The data-generating process of productivity growth includes both trend and business-cycle shocks, generating many counterfactuals for prices under full-information. In practice, agents cannot immediately distinguish between the two shocks, leading to "rational confusion": each shock inherits properties of its counterpart.
We axiomatize subjective probabilities on finite domains without requiring richness in the outcome space or restrictions on risk preference through event exchangeability, defined in Chew and Sagi (2006), which was implicit in the prior literature (Savage, 1954; Machina and Schmeidler, 1992; Grant, 1995). We characterize the unique subjective probability representing the underlying exchangeability relation.
This paper provides basic facts on worker flows between former Public Company Accounting Oversight Board (PCAOB) employees and large audit firms. Using a large sample of publicly available curricula vitae, we document that an increasing number of former PCAOB employees join U.S. audit firms in senior-level positions during recent years. We also find that the number of PCAOB employees hired by these firms is positively related to the number of deficiencies reported in their prior PCAOB inspection report, and that the number of deficiencies reported in firms’ future inspection reports is negatively associated with the number of former PCAOB employees hired.
This paper examines corporations’ actions, and statements about actions, following the tax law change known as the Tax Cuts and Jobs Act (TCJA). Specifically, we examine four different outcomes—bonuses (or other actions that benefit workers), announcements of new investments, share repurchases, and dividend announcements.
We argue that behavioral strategy can learn a great deal from the Theory of Computational Complexity and Artificial Intelligence. Also, a concept of “organizational intractability” may be useful in determining what analytical decision technologies are actually intractable in real organizations with constraints on time and managerial attention.
It is generally accepted that operating with a combined (i.e., pooled) queue rather than separate (i.e., dedicated) queues is beneficial mainly because pooling queues reduces long-run average throughput time. In fact, this is a well-established result in the literature, e.g., when servers and jobs are identical. We consider an observable multi-server queueing system which can be operated with either dedicated queues or a pooled one.
My particular path has contained, as most paths do, twists and turns. As I look back, they all seem somehow related to each other, but they were not all planned. Design/methodology/approach I will discuss my life and career in chronological order, then reflect on my career and research philosophy. I will also discuss several of my most cited articles and how they emerged. Findings I emphasize research that is both academically rigorous and relevant to business. I also show that passion for a subject, even one that is risky and not encouraged by others, has resulted in lifelong interest and inspiration for me.
We examine the role of political affiliation during the selection of Opportunity Zones, a place-based tax incentive enacted by the Tax Cuts and Jobs Act of 2017. We find governors are on average 7.6% more likely to select a census tract as an Opportunity Zone when the tract’s state representative is a member of the governor’s political party. This effect is incremental to local demographic factors that increased the likelihood of selection, such as lower income levels and preceding improvements in local conditions.
Public calls for a national paid sick leave policy continue to grow in the United States. In the absence of a federal policy, many localities and states enacted their own paid sick leave mandates. We document an average increase of 1.9% in employment following the implementation of a paid sick leave policy.
Financial intermediaries often provide guarantees resembling out-of-the-money put options, exposing them to undiversifiable tail risk. We present a model in the context of the U.S. life insurance industry in which the regulatory framework incentivizes value-maximizing insurers to hedge variable annuity (VA) guarantees, though imperfectly, and shift risks into high-risk and illiquid bonds. We calibrate the model to insurer-level data and identify the VA-induced changes in insurers' risk exposures.
Why do fashion brands maintain classics for years or even decades amidst the ever-changing fashion trends? Why do some fashion brands position classic products as premium items, while others treat classics as entry-level offerings? In this research, our aim is to explain the emergence of fashion classics and various classics strategies by considering the possibility of cross-generation signaling in an overlapping generations model.
The slope carry takes a long (short) position in the long-term bonds of countries with steeper (flatter) yield curves. The traditional carry takes a long (short) position in countries with high (low) short-term rates. We document that: (i) the slope carry return is slightly negative (strongly positive) in the pre (post) 2008 period, whereas it is concealed over longer samples; (ii) the traditional carry return is lower post-2008; and (iii) expected global growth and inflation declined post-2008.
Kenan Scholar Laura Gerlach (BSBA '20) reflects on her experience with the Kenan Scholars Board of Mentors.
On April 25, the Kenan Institute presented UNC students Alex Cooper and Phillippa Owens with the institute’s two highest honors. Cooper received the Rollie Tillman Jr. Outstanding Leadership Award, and Owens was recognized with the Kenan Institute Impact Award. Both awards honor students have made a significant impact on the Kenan Institute and its initiatives and exhibited leadership at UNC and in the broader community.
Reliably detecting insider trading is a major impediment to both research and regulatory practice. Using account-level transaction data, we propose a novel approach. Specifically, after extracting several key empirical features of typical insider trading cases from existing regulatory actions, we then employ a machine learning methodology to identify suspicious insiders across our full sample.
AI applications are ubiquitous – and so is their potential to exhibit unintended bias. Algorithmic and automation biases and algorithm aversion all plague the human-AI partnership, eroding trust between people and machines that learn. But can bias be eradicated from AI? Dr, Fay Cobb Payton, Professor of Information Systems & Technology at NC State’s Poole College of Management and a Program Director at the National Science Foundation in the Division of Computer and Network Systems moderates a discussion between Timnit Gebru, research scientist and the co-lead of the Ethical AI Team at Google and the co-founder of Black in AI; Brenda Leong, senior counsel and director of artificial intelligence and ethics at the Future of Privacy Forum; Professor Mohammad Jarrahi, associate professor at UNC’s School of Information and Library Science; and Chris Wicher, Rethinc. Labs AI Research Fellow, former director of AI Research at KPMG’s AI Center of Excellence and Vice President of Watson Engineering at IBM.