At-scale field experiments at major U.K. banks show that automatic enrollment into “just-in-time” text alerts reduces unarranged overdraft and unpaid item charges 17% to 19% and arranged overdraft charges 4% to 8%, implying annual market-wide savings of £170 million to £240 million. Incremental benefits from “early-warning” alerts are statistically insignificant, although economically significant effects are not ruled out. Prior to the experiments, over half of overdrafts could have been avoided by using lower-cost liquidity available in savings and credit card accounts. Alerts help consumers achieve less than half of these potential savings.
The aggregate leverage of broker-dealers responds to demand and supply disturbances that have opposite effects on financial markets. Specifically, leverage supply shocks that relax broker-dealers' funding constraints increase leverage, liquidity, and returns and carry a positive price of risk, while leverage demand shocks also increase leverage but reduce liquidity and returns and carry a negative price of risk. Disentangling demand- and supply-like shocks resolves existing puzzles around the price of leverage risk and yields consistent evidence across many markets of a central role for intermediation frictions and dealers' aggregate leverage in asset pricing.
We examine the ability of structural models to predict credit spreads using global default data and security-level credit spread data in eight developed economies. We find that two representative, pure default-risk models tend to underpredict the average credit spreads on investment-grade (IG) bonds, especially their spreads over government bonds, thereby providing evidence for a “global credit spread puzzle.” However, a model incorporating endogenous liquidity in the secondary debt market helps mitigate the puzzle. Furthermore, the model captures certain determinants of corporate bond market frictions across the eight economies and substantially improves the cross-sectional fit of individual IG credit spreads.
Uniswap is a system of smart contracts on the Ethereum blockchain and is the largest decentralized exchange with a liquidity balance worth up to 4 billion USD and daily trading volume of up to 7 billion USD. It is a new model of liquidity provision, so-called automated market making. For this new market form, we characterize equilibrium in the liquidity pools. We collect all 95.8 million Uniswap interactions and compare this automated market maker (AMM) to a centralized limit order book. We document absence of long-lived arbitrage opportunities, and show conditions under which the AMM dominates a limit order market.
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.
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.
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