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Section 404 of the Sarbanes–Oxley Act (SOX) altered the relationship between auditors and their clients by requiring an external audit of companies’ internal controls. Regulatory guidance is interpreted and applied by external auditors to comply with SOX. The purpose of this paper is to apply service operations management theories and techniques to the internal control audit process to better understand the role regulatory guidance plays in audit services. We discuss service operations management theories that apply to the production of audit services and employ the operations management technique of simulation to examine the effects of a historical relationship between the client and the auditor, information sharing between the client and the auditor, and the auditor’s perceived risk of the client on the internal control audit process. The application of service operations management theories and the simulation results illustrate that risk and information sharing are key factors for the audit process. The results suggest the updated Public Company Accounting Oversight Board guidance from Auditing Standard 2 to Auditing Standard 5 appropriately increased audit effectiveness by encouraging risk-based judgments and information sharing. This paper merges accounting and service operations management research to examine the effects of regulatory guidance on the internal control audit process. The paper uses simulation to illustrate the importance of interpreting regulatory guidance and the specific effects of risk and information sharing on the internal control audit process.

In this chapter, I analyse the ethics of organisations assessing applicants based on group risk statistics; for example, parole boards consider information predicting recidivism risk, and employers want to minimise the risk of selecting lower-productivity employees. The organisational rejection of applicants from risky groups is explored as a form of discrimination to help identify the distinct ethical implications for applicant autonomy from the use of group risk statistics. Contra arguments from Schoeman (1987) and Schauer (2003), I argue that there is a substantive difference between assessing applicants directly through group statistics rather than including ‘individualised’ evidence. This difference impacts on the agency of applicants in the process. As organisations have reason to statistically assess applicants, some considerations for increasing applicant agency in the process are suggested. These include focusing on the nature of the factors used to assess applicants (static or dynamic), the transparency of the process to applicants, and the use of statistics specific to individuals.

Factor investment is booming in global asset management, especially environmental, social, and governance (ESG), dividend yield, and volatility factors. In this chapter, we use data from the US securities market from 2003 to 2019 to predict dividends and volatility factors through machine learning and historical data–based methods. After that, we utilize particle swarm optimization to construct the Markowitz portfolio with limits on the number of assets and weight restrictions. The empirical results show that that the prediction ability using XGBoost is superior to the historical factor investment method. Moreover, the investment performance of our portfolio with ESG, high-yield, and low-volatility factors outperforms baseline methods, especially the S&P 500 ETF.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

Healthcare-associated infections (HAIs) are a major cause of concern because of the high levels of associated morbidity, mortality, and cost. In addition, children and intensive care unit (ICU) patients are more vulnerable to these infections due to low levels of immunity. Various medical interventions and statistical process control techniques have been suggested to counter the spread of these infections and aid early detection of an infection outbreak. Methods such as hand hygiene help in the prevention of HAIs and are well-documented in the literature. This chapter demonstrates the utilization of a systems methodology to model and validate factors that contribute to the risk of HAIs in a pediatric ICU. It proposes an approach that has three unique aspects: it studies the problem of HAIs as a whole by focusing on several HAIs instead of a single type, it projects the effects of interventions onto the general patient population using the system-level model, and it studies both medical and behavioral interventions and compares their effectiveness. This methodology uses a systems modeling framework that includes simulation, risk analysis, and statistical techniques for studying interventions to reduce the transmission likelihood of HAIs.