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This chapter develops a return forecasting methodology that allows for instability in the relationship between stock returns and predictor variables, model uncertainty, and parameter estimation uncertainty. The predictive regression specification that is put forward allows for occasional structural breaks of random magnitude in the regression parameters, uncertainty about the inclusion of forecasting variables, and uncertainty about parameter values by employing Bayesian model averaging. The implications of these three sources of uncertainty and their relative importance are investigated from an active investment management perspective. It is found that the economic value of incorporating all three sources of uncertainty is considerable. A typical investor would be willing to pay up to several hundreds of basis points annually to switch from a passive buy-and-hold strategy to an active strategy based on a return forecasting model that allows for model and parameter uncertainty as well as structural breaks in the regression parameters.

This is the third in a series of papers aimed at providing models effective in predicting the degree of pain and discomfort in canines. The first two papers provided benchmarking and examination of dogs suffering from osteoarthritis (OA). In this chapter, we extend the study to include dogs suffering from OA, sarcoma, and oral mucositis (a side effect of chemotherapy and radiation treatments). The R programming language and SAS JMP are used to clean data, generate ANOVA, LSR regression, decision tree, and nominal logistic regression models to predict changes in activity levels associated with the progression of arthritis. The predictive models provide a diagnostic basis for determining the degree of disease in a dog (based on demographics and activity levels) and provide forecasts that assist in establishing appropriate medication dosages for suffering dogs.

This chapter examines the dynamic linkages between the returns of Bitcoin, gold, and oil by using daily closing price data between July 17, 2010 and January 8, 2021. This study applies the diagonal BEKK–GARCH model for the purpose of analyzing a volatility spillover of variables in positive or negative ways. The empirical results show that the lagged returns inversely affect their current returns in oil. Based on the return spillovers between Bitcoin and gold, the empirical results indicate a unidirectional return spillover from Bitcoin to gold. Moreover, the authors found a unidirectional return transmission is observed from oil to Bitcoin, implying that oil returns are useful in forecasting Bitcoin returns. These findings are not only valuable for understanding of the interrelationships between the returns of Bitcoin, gold, and oil, but they are also of great interest to portfolio managers, investors, and investment funds that are actively dealing in Bitcoin, gold, and oil.

This chapter studies the effects of the COVID-19 pandemic on the economic structure of the US and EU economies by measuring its impact on some reference macro-economic variables. We use a factor model approach on a set of variables available at different frequencies (daily, weekly, monthly, and quarterly) and provide evidence of instability in the primary factors driving the economy. A sequential analysis of the factors allows us to evaluate the model's forecasting performance and extract some instability measures based on the factor model's eigenvalues. Finally, we show how to use COVID-related variables, such as policy, economic, and health indicators, to compute conditional forecasts with factor models, and perform a scenario analysis on the variables of interest to understand economic instability.

The expected growth of elders and chronically ill patients requires health insurers and health-care systems to shift from primarily focusing on the care of the sick to focusing both on care of the sick and on preventive health care. Public expectations for high-quality health care call for a new, more profound, and more actionable understanding of the healthy customer’s mind regarding health-promoting behaviors. Mind-Genomics may become the next big idea in health service. The relation between Mind-Genomics and data-driven personalized health plans is now being systematically explored. Some of the findings are reported in this research project.

Methodology: Respondents were 200 members of the Excellus American health fund. Based on measures of patient experience the authors created and tested concepts of messaging. The authors conducted a series of conjoint-based designed experiments to establish response patterns to our messaging. The authors analyzed data using Mind-Genomics, an empirical “micro-science” for discovering psychographic mind-sets and mapping motivating messages for healthy behaviors.

Results: The authors segmented patients by attitudes, perceptions, preferences, needs, and behaviors by their responses to messaging, thereby uncovering underlying psychographic mind-sets. The authors used the Personal Viewpoint Identifier to tag each person in the population by a sample mind-set and use the right messaging.

Discussion: To understand the mind of the patient regarding health plans, health funds may use the powerful tools of Mind-Genomics. Health insurers and health systems may implement Mind-Genomics as the next frontier of knowledge development to offer customized health plans, thus investing in preventive medicine.

The higher education world around us is changing fast. Mobile learning (M-learning) and apps in education are new and exciting. However, this does not mean that the faculty is less relevant. On the contrary, the faculty are an ever-important factor that is needed to increase student engagement. Disengaged students are in fact more problematic than low achieving students. This is because of the disruption disengaged student bring in the classroom. Therefore, increasing student engagement is a key challenge in higher education today. Research suggests that active learning, value and expectations are important factors that determine student engagement. Some of these factors can be indirectly influenced by the faculty and course designers. Against this background, the objective of this chapter is to examine if dwindling student engagement in higher education can be addressed with CURPA, i.e. a CURriculum and course planning (CURP) App. CURPA is the result of our efforts at Utrecht University to help design courses and curricula by using cards to account for different activities, with the strategic aim being to stimulate student engagement in higher education. Our expert interviews resulted in a MoSCoW-prioritised list of requirements that can be implemented over time, structured in line with Becker's student engagement model.

This research follows Emerson and Conroy (2004) by examining student attitudes regarding a number of ethical vignettes, and extends their research to incorporate an investigation of the effects of different ethical prompts (including the American Institute of Certified Public Accountants (AICPA) Code of Professional Conduct) on responses. We explore relationships within the responses and identify significant patterns in attitudes by gender, major, and experimental treatment. While female subjects consistently view unethical behavior less favorably, our results suggest that status as an accounting major also may lead to less acceptance of the unethical behavior portrayed in the vignettes. For example, male and female accounting students were less accepting of padding expense accounts and insider stock trading, indicating that ethical training in accounting classes may be delivering some desirable results.