We develop methods for testing factor models when the weights in portfolios of factors and test assets can vary with lagged information. We derive and evaluate consistent standard errors and finite sample bias adjustments for unconditional maximum squared Sharpe ratios and their differences. Bias adjustment using a second-order approximation performs well. We derive optimal zero-beta rates for models with dynamically trading portfolios. Factor models’ Sharpe ratios are larger but standard test asset portfolios’ maximum Sharpe ratios are larger still when there is dynamic trading. As a result, most of the popular factor models are rejected.

The year 2020 set into motion a perfect storm that would lead to the global panic ignited by the murder of George Floyd in late May of that year. The COVID-19 virus impacted billions of people around the world. With many forced to shelter in place at home, some Americans for the first time (and an exhaustingly innumerable time for others) observed up close the inequality apparent in American policing. On average, Black Americans are 2.9 times more likely to be shot and killed by the police, with very few officers held accountable and prosecuted for these deaths.1 One cannot make sense of this special section on Corporate Responses to Racial Unrest without an examination of this fact and the events leading up to Floyd’s murder. Statistically speaking, however, the year 2020 did not signal anything unusual. In that year, according to Statistica.com, U.S. police killed 1,020 people. Fatalities had been rising steadily from 981 in 2017 to 983 in 2018 and 999 in 2019. 2 It is not immediately apparent how best to interpret these numbers. What justifies police use of deadly force, and in turn, what is an acceptable rate of police killings per year? Or is this even a productive line of thought? The effectiveness of police power in the United States has been a standing debate since the foundations of American government.3 And the nature of inequality marked by race within policing has been demonstrated countless times in the literature.4 Reforming the phenomenon of “policing” in the United States, however, though simple to call for, is complicated to enact—not least owing to the “blue wall of silence” that protects police officers from the consequences of misconduct and the near-term spikes in crime and expenses that the very communities who are most disadvantaged by current policing practices are forced to bear.

This article explores the growth of abortion-related businesses in New York State that emerged to encourage Canadian women to travel across the border to access care. Referral agencies and clinics advertised their services, publicized their fees, and competed with each other. Canadian women living near the border were used to crossing to access goods and services not available in their home market. Their practice of traveling to New York for abortions was shaped by their experiences as consumers. The media used the language of commerce to explain this phenomenon, describing those involved in referral agencies as entrepreneurs and businessmen, highlighting the profits being made and evaluating the services being offered.

We investigate how directors’ positions within board interlocking networks influence their monitoring behaviors from a social network perspective. We argue that the effectiveness of directors’ monitoring of a firm's management depends on their ability to overcome the information barrier and their motivation to develop a public reputation in the directorship market. We further contend that network centrality can supplement directors’ existing information set and facilitate reputation spillover, leading to an increase in the extent of their dissent on boards. We analyze the unique individual-director-level data of Chinese firms and find that directors occupying positions of greater centrality in the board interlock network are more likely to dissent. We then examine the underlying mechanisms of information and reputation through two moderators: firm transparency and media mention of a director. We also find that the effect of network centrality on dissent is weaker for independent directors. Our study advances the corporate governance literature by examining the micro-foundations of board monitoring and providing a social network perspective.

This study adopts the resource-based view (RBV) to explain the difference in firm profit and growth determinants. We argue that profit is driven more by valuable, rare, inimitable, and non-substitutable (VRIN) resources, and growth is driven more by versatile resources. Since some versatile resources, such as cash, are less firm-specific, the firm effect is more critical in determining profit than growth. We also expect that emerging market firms are more capable of utilizing versatile resources than developed market firms, and developed market firms are more capable of utilizing VRIN resources than emerging market firms. As a result, the determinants of firm performance also differ between emerging and developed markets. The study employs multilevel mixed models to decompose firm performance in US, Chinese, and global samples. The findings confirm that the firm effect is more important in influencing profit than growth, persisting across all three samples. The firm effect is also more important in influencing performance in developed countries than in emerging markets.

We show that the negative relation between real investments and future stock returns is primarily driven by the subsample of firms building additional capacity. We develop a real options model to rationalize that evidence based on the premise that firms need to learn how to best operate modern capacity vintages, inducing idiosyncratic uncertainty in that capacity’s production costs over the learning period. Conversely, the uncertainty lowers the expected return of firms with newly built capacity until it is resolved. Further evidence based on profit sensitivities to aggregate conditions; analyst forecast-error volatilities; and high- versus low-tech industry subsamples supports our uncertainty explanation.