Journal of Financial Economics最新文献

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.

We find that central bank reserves injected by QE crowd out bank lending. We estimate a structural model with cross-sectional instrumental variables for deposit and loan demand. Our results are determined by the elasticity of loan demand and the impact of reserve holdings on the cost of supplying loans. The reserves injected by QE raise loan rates by 7.4 basis points, and each dollar of reserves reduces bank lending by 7.7 cents. Our results imply that a large injection of central bank reserves has the unintended consequence of crowding out bank loans because of bank balance sheet costs.

Transaction costs have a first-order effect on the performance of currency portfolios. Proportional costs based on quoted bid–ask spread are relatively small, but when a fund is large, costs due to the trading volume price impact are sizable and quickly erode returns, leaving many popular strategies unprofitable. A mean–variance-transaction-cost optimized approach (MVTC) that accounts for costs in the optimization efficiently tackles the problem with only relatively minor negative implications on before-cost profitability. MVTC is robust even when the price impact of trading is severe. Finally, we introduce an accurate extrapolation approach to expand the sample of the realized Amihud measure of Ranaldo and Santucci de Magistris (2022) from 12 to 26 currencies and from 2012 back in time to 1986.

We estimate a structural model of borrowing and lending in the illegal money lending market using a unique panel survey of 1,090 borrowers taking out 11,032 loans from loan sharks. We use the model to evaluate the effects of interventions aimed at limiting this market. We find that an enforcement crackdown that occurred during our sample period increased lenders’ unit cost of harassment and interest rates, while lowering volume of loans, lender profits and borrower welfare. Policies removing borrowers in the middle of the repayment ability distribution, reducing gambling or reducing time discounting are also effective at lowering lender profitability.