Explore the vast range of titles available.

Post-1970, share issuance exhibits a strong cross-sectional ability to predict stock returns. This predictive ability is more statistically significant than the individual predictive ability of size, book-to-market, or momentum. Our finding is related to research that finds that long-run returns are associated with share repurchase announcements, seasoned equity offerings, and stock mergers, although our results remain strong even after exclusion of the data used in these studies. We estimate the issuance relation pre-1970 and find no statistically significant predictive ability for most holding periods.

We explain the poor out-of-sample performance of mean-variance optimized portfolios, developing theoretical bias adjustments for estimation risk by asymptotically expanding future returns of portfolios formed with estimated weights. We provide closed-form non-Bayesian adjustments of classical estimates of portfolio mean and standard deviation. The adjustments significantly reduce bias in international equity portfolios, increase economic gains, and are robust to sample size and to nonnormality. Dominant terms grow linearly with the number of assets and decline inversely with the number of past time periods. Under suitable conditions, Sharpe-ratio maximizing tangency portfolios become more diversified. Using these approximation methods it may be possible to assess, before investing, the effect of statistical estimation error on portfolio performance.

The authors explain optimized portfolios' poor out-of-sample performance (to minimize tracking error relative to a given benchmark, while achieving a specified expected excess return) in the presence of estimation error in the underlying asset means and covariances. The theoretical bias adjustments for this estimation risk developed by the authors involves taking mathematical expectations of asymptotically expanded future returns of portfolios formed with estimated weights. They provide closed-form adjustments for estimates of the expectation and standard deviation of the portfolio's excess returns. The adjustments significantly reduce bias in global equity portfolios, reduce the costs of rebalancing portfolios, and are robust to sample size and non-normality. By using these approximation methods before investing, it may be possible to assess the effect of statistical estimation error on tracking-error-optimized portfolio performance.

We explain the poor out-of-sample performance of mean-variance optimized portfolios, developing theoretical bias adjustments for estimation risk by asymptotically expanding future returns of portfolios formed with estimated weights. We provide closed-form non-Bayesian adjustments of classical estimates of portfolio mean and standard deviation. The adjustments significantly reduce bias in international equity portfolios, increase economic gains, and are robust to sample size and to nonnormality. Dominant terms grow linearly with the number of assets and decline inversely with the number of past time periods. Under suitable conditions, Sharpe-ratio maximizing tangency portfolios become more diversified. Using these approximation methods it may be possible to assess, before investing, the effect of statistical estimation error on portfolio performance.

In this dissertation I propose and test a new explanation for the price momentum anomaly: fundamental firm characteristics initially form price momentum portfolios, yet the return pattern is continued through earnings management. I find some evidence of earnings management being used to continue the return pattern of price momentum portfolios, yet not enough to explain the momentum anomaly. Namely, I find that discretionary accruals are positively and significantly predicted by past returns. However, contemporaneous returns are negatively correlated with discretionary accruals. Those price momentum firms that encounter changes in business conditions appear to be successful at misleading the market through earnings management.