Corporate Governance, Product Market Competition and Announcement Returns of Spinoff Firms

Our paper investigates the relationship between corporate governance (internal corporate governance mechanism) and announcement returns of spinoff firms, and examines whether such relationship can be explained by product market competition (external corporate governance mechanism). Using a sample of 269 completed spinoffs between 1983 and 2009, we find a nonlinear U-shaped relationship between corporate governance and the cumulative abnormal return around the announcement period. Moreover, we find that such a nonlinear relationship hinges on the level of competition in the market in which the spinoff firms operate. Specifically, we find that weak governance firms experience higher announcement period return only in highly competitive industries, while strong governance firms exhibit higher announcement period return, but only in moderately competitive industries. Our findings reconcile the mixed results in the literature regarding the relationship between corporate governance and firm value by examining the effect of product market competition on this relationship. Our results highlight the importance of product market competition as a moderator between corporate governance and the announcement period return of the spinoff firms.

The Momentum Gap and Return Predictability

The formation period return difference between past winners and losers, which I call the momentum gap, negatively predicts momentum profits. I document this for the U.S. stock market and find consistent results across 21 major international markets. A one-standard-deviation increase in the momentum gap predicts a 1.25$\%$ decrease in the monthly momentum return after controlling for existing predictors. This predictability extends up to 5 years for static momentum portfolios, consistent with time-varying investor biases. Following the simple real-time strategy of investing in momentum only when the momentum gap is below the 80th percentile delivers a Sharpe ratio of 0.78.

An Evaluation of Block-Maximum-Based Estimation of Very Long Return Period Precipitation Extremes with a Large Ensemble Climate Simulation

The recurring devastation caused by extreme events underscores the need for reliable estimates of their intensity and frequency. Operational frequency and intensity estimates are very often obtained from generalized extreme value (GEV) distributions fitted to samples of annual maxima. GEV distributed random variables are “max-stable,” meaning that the maximum of a sample of several values drawn from a given GEV distribution is again GEV distributed with the same shape parameter. Long-period return value estimation relies on this property of the distribution. The data to which the models are fitted may not, however, be max-stable. Observational records are generally too short to assess whether max-stability holds in the upper tail of the observations. Large ensemble climate simulations, from which we can obtain very large samples of annual extremes, provide an opportunity to assess whether max-stability holds in a model-simulated climate and to quantify the impact of the lack of max-stability on very long period return-level estimates. We use a recent large ensemble simulation of the North American climate for this purpose. We find that the annual maxima of short-duration precipitation extremes tend not to be max-stable in the simulated climate, as indicated by systematic variation in the estimated shape parameter as block length is increased from 1 to 20 years. We explore how the lack of max-stability affects the estimation of very long period return levels and discuss reasons why short-duration precipitation extremes may not be max-stable.

What Drives the Declining Wealth Effect of Subsequent Share Repurchase Announcements?

Recent academic studies document that open market share repurchase announcements in the United States generate significantly lower returns than those reported in earlier studies. We find that the lower announcement return is associated with an increasing number of subsequent announcements in the more recent periods. Although the announcement period return from the initial announcement is positive, subsequent announcement returns are significantly decreasing. Further, we find that the decreasing returns of subsequent announcements are attributed to firms with negative past repurchase announcement returns. Our multivariate regression test results are consistent with the notion that the decreasing subsequent repurchase announcement returns are driven by hubris-endowed managers.

Evaluation and projection of long period return values of extreme daily precipitation in the CMIP5 and CMIP6 models

<p>Using a non-stationary Generalized Extreme Value statistical method, we calculate selected extreme daily precipitation indices and their 20 year return values from the CMIP5 and CMIP6 climate models over the historical and future periods. We evaluate model performance of these indices and their return values in replicating similar quantities calculated from multiple gridded observational products. Difficulties in interpreting model quality in the context of observational uncertainties are discussed. Projections are framed in terms of specified global warming target temperatures rather than at specific times and under specific emissions scenarios. The change in framing shifts projection uncertainty due to differences in model climate sensitivity from the values of the projections to the timing of the global warming target. At their standard resolutions, we find there are no meaningful differences between the two generations of models in their quality or projections of simulated extreme daily precipitation.</p>