Green and Hollifield (1992) argue that the presence of a dominant factor would result in extreme negative weights in mean‐variance efficient portfolios even in the absence of estimation errors. In that case, imposing no‐short‐sale constraints should hurt, whereas empirical evidence is often to the contrary. We reconcile this apparent contradiction. We explain why constraining portfolio weights to be nonnegative can reduce the risk in estimated optimal portfolios even when the constraints are wrong. Surprisingly, with no‐short‐sale constraints in place, the sample covariance matrix performs as well as covariance matrix estimates based on factor models, shrinkage estimators, and daily data.

Markowitz’s portfolio theory is one of the most important theoretical developments in finance. Its elegance and theoretical appeal are illustrated by the fact that it is taught in virtually every undergraduate and MBA level course on portfolio management. Yet the Markowitz mean-variance (MV) optimization is not widely used by the investment community. In this book, Richard Michaud, a leading researcher in this area, explains why this is so. Based on his diagnosis of the problem, he advocates a “statistical view” of the MV optimization that leads to new procedures that can reduce or eliminate many practical deficiencies of the MV optimization. Using a simple global asset allocation example throughout the book, he illustrates the problems associated with a naïve implementation of the MV optimization procedure. He then describes how the new procedures can be applied, and shows how they improve the MV optimization results. The bottom line: the “statistical view” and the new procedures offer the potential of substantial improvements in the investment performance of the MV optimization. I agree with this assessment. I find this book a must-read for industry professionals and researchers who are interested in this area.

Bali and Hite (1998) and Dubofsky (1992) propose models in which market microstructure effects play a role in the ex-dividend day price drop anomaly. Bali and Hite suggest that the anomaly is caused solely by price discreteness, while Dubofsky suggests that NYSE Rule 118 is also involved. We test these models by examining cum- to ex-day price drops during the one-eighth, one-sixteenth, and decimal tick size regimes. While the evidence is qualitatively consistent with Dubofsky's predictions, neither model is satisfactory in a quantitative sense. One of our main empirical findings is that no significant decline was evident in the magnitude of the ex-day anomaly after the tick size reduction.

This note corrects an error in the proof of Proposition 2 of “Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraint Helps” that appeared in the Journal of Finance , August 2003.

We find that cash holdings are more valuable for firms disclosing material weaknesses in the Sarbanes–Oxley (SOX) 404 internal control assessments. We estimate that the value spread for firms with weak controls vs. effective controls is about $0.25 for an extra dollar of cash. Our results are not driven by account-level weaknesses but by more severe, company-level weaknesses in internal control over financial reporting (ICFR). Further, the economic consequences of cash resources significantly decrease with the remediation of previously reported material weaknesses. These results suggest that the favorable (precautionary) impact induced by weak ICFR appears to more than offset the adverse (agency) effect entailed by ineffective ICFR. Overall, our results survive alternative variable specifications, sample splits, matched sample analyses, and a variety of controls.