We examine whether the post-earnings announcement drift (PEAD) varies cross-sectionally with short-horizon return predictability from order flows, which characterizes the information environment and reflects the extent to which information is efficiently impounded in prices. We first demonstrate that this proxy for market efficiency (developed by Chordia, Roll, and Subrahmanyam 2008) captures the degree of market frictions that limit arbitrage activities. We then present evidence that the inverse of short-horizon return predictability is negatively associated with the PEAD and remains statistically and economically significant after controlling for a wide range of explanatory variables used in prior research. Finally, although we find that profits of implementing the PEAD trading strategy are significantly reduced by transaction costs, we demonstrate that profits continue to remain statistically and economically significant for the less efficient firms that face otherwise higher barriers to arbitrage. Our results indicate that short-horizon return predictability from order flows better explains stock returns after earnings announcements.

We develop a new approach to approximating asset prices in the context of continuous-time models. For any pricing model that lacks a closed-form solution, we provide a closed-form approximate solution, which relies on the expansion of the intractable model around an “auxiliary” one. We derive an expression for the difference between the true (but unknown) price and the auxiliary one, which we approximate in closed-form, and use to create increasingly improved refinements to the initial mispricing induced by the auxiliary model. The approach is intuitive, simple to implement, and leads to fast and extremely accurate approximations. We illustrate this method in a variety of contexts including option pricing with stochastic volatility, computation of Greeks, and the term structure of interest rates.

This study investigates the relationship between monitoring, decision making, and learning among lower-level employees. We exploit a field-research setting in which business units vary in the “tightness” with which they monitor employee decisions. We find that tighter monitoring gives rise to implicit incentives in the form of sharp increases in employee termination linked to “excessive” use of decision-rights. Consistent with these implicit incentives, we find that employees in tightly monitored business units are less likely than their loosely monitored counterparts to: (1) use decision-rights; and (2) adjust for hard information, in the form of historical performance data, in their decisions. These decision-making patterns are associated with large and systematic differences in learning rates across business units. Learning is concentrated in business units with “loose monitoring” and entirely absent in those with “tight monitoring.” The results are consistent with an experimentation hypothesis in which tight monitoring of decisions leads to more control but less learning. Data Availability: The data used in this study cannot be made publicly available due to confidentiality agreements with the participating organization.