Knowledge that Transforms

We investigate a seller's strategic choice between optimally structured negotiations with fewer bidders and an auction with more competing bidders when payments can have a contingent component, as is common in mergers and acquisitions (M&A), patent licensing, and employee compensation. The key factor favoring negotiations is that it allows the seller to set her preferred payment structure—that is, the revenue‐maximizing mix of cash and contingent pay; reserve prices are of secondary importance. Negotiations are more likely to dominate if synergies increase in bidders' productivity types (as with acquirer‐target complementarities in M&A). Higher dispersion and magnitude of bidders' private valuations also favor negotiations.

We study a search and bargaining model of over‐the‐counter markets for nonstandardized assets of heterogeneous quality. Once matched, investors privately learn their values positively correlated with asset quality. Bargaining results in delay that is hump‐shaped in quality and U‐shaped in asset turnover. We document these patterns in commercial real estate and corporate bonds markets. Extreme qualities are little affected by changes in asset standardization, while intermediate qualities are more susceptible. For nonstandardized assets, opacity ensures active trading of all assets, which explains why their trading is decentralized and suggests that trade centralization should come with greater standardization.

We assess racial discrimination in mortgage approvals using confidential data on mortgage applications. Minority applicants tend to have lower credit scores and higher leverage, and are less likely to receive algorithmic approval from race‐blind automated underwriting systems (AUS). Observable applicant‐risk factors explain most of the racial disparities in lender denials. Further, exploiting the AUS data, we show there are risk factors we do not observe, and these factors at least partially explain the residual 1 to 2 percentage point denial gaps. We conclude that differential treatment plays a more limited role in generating denial disparities than previous research suggests.

Portfolio allocation decisions increasingly incorporate social values. We develop a tractable framework to study how competition between investors to own socially valuable assets affects social welfare. Relative to the most common social‐investing strategies, we identify alternative strategies that result in higher impact and higher financial returns. We identify strategies for investors to have impact when impact is difficult to measure. From the firm's perspective, increasing profitability can have greater impact than directly increasing social value. We present new empirical evidence on the social preferences of investors that demonstrates the practical relevance of our theory.

We study the optimal design of stress scenarios. A principal manages the unknown risk exposures of agents by asking them to report losses under hypothetical scenarios before taking remedial actions. We apply a Kalman filter to solve the learning problem, and we relate the optimal design to the risk environment, the principal's preferences, and available interventions. In a banking context, optimal capital requirements cover losses under an adverse scenario, while targeted interventions depend on covariances among residual exposures and systematic risks. Our calibration reveals that information is particularly valuable for targeted interventions as opposed to broad capital requirements.

I study how the secular decline in interest rates affects banks' intermediation spreads and credit supply. Following a permanent decrease in rates, bank lending may rise initially but contracts in the long run. As lower rates compress deposit spreads even well above the zero lower bound, banks' retained earnings, equity, and lending fall until loan spreads have risen enough to offset the reduction in deposit spreads. A higher inflation target can support bank lending at the cost of higher liquidity premia. I find support for the model's predictions in U.S. aggregate and bank‐level data.

We study links between the labor market for loan officers and access to mortgage credit. Using novel data matching mortgage applications to loan officers, we find that minorities are underrepresented among loan officers. Minority borrowers are less likely to complete mortgage applications, have completed applications approved, and to ultimately take up a loan. These disparities are reduced when minority borrowers work with minority loan officers. These pairings also lead to lower default rates, suggesting minority loan officers have an informational advantage with minority borrowers. Our results suggest minority underrepresentation among loan officers reduces minority borrowers’ access to credit.

Corporate credit lines are drawn more heavily when funding markets are stressed. This elevates expected bank funding costs. We show that credit supply is dampened by the associated debt‐overhang cost to bank shareholders. Until 2022, this impact was reduced by linking the interest paid on lines to a credit‐sensitive reference rate like the London interbank offered rate (LIBOR). We show that transition to risk‐free reference rates may exacerbate this friction. The adverse impact on credit supply is offset if drawdowns are expected to be deposited at the same bank, which happened at some of the largest banks during the global financial crisis and COVID recession.

Based on a comprehensive data set of classified (staggered) boards covering nearly all U.S. public firms from 1991 to 2020, we show that contrary to conventional wisdom, the use of classified boards remains widespread. Moreover, classified board usage over a firm's life cycle depends significantly on the decade the firm matured or year it went public. While classified boards were rarely removed in the 1990s, firms became more likely to declassify as they matured during the following decades. Decreased collective action costs and increased innovation‐related investments, institutional ownership, and scrutiny of governance contributed to this more dynamic adjustment.

We show that two important issues in empirical asset pricing—the presence of weak factors and the selection of test assets—are deeply connected. Since weak factors are those to which test assets have limited exposure, an appropriate selection of test assets can improve the strength of factors. Building on this insight, we introduce supervised principal component analysis (SPCA), a methodology that iterates supervised selection, principal‐component estimation, and factor projection. It enables risk premia estimation and factor model diagnosis even when weak factors are present and not all factors are observed. We establish SPCA's asymptotic properties and showcase its empirical applications.