Journal of Financial Economics最新文献

We provide the first analysis of the risk exposure and risk-adjusted performance of impact investing funds, private market funds with dual financial and social goals. We introduce a dataset of impact fund cash flows and exploit distortions in VC performance measures to characterize risk profiles. Impact funds have a lower market $\beta$ than comparable private market strategies. Accounting for $\beta$, impact funds underperform the public market, though not necessarily more so than comparable strategies. We consider alternative pricing models, accounting for sustainability and emerging markets risk. We show investors’ wealth portfolios and taste change the perceived financial merit of impact investing.

We document aggregate outflows from corporate bond mutual funds days before and after the announcement of increases in the Federal Funds Target rate (FFTar). To rationalize this phenomenon, we build a model in which funds’ net-asset-values (NAVs) are stale and investors strategically redeem to profit from the mispricing when they learn about the increases of FFTar. Consistent with the model’s predictions, we find that stale NAVs and loose monetary policy environments weaken (strengthen) outflows sensitivity to increases in FFTar during illiquid (liquid) market conditions. Our results highlight when and how monetary policy could systematically exacerbate the fragility of corporate bond funds.

A critical question facing speculators contemplating to trade on private information is whether their signal has already been priced in by the market. In our model, speculators assess the novelty of their information based on recent price movements, and market makers are aware that speculators might be trading on stale news. An asymmetric response to past price movements ensues: after price increases, buy volume – because it may result from stale news trading – has a lower price impact than sell volume (and vice versa after price decreases). Consequently, return skewness is negatively related to lagged returns. We find strong support for these and other predictions using a comprehensive sample of US stocks.

Popular bid–ask spread estimators are downward biased when trading is infrequent. Moreover, they consider only a subset of open, high, low, and close prices and neglect potentially useful information to improve the spread estimate. By accounting for discretely observed prices, this paper derives asymptotically unbiased estimators of the effective bid–ask spread. Moreover, we combine them optimally to minimize the estimation variance and obtain an efficient estimator. Through theoretical analyses, numerical simulations, and empirical evaluations, we show that our efficient estimator dominates other estimators from transaction prices, yields novel insights for measuring bid–ask spreads, and has broad applicability in empirical finance.

Consistent with contemporaneous research, we document that minority-owned firms were more likely than observationally similar white-owned firms to receive PPP loans from nonbank lenders than from banks. However, we show that this substitution to nonbanks was only partial, resulting in significantly lower PPP take-up by minority-owned firms, particularly Black-owned ones. Location and firm characteristics explain about two-thirds of the 25 percentage point disparity in PPP take-up by Black-owned firms. While there was greater substitution to nonbanks in more racially biased locations, overall take-up was still lower in those locations. Access to professional help with applications facilitated use of nonbanks and mitigated disparities.

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.