Journal of Finance最新文献

A war-related factor model derived from textual analysis of media news reports explains the cross section of expected stock returns. Using a semisupervised topic model to extract discourse topics from 7,000,000 New York Times stories spanning 160 years, the war factor predicts the cross section of returns across test assets derived from both traditional and machine learning construction techniques, and spanning 138 anomalies. Our findings are consistent with assets that are good hedges for war risk receiving lower risk premia, or with assets that are more positively sensitive to war prospects being more overvalued. The return premium on the war factor is incremental to standard effects.

Aggregate U.S. bank lending to firms expanded following the outbreak of COVID-19. Using loan-level supervisory data, we show that this expansion was driven by draws on credit lines by large firms. Banks that experienced larger credit line drawdowns restricted term lending more, crowding out credit to smaller firms, which reacted by reducing investment. A structural model calibrated to match our empirical results shows that while credit lines increase total bank credit in bad times, they redistribute credit from firms with high propensities to invest to firms with low propensities to invest, exacerbating the decrease in aggregate investment.

This article quantifies the effects of a multiday cyberattack that forced offline a technology service provider (TSP) to the banking sector. The attack impaired customers’ ability to send payments through the TSP, but the business continuity plans of banks and the TSP reduced the effect by more than half. Large banks performed better. Through contagion, banks not directly exposed to the attack experienced a liquidity shortfall, causing them to borrow funds or tap reserves. The ability to send payments after hours helped avoid further contagion. These results highlight the importance of preparedness by the private and official sector for cyberattacks.

We study a search and bargaining model of over-the-counter markets for nonstandardized assets of heterogeneous quality. Once matched, investors privately learn their values positively correlated with asset quality. Bargaining results in delay that is hump-shaped in quality and U-shaped in asset turnover. We document these patterns in commercial real estate and corporate bonds markets. Extreme qualities are little affected by changes in asset standardization, while intermediate qualities are more susceptible. For nonstandardized assets, opacity ensures active trading of all assets, which explains why their trading is decentralized and suggests that trade centralization should come with greater standardization.

Based on a comprehensive data set of classified (staggered) boards covering nearly all U.S. public firms from 1991 to 2020, we show that contrary to conventional wisdom, the use of classified boards remains widespread. Moreover, classified board usage over a firm's life cycle depends significantly on the decade the firm matured or year it went public. While classified boards were rarely removed in the 1990s, firms became more likely to declassify as they matured during the following decades. Decreased collective action costs and increased innovation-related investments, institutional ownership, and scrutiny of governance contributed to this more dynamic adjustment.