We use slow and fast time-series momentum to characterize four stock market cycles—Bull, Correction, Bear, and Rebound. The steep market declines of Bears concentrate in high-risk states, yet predict negative expected returns, which is difficult to rationalize by most models of time-varying risk premia. Using a model to analyze slow and fast momentum strategies, we estimate both relatively high mean persistence and realization noise in U.S. stock market returns. Intermediate-speed momentum portfolios, formed by blending slow and fast momentum strategies, translate predictive information in market cycles into positive unconditional alpha, for which we propose a novel decomposition.

Financial statement fraud generates many negative effects, including reducing people's willingness to participate in the stock market. If it also stigmatizes accounting, it may similarly adversely affect the quantity and quality of workers willing to become accountants, thereby potentially creating negative effects for years to come. We examine the impact of fraud on the labor force entering the accounting profession, which is a key input into the production of accounting information (i.e., the output). Using data describing millions of college students across the United States, we find incoming students are actually more likely to major in accounting when local frauds occur during their formative years. These students are also more likely to have attributes desired by the accounting profession (e.g., high academic aptitude) and are more likely to subsequently serve in public accounting and become Certified Public Accountants. In the context of other fields (i.e., all college majors), we find that fraud similarly spurs interest in other business disciplines, but not in majors outside of business schools. Those attracted to other business disciplines, however, generally possess different traits. Specifically, students entering accounting are distinctively more likely to exhibit values espoused by the accounting profession, including a predisposition to public service and less commercial orientation. Thus, nonpecuniary motives appear to uniquely drive accounting student enrollment following fraud. Collectively, our findings suggest that, while fraud is unmistakably bad, it appears to have the positive unintended consequence of attracting labor into business disciplines and, in accounting, increasing the prevalence of desirable traits among entrants.

We introduce a portfolio friction in a two-country DSGE model where investors face a constant probability to make new portfolio decisions. The friction leads to a more gradual portfolio adjustment to shocks and a weaker portfolio response to changes in expected excess returns. We apply the model to monthly data for the US and the rest of the world for equity portfolios. We show that the model is consistent with a broad set of evidence related to portfolios, equity prices, and excess returns for an intermediate level of friction. The evidence includes portfolio inertia, limited sensitivity to expected excess returns, a significant impact of financial shocks, excess return predictability, and asset price momentum and reversal.

Patented knowledge capital improves the transparency of research and development expenditures, converts intangible intellectual property into collateralizable and salable assets, enhances sustained competitive advantage by enabling firms to weather business cycles, withstand obsolescence risk and competitive threats, and lowers future financing risk and growth uncertainties. Consistent with this conjecture, we find that knowledge capital, proxied by stocks of patents, their forward citations and estimated market value, is associated with lower future cost of equity as well as firm risk. These findings appear robust to controlling for the stock of R&D expenses, potential endogeneity concerns about firms’ innovative activities, controls for technology spillovers from industry rivals, and product market competition.

This paper compares outcomes from informally negotiated oil and gas leases to those awarded via centralized auction. We focus on Texas, where legislative decisions in the early twentieth century assigned thousands of proximate parcels to different mineral allocation mechanisms. We show that during the fracking boom, which began unexpectedly decades later, auctioned leases generated at least 55 percent larger up-front payments and 40 percent more output than negotiated leases did. These results suggest large potential gains from employing centralized, formal mechanisms in markets that traditionally allocate in an unstructured fashion, including the broader $3 trillion market for privately owned minerals.

This study examines how information spillovers from equity to debt markets (“equity spillovers”) affect sell-side debt analysts’ activities and the informativeness of their reports. To explore this relation, I compare debt analyst reports for firms without equity spillovers (private firms) to firms with equity spillovers (public firms). I find that, absent equity spillovers, analysts rely more on mandatory SEC filings to fill the information gap for private firms. This additional effort, however, reduces debt analysts’ coverage of private firms overall. Conditional on coverage, the content of debt analyst reports is remarkably similar across private and public firms. Finally, I find that debt analyst reports are more informative for private firms than for public firms. Collectively, my findings shed light on the externalities created by equity spillovers and how debt market intermediaries contribute to the price discovery process.

This essay is based on my plenary address at the 105th Annual Meeting of the American Accounting Association (AAA). I am honored to have been selected as the AAA 2021 Presidential Scholar. This essay represents a call to action. I invite my fellow accounting professors to fulfill our stated mission and shared values to be thought leaders in accounting. I present ideas and suggestions on how we can lead our students, our academic institutions, and the accounting profession in supporting a prosperous and just society.

This article develops a discrete choice model of demand with optimal sequential consumer search. Consumers first choose a product to search; then, once they learn the utility they get from the searched product, they choose whether to buy it or to keep searching. We characterize the search problem as a standard discrete choice problem and propose a parametric search cost distribution that generates closed-form expressions for the probability of purchasing a product. We propose a method to estimate the model that supplements aggregate product data with individual-specific data which allows for the separate identification of search costs and preferences. We estimate the model using data from the automobile industry and find that search costs have non-trivial implications for elasticities and markups. We study the effects of exclusive dealing regulation and find that firms benefit at the expense of consumers, who face higher search costs and higher prices than would be the case if multi-brand dealerships were used.

Going public by merging with a Special Purpose Acquisition Company (SPAC) is much more expensive than conducting a traditional IPO. We rationalize why some companies merge with a SPAC by listing the potential benefits. We analyze the agency problems that certain SPAC features address. SPAC IPO investors and deal sponsors have earned remarkably high annualized average returns, although we warn that recent deals are likely to disappoint. Public investors in the merged companies have earned very low market-adjusted returns on an equally weighted basis, although high redemptions on the worst deals have limited the amount of money that they lost.

Firm-level variables that predict cross-sectional stock returns, such as price-to-earnings and short interest, are often averaged and used to predict market returns. Using various samples of cross-sectional predictors and accounting for the number of predictors and their interdependence, we find only weak evidence that cross-sectional predictors make good time-series predictors, especially out-of-sample. The results suggest that cross-sectional predictors do not generally contain systematic information.