Accounting rules, through their interactions with capital regulations, affect financial institutions’ trading behavior. The insurance industry provides a laboratory to explore these interactions: life insurers have greater flexibility than property and casualty insurers to hold speculative-grade assets at historical cost, and the degree to which life insurers recognize market values differs across U.S. states. During the financial crisis, insurers facing a lesser degree of market value recognition are less likely to sell downgraded asset-backed securities. To improve their capital positions, these insurers disproportionately resort to gains trading, selectively selling otherwise unrelated bonds with high unrealized gains, transmitting shocks across markets.
Using a convolutional neural networks approach to process the images, this study reviews Airbnb listings in two cities and derives a descriptive model of image technical features, content, and other property attributes (e.g., price, textual information, characteristics) to predict demand at the property level.
We consider the allocation of inventory to stores in a “merchandise test,” whereby a fashion retailer deploys a new product to stores in limited quantities in order to learn about demand prior to the main selling season. Our problem formulation includes practical considerations like fixed costs and multiperiod inventory considerations but is challenging to analyze directly. Instead, we take a bounding approach that isolates the novel aspect of our problem: the impact of test inventory allocation on demand learning.
We model the threat of such liquidation through the intermediation of an activist shareholder. Among other things, our model predicts that MDPs are more likely to be adopted by funds that appear to be less effective in providing portfolio services to their investors and that are relatively easy to liquidate or ‘attack’. We test the model on a panel of 236 CEFs and find good agreement with our model.
We analyze why firms use non-intermediated short-term debt by studying the commercial paper (CP) market. Using a comprehensive database of CP issuers and issuance activity, we show that firms use CP to provide start-up financing for capital investment.
Suppose one uses a parametric density function based on the first four (conditional) moments to model risk. There are quite a few densities to choose from and depending on which is selected, one implicitly assumes very different tail behavior and very different feasible skewness/kurtosis combinations.
Volatility component models have received considerable attention recently, not only because of their ability to capture complex dynamics via a parsimonious parameter structure, but also because it is believed that they can handle well structural breaks or nonstationarities in asset price volatility.
Recognizing the importance of the person who occupies the chief marketing officer (CMO) position, we posit that a CMO’s managerial capital, as signaled by his or her education, origin, and experience, indicates what a new CMO can bring to the table.
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.
The prevailing view of implied volatility comovements, IVC, defined as the correlation between a firm’s implied volatility and the market’s implied volatility, is that they indicate the presence of systematic volatility risk to the firm’s investors. We take a different stance and conjecture that implied volatility comovements can also indicate expected information arrival for both the firm and aggregate equity markets, and we find evidence supporting this view.
We investigate the effect of CFO narcissism, as measured by signature size, on financial reporting quality. Experimentally, we validate that narcissism predicts misreporting behavior, and that signature size predicts misreporting through its association with narcissism.
With the increasing prevalence of renewable energy supply contracts, utility suppliers are investing in new green sources and developing allocation policies of those to satisfy renewable targets required by customers. However, the variability of customer demand and the intermittency in supply complicates the supplier's decision process. In this paper, we address these challenges by formulating the utility supplier's problem as a two-stage stochastic program.
We find analysts convey information about a firm’s earnings without fully revising their earnings forecast by increasing bundling intensity, which is the extent to which an analyst report that has an earnings forecast revision includes also price target and/or recommendation revisions with the same sign as the earnings forecast revision. We develop a firm-level measure of bundling intensity, BF_Score, and find it is an economically meaningful predictor of analyst-based earnings surprises.
Angel investor tax credits are commonly used around the world to spur entrepreneurship. Exploiting the staggered implementation of these tax credits in 31 U.S. states, we find that while they increase angel investment, marginal investments flow to relatively low-growth firms.
The Biden administration's $2.3 trillion American Jobs Plan comes with a hefty price tag, which the president hopes to pay in part by introducing a 15% minimum tax on corporate book income. Predictably, policymakers from both sides of the aisle are sounding off, but the argument is more complicated and nuanced than partisan rhetoric. In this Kenan Insight, we outline the intricacies and implications of taxing book income.
Introduction to machine learning techniques using the SAS platform. Agenda includes: (1) Overview of SAS Visual Data Mining and Machine Learning; (2) Hands-on exercises with SAS machine learning visual interfaces; (3) SAS Programming w/ demonstration of SAS Studio development environment; (4) Python programming with SAS machine learning including hands-on exercises; and (5) Advanced SAS AI Topics. Open only to UNC students, faculty, and staff. Space is limited, please register to attend.
On October 14, 2016, the Frank Hawkins Kenan Institute of Private Enterprise at the University of North Carolina Kenan-Flagler Business School hosted a conference titled What’s Next, America. Convened fewer than four weeks prior to the presidential election, the objective of the forum was to allow influential business leaders, academics and policy makers to examine issues critical to the U.S. economy now and in the future. The conference offered actionable solutions to the most important economic issues facing the next administration.
Developing measures to improve the traceability of contaminated food products across the supply chain is one of the key provisions of the 2011 FDA Food Safety Modernization Act (FSMA). In the event of a recall, FSMA requires companies to provide information about their immediate suppliers and customers—what is referred to as “one step forward” and “one step backward” traceability.
Private equity funds hold assets that are hard to value. Managers may have an incentive to distort reported valuations if these are used by investors to decide on commitments to subsequent funds managed by the same firm.
In the U.S. automobile industry, manufacturers distribute products through dealers and rental agencies. To mediate direct competition between the two intermediaries, manufacturers adopted buyback programs to repurchase used rental cars from rental agencies and redistribute them through dealers.