Journal of Financial Economics最新文献

We study the international coordination of bank capital requirements under a host-country rule: the requirement depends on where the borrower, not the bank, is located. In such a regime, countries compete for scarce bank equity capital. Raising a country’s requirement may generate bank capital outflows as well as inflows. We pin down the condition for the sign of the capital flow and the associated externality, and highlight the policy implications. Absent collaboration, overshooting is likely: individual countries have an incentive to increase Basel III’s Counter-Cyclical Capital Buffer too much in good times and cut it too much in bad times.

This paper studies how tightening monetary policy transmits to the economy through the mortgage market and sheds new light on the distributional consequences at both individual and regional levels. We specifically examine the sharp increase in mortgage interest rates during 2022 and 2023. We find that almost all of the decline in mortgages compared to prior years was concentrated in loans that would have had a debt-to-income (DTI) ratio above underwriting thresholds. These effects are even more pronounced for minority and middle-income borrowers. Additionally, regions more affected by the thresholds exhibited greater reductions in mortgage originations, house prices, and consumption.

We test a bank credit channel through which unexpected increases in inflation lead to short-run macroeconomic fluctuations. For identification, we study an unexpected U.S. inflation increase in early 1977 and exploit differences in state-level reserve requirements for Federal Reserve nonmember banks, which create differences in banks’ inflation exposures. More exposed banks reduce lending, lowering local house prices and construction employment. We provide evidence for potential mechanisms, including a bank net wealth and a loan misallocation channel. Our results suggest that an important consequence of inflation is its impairment of the banking sector.

We analyze two insider-trading regulations recently introduced by the Securities and Exchange Commission: mandatory disclosure and “cooling-off period”. The former requires insiders disclose trading plans at adoption, while the latter mandates a delay period before trading. These policies affect investors’ trading profits, risk sharing, and hence their welfare. If the insider has sufficiently large hedging needs, in contrast to the conventional wisdom from “sunshine trading”, disclosure reduces the welfare of all investors. In our calibration, a longer cooling-off period benefits speculators, and its implications for the insider and hedgers depend on whether the disclosure policy is already in place.

An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win “Man vs. Machine” when institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in “Man + Machine”, which also substantially reduces extreme errors. Analysts catch up with machines after “alternative data” become available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess.

We study the optimal execution problem in a principal–agent setting. A client contracts to purchase from a dealer. The dealer hedges, buying from the market, creating temporary and permanent price impact. The client chooses a contract, which specifies payment as a function of market prices; hidden action precludes conditioning on the dealer’s hedging trades. We show the first-best benchmark is theoretically achievable with an unrestricted contract set. We then consider weighted-average-price contracts, which are commonly used. In the continuous-time limit, the optimal weighting entails a constant density at interior times and discrete masses at the extremes.

Trading in cryptocurrencies grew rapidly over the last decade, dominated by retail investors. Using data from eToro, we show that retail traders are contrarian in stocks and gold, yet the same traders follow a momentum-like strategy in cryptocurrencies. The differences are not explained by individual characteristics, investor composition, inattention, differences in fees, or preference for lottery-like assets. We conjecture that retail investors have a model where cryptocurrency price changes affect the likelihood of future widespread adoption, which leads them to further update their price expectations in the same direction.

We develop a conceptual framework and an empirical methodology to analyze the effect of rising interest rates on the value of U.S. bank assets and bank stability. We mark-to-market the value of banks’ assets due to interest rate increases from Q1 2022 to Q1 2023, revealing an average decline of 10 %, totaling about $2 trillion in aggregate. We present a model illustrating how asset value declines due to higher rates can lead to self-fulfilling solvency runs even when banks’ assets are fully liquid. Banks with high asset losses, low capital, and, critically, high uninsured leverage are most fragile. A case study of the failed Silicon Valley Bank confirms the model insights. Our empirical measures of bank fragility suggest that, in the absence of regulatory intervention, many U.S. banks would have been at risk of self-fulfilling solvency runs.

We investigate how high-frequency trading (HFT) in equity markets affects options market liquidity. We find that increased aggressive HFT activity in the stock market leads to wider bid–ask spreads in the options market through two main channels. First, options market makers’ quotes are exposed to sniping risk from HFTs exploiting put–call parity violations. Second, informed trading in the options market further amplifies the impact of HFT in equity markets on the liquidity of options by simultaneously increasing the options bid–ask spread and intensifying aggressive HFT activity in the underlying market.

In the U.S., student debt is currently the second largest component of consumer debt. Households are required to repay these loans early in their lifecycle, when marginal utility is particularly high. We study alternative contracts that offer partial or full payment deferral until later in life. We calibrate an economy with the current contracts, and then solve for counterfactual equilibria. The alternative contracts yield large welfare gains, which are robust to assumptions about the behavior of the lenders and borrower preferences. The gains are similar to those that could come from the debt relief program currently being considered in the U.S., but without its adverse fiscal implications.