Knowledge that Transforms

Between 2009 and 2011, the Spanish banking system underwent a restructuring process based on savings banks’ consolidation. The program’s design allows us to study how banks’ consolidation affects credit supply and performance. We propose a quasi-experimental analysis showing that bank mergers restrict credit supply and set higher interest rates but also reject fewer applicants and report fewer nonperforming loans. We then estimate a structural model of credit in which banks set interest rates and lending standards. We find that, despite the relaxation in their lending standards, merged banks’ credit performance improved thanks to a significant drop in their screening costs.

We study the structure of public firm buyouts in a model that features the Berle-Means problem (lack of incentives) and the Grossman-Hart problem (holdout). We find that bootstrapping, debt in excess of funding needs, and upfront fees to bidders are socially optimal and increase buyout premiums. These elements make LBO financing tantamount to a “management contract” arranged by an outside manager to receive cash and incentives to manage a firm—except the cash is funded by excess debt imposed on the firm. Our model also rationalizes why PE firms collect fees from their equity partnerships and directly from target firms.

We combine administrative data with a life cycle structural model that exploits the unique features of the U.K. mortgage market to analyze the sources of inaction and to estimate borrowers’ nonpecuniary remortgaging costs. The utility costs needed to generate a given level of inaction depend on monetary gains from remortgaging and on the importance of those monetary gains for agents, which in turn depend on the (endogenously determined) marginal utility of consumption. The model results reveal significant nonpecuniary costs of action that, when measured as a proportion of borrower income, are larger for the young and for lower-income households.

We study the importance of production networks for the transmission of monetary policy using the stock market reaction as laboratory. We attribute 55% to 85% of the overall response to network effects. Large network effects are a robust feature of the data; we document similar patterns in realized fundamentals. Matching sparsity and the first two outdegrees industry-by-industry can explain large network effects. A simple model with intermediate inputs predicts the reaction of stock returns follows a spatial autoregression, which we exploit for our empirical strategy. Our results suggest production networks are an important mechanism for transmitting monetary policy shocks.

Mortgage cramdown enabled bankruptcy judges to discharge the underwater portion of a mortgage in a chapter 13 bankruptcy until the Supreme Court disallowed this practice in 1993. We investigate the impact of mortgage cramdown on household distress exploiting the random assignment of cases to judges. The impact of bankruptcy protection on foreclosures is reduced by more than half after the Supreme Court disallowed cramdown. Our results suggest that large principal reductions considerably decrease homeowners’ distress by reducing debt overhang.

This paper derives ex-ante confidence intervals for stock risk premium forecasts that are based on a wide range of linear and machine learning models. Exploiting the cross-sectional variation in the precision of risk premium forecasts, I provide improved investment strategies. The confident-high-low strategies that take long-short positions exclusively on stocks with precise risk premium forecasts outperform traditional high-low strategies in delivering superior out-of-sample returns and Sharpe ratios across all models. The outperformance increases (decreases) with the model complexity (bias). The confident-high-low strategies are economically interpretable as trading strategies of ambiguity-averse investors who account for confidence intervals around risk premium forecasts.

Traditional and shadow banks interacted in similar ways in the 2007 and COVID-19 crises, when both assets and liabilities flew out of shadow banks and into traditional banks. We explain these facts in a model of the coexistence of traditional and shadow banks in which liabilities and assets flow from the former to the latter in good times, avoiding regulation, and move the other way in a crisis, alleviating fire sales. The model sheds light on how regulations for traditional banks have (unintended) consequences on the shadow banking sector.

In March 2020, safe asset markets experienced surprising and unprecedented price crashes. We explain how strategic investor behavior can create such market fragility in a model with investors valuing safety, investors valuing liquidity, and constrained dealers. While safety investors and liquidity investors can form a symbiotic relationship with offsetting trades during times of stress, strategic interactions among liquidity investors harbor the potential for self-fulfilling fragility. When the market is fragile, standard flight-to-safety can have a destabilizing effect and trigger a “dash-for-cash” by liquidity investors. Well-designed policy interventions can reduce market fragility ex ante and restore orderly functioning ex post.

Wholesale market makers pay for retail options orders that must be executed on exchanges. Payment for order flow (PFOF) wholesalers compete via price improvement in exchange auctions. To attract retail orders, wholesalers run more auctions when their recent price improvement has been lower. However, auction price improvement lowers market maker revenues. Wholesalers earn revenues to pay PFOF in nonauction trades where their designated market maker status increases their execution priority. While some auctions produce substantial price improvement, most do not have multiple bidders offering meaningful price improvement. Overall, options market structure better promotes competition in auctions than in nonauctions.

What explains wealth and portfolio differences between black and white Americans? We find that disparities in economic factors explain portfolios well, but only partly explain the wealth gap. In a dynamic setting, economic factors often change optimal saving rates in ways that offset their effects on income and returns. Consequently, their net wealth effect is often limited, making the wealth gap harder to explain. We estimate that differences in income levels, income risk, family structures, mortality, health expenditures, property taxes, mortgage rates, and asset returns explain half of the differential between the racial wealth gap and the racial income gap.