the interests of the child are given as much weight as those of the parent and the state; and to discuss which protections are available, and how they can be improved. Specific legal tools available to protect that child are examined, including tort liability of the parents to the child, whether and to what degree criminal law has a role, under what circumstances parental choice should be overridden, and the role of school immunization requirements in protecting the individual child.