[Excerpt] This study examines the relation between the level of institutional investor ownership and the magnitude of security price variability at quarterly earnings announcement dates. Prior research consistently documents a negative association between firm size and announcement-date return variability. One explanation for this finding is that as more timely, alternative information becomes available on large firms prior to an announcement date, their security prices become informative, thereby reducing the information content of the earnings announcement. Large firms are closely followed by institutional investors. These investors dedicate substantial resources to information search. Therefore, the link between size and information production may be attributable to the influence of institutional investors on the information production process. Because institutional trades can also affect security prices, however, the precise impact of institutional following on the variability of prices at quarterly earnings dates is not evident.

Reviews the book "Fiscal Planning for Total War," by William Leonard Crum, John F. Fennelly and Lawrence Howard Seltzer.

Reviews the book "Preparing for the CPA Examination," by Robert J. Smith.

We document that CRSP and Thomson contain many voluntarily reported mutual fund portfolios that are not in SEC filings while, additionally, CRSP and Thomson are missing many SEC mandated portfolios available in SEC filings. We document that the voluntary disclosures are likely driven by convenience rather than duplicity. Although mandated portfolios contain securities with more return momentum, we find use of SEC or Thomson data lead to similar empirical findings. CRSP, however, contains inaccurate position information prior to 2008. Our findings have important implications, such as highlighting a 35% increase in observed manager trading by combining data sources.

This paper develops a new approach to change-point modelling that allows the number of change-points in the observed sample to be unknown. The model we develop assumes that regime durations have a Poisson distribution. It approximately nests the two most common approaches: the time-varying parameter (TVP) model with a change-point every period and the change-point model with a small number of regimes. We focus considerable attention on the construction of reasonable hierarchical priors both for regime durations and for the parameters that characterize each regime. A Markov chain Monte Carlo posterior sampler is constructed to estimate a version of our model, which allows for change in conditional means and variances. We show how real-time forecasting can be done in an efficient manner using sequential importance sampling. Our techniques are found to work well in an empirical exercise involving U.S. GDP growth and inflation. Empirical results suggest that the number of change-points is larger than previously estimated in these series and the implied model is similar to a TVP (with stochastic volatility) model.