Recent studies on agency problems in private equity fueled the suspicion that fund managers strategically manipulate performance estimates around fundraising times. While these studies use aggregated portfolio data, this paper offers the first analysis of “window dressing” in private equity based on deal-level performance. In contrast to previous findings of a smoking gun at the fund level, I do not find any evidence of inflated performance at the deal level. Fund performance peaks are driven by a cohort effect whereby late investments are made under pressure before fundraising and have lower returns than those made earlier in the fund’s life.

Between 1974 and 1986, the intervention of various French governments on both the right and the left—in addition to corporate maneuvering and increased focus on competitiveness and lean production—resulted in foreign direct investment, mergers, plant closures, and bankruptcies among struggling French automotive suppliers. This article will explore why these efforts were unsuccessful by revisiting the first Japanese attempts to enter the European automobile industry. It does so not only through the case of Nissan in the United Kingdom in 1984 but also through the essentially unfamiliar and contemporaneous example of French automotive suppliers.

We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based on observable characteristics, using machine learning principles with linear models. The best-performing methods are flexible two-stage sparse models that capture group-membership predictive relationships. Portfolios formed to exploit such group-varying predictions based on a parsimonious set of characteristics deliver economically meaningful returns with low turnover. We propose statistical tests based on nonparametric bootstrapping for our results, and detail how different characteristics may matter for different groups of firms, making comparisons to the existing literature.

Cybervetting is the widespread practice of employers culling information from social media and/or other internet sources to screen and select job candidates. Research evaluating online screening is still in its infancy; that which exists often assumes that it offers value and utility to employers as long as they can avoid discrimination claims. Given the increasing prevalence of cybervetting, it is extremely important to probe its challenges and limitations. We seek to initiate a discussion about the negative consequences of online screening and how they can be overcome. We draw on previous literature and our own data to assess the implications of cybervetting for three key stakeholders: job candidates, hiring agents, and organizations. We also discuss future actions these stakeholders can take to manage and ameliorate harmful outcomes of cybervetting. We argue that it is the responsibility of the organizations engaged in cybervetting to identify specific goals, develop formal policies and practices, and continuously evaluate outcomes so that negative societal consequences are minimized. Should they fail to do so, professional and industry associations as well as government can and should hold them accountable.

The rapid pace at which technology changes creates a challenge for industrial-organizational (I-O) psychologists, who often conduct hypothetico-deductive research. In this article, we examine technology research in the I-O psychology community by asking three questions: Why should I-O psychologists study new technologies? How timely is I-O psychologists’ technology research? How can I-O psychologists produce timelier technology research? Using archival data from 23 years of SIOP conferences and a historical timeline of technology innovations, we find that I-O psychologists study technology milestones an average of 6.10 years after they first enter widespread awareness and adoption. We discuss the implications of this lag and conclude by urging I-O psychologists to study technology with an eye toward action, exploration, collaboration, dissemination, and creation.

We examine whether natural disaster experiences affect households’ portfolio choice decisions. Using data from the National Longitudinal Survey of Youth 1979, we find that adversely affected households are less likely to participate in risky asset markets. After a disaster shock, households become more risk-averse and lower their expectations on future stock market returns. Such conservative portfolio choices persist even after households relocate to less disaster-prone areas, consistent with risk preferences being altered by disaster experiences. Overall, our evidence suggests that transient but salient experiences can be an important factor in explaining the limited participation puzzle.

Using firm-level survey information, we investigate whether relationship lending affects firms’ employment decisions in the face of negative sales shock. We find that firms with a durable relationship with their main bank display significantly less employment growth sensitivity to such shocks, especially where these are transitory. The result is stronger for younger and smaller firms that benefit from tighter bank-firm relationships, and for firms in sectors or economic environments where the costs of employment adjustment are greater. Our findings indicate that relationship lending provides liquidity insurance to firms to meet their demand for labor hoarding.