We demonstrate analytically that cross-sectional variation in the effects of events, i.e., in true abnormal returns, necessarily produces event-induced variance increases, biasing popular tests for mean abnormal returns in short-horizon event studies. We show that unexplained cross-sectional variation in true abnormal returns plausibly produces nonproportional heteroskedasticity in cross-sectional regressions, biasing coefficient standard errors for both ordinary and weighted least squares. Simulations highlight the resulting biases, the necessity of using tests robust to cross-sectional variation, and the power of robust tests, including regression-based tests for nonzero mean abnormal returns, which may increase power by conditioning on relevant explanatory variables.

We investigate incentive effects of a typical hedge fund contract for a manager with power utility. With a one-year horizon, the manager displays risk taking that varies dramatically with fund value. We extend the model to multiple yearly evaluation periods and find that the manager's risk taking is rapidly moderated if the fund performs reasonably well. The most realistic approach to modeling fund closure uses an endogenous shutdown barrier where the manager optimally chooses to shut down. The manager increases risk taking as fund value approaches that barrier, and this boundary behavior persists strongly with multiyear horizons.