This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling one‐minute‐ahead return forecasts using the entire cross‐section of lagged returns as candidate predictors. The LASSO increases both out‐of‐sample fit and forecast‐implied Sharpe ratios. This out‐of‐sample success comes from identifying predictors that are unexpected, short‐lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identify predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals.

This paper investigates whether individuals that we identify as “connectors”—who possess a blend of innate traits and skills that predispose them to be personable, willing to relate to others, and able to influence others’ relationships—can serve as a catalyst for improving group outcomes. More specifically, we explore whether identifying connectors and placing them in work groups can serve as a control to help firms manage undesirable voluntary employee turnover by improving the group experience and reducing their fellow group members’ turnover intentions. We conduct an experiment to test our hypotheses that members in a group with a connector (versus without) have lower turnover intentions because their experiences are perceived as more positive, and that this turnover intention effect is more pronounced for group members who are demographically distinct from others in their group. Results are consistent with predictions, although the effect of connectors on lowering group members’ turnover intentions is driven by members who are distinct. Our findings broaden the understanding of who connectors are and how they affect group interactions, and further suggest that hiring and deploying connectors in work groups can be an effective component of a more comprehensive retention strategy.

We conduct a controlled laboratory experiment in the spirit of Merton (1971), in which subjects dynamically choose their portfolio allocation between a risk-free and risky asset. Using the optimal allocation of an investor with hyperbolic absolute risk aversion (HARA) utility, we fit the experimental choices to characterize the risk profile of our participants. Despite substantial heterogeneity, decreasing absolute risk aversion and increasing relative risk aversion are the predominant types. We also find some evidence of increased risk taking after a gain. Finally, the session level risk attitudes show a different profile than the individual descriptions of risk attitudes.

Traditional approaches to structural vector autoregressions (VARs) can be viewed as special cases of Bayesian inference arising from very strong prior beliefs. These methods can be generalized with a less restrictive formulation that incorporates uncertainty about the identifying assumptions themselves. We use this approach to revisit the importance of shocks to oil supply and demand. Supply disruptions turn out to be a bigger factor in historical oil price movements and inventory accumulation a smaller factor than implied by earlier estimates. Supply shocks lead to a reduction in global economic activity after a significant lag, whereas shocks to oil demand do not.

We analyze the expected value of information about an agent's type in the presence of moral hazard and adverse selection. Information about the agent's type enables the principal to sort/screen agents of different types. The value of the information decreases in the variability of output and the agent's risk aversion, two factors that are typically associated with the severity of the moral hazard problem. However, the value of the information about agent type first increases but ultimately decreases in the severity of adverse selection. The decrease comes about because the means available to the principal to induce effort—namely, the pay–performance sensitivity—must also be used to sort/screen agents, and these two goals conflict. This decline in value occurs despite the monotonically increasing importance of the information in determining the principal's expected profits. Further, we show that the peak value of information occurs at a predictable level of adverse selection. These results imply that over some range, the importance of the information will be increasing, and the value of the information will be simultaneously decreasing, in the severity of adverse selection.

In the late nineteenth century Britain had almost no mandatory shareholder protections, but had very developed financial markets. We argue that private contracting between shareholders and corporations meant that the absence of statutory protections was immaterial. Using approximately 500 articles of association from before 1900, we code the protections offered to shareholders in these private contracts. We find that firms voluntarily offered shareholders many of the protections that were subsequently included in statutory corporate law. We also find that companies offering better protection to shareholders had less concentrated ownership. Received August 19, 2016; editorial decision October 24, 2018 by Editor David Denis.

We study the introduction of a wealth-management robo-adviser that constructs portfolios tailored to investors’ holdings and preferences. Adopters are similar to non-adopters in terms of demographics and prior interactions with human advisers but tend to be more active and have greater assets under management. Investors adopting robo-advising experience diversification benefits. Ex ante undiversified investors increase stock holdings and hold portfolios with less volatility and better returns. Already well-diversified investors hold fewer stocks, yet see some reduction in volatility, and trade more after adoption. All investors increase attention based on online account logins. We find that adopters exhibit declines in prominent behavioral biases, including the disposition, trend chasing, and rank effect. Our results emphasize the promises and pitfalls of robo-advising tools, which are becoming ubiquitous all over the world.Received May 31, 2017; editorial decision August 21, 2018 by Editor Wei Jiang.

We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions and characterize the potential for estimation and inference improvements. We also present new covariate-adjusted mean-squared error expansions and robust bias-corrected inference procedures, with heteroskedasticity-consistent and cluster-robust standard errors. We provide an empirical illustration and an extensive simulation study. All methods are implemented in R and Stata software packages.

Prior research documents that providing relative performance information (RPI) motivates employees to increase effort; however, a potential downside of RPI is that it also motivates employees to distort their effort allocations between tasks such that it can be detrimental to overall firm performance. This study investigates via an experiment how the informativeness of RPI affects employees' effort allocations and performance in a multitask environment. We investigate the informativeness of two RPI design choices that are observed in practice: detail level and temporal aggregation. Regarding detail level, firms may provide each employee's performance ranking on tasks, which is less informative than providing the actual performance score of each employee. Regarding temporal aggregation, firms may provide RPI that is reset each period, which is less informative than RPI that is based on cumulative performance. We find RPI detail level and temporal aggregation interact to influence effort distortion. Specifically, we find that, compared to reset RPI, cumulative RPI leads to greater distortion of effort away from firm‐preferred allocations and that this effect is magnified when RPI provides actual performance scores rather than performance rankings. Finally, high levels of effort distortion hurt overall performance, thereby demonstrating the potentially detrimental effect of effort distortion on performance. Results of our study enhance our understanding of how firms can use their control over the design of RPI to enhance its usefulness in directing employees' effort in multitask environments by highlighting the role that informativeness of information can have on employee behavior.