This paper introduces a new approach to constructing optimal mean-variance portfolios. The approach relies on a novel unconstrained regression representation of the mean-variance optimization problem combined with high-dimensional sparse-regression methods. Our estimated portfolio, under a mild sparsity assumption, controls for risk and attains the maximum expected return as both the numbers of assets and observations grow. The superior properties of our approach are demonstrated through comprehensive simulation and empirical analysis. Notably, using our strategy, we find that investing in individual stocks, in addition to the Fama-French three-factor portfolios, leads to substantially improved performance. Received October 6, 2014; editorial decision July 13, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

This study evaluates the link between CEO governance heterogeneity, power structure of the firm, and product market competition on various facets of post-IPO cash policy. Our results suggest that post-IPO cash holdings as well as marginal value of cash reserves are higher under a founder CEO governance regime relative to non-founder CEOs. Concentrating board power in the hands of founder CEOs however, reduces their ability to maintain higher post-IPO cash reserves. Our results also suggest that product market competition influences both the level and marginal value of cash reserves in the hands of founder CEOs. Further, we find that stronger internal governance reduces the tendency of IPO firms to deploy excess cash reserves to fund internal investments in excess of industry rivals. Finally, our results suggest that excess cash reserves in competitive industry environments lead to superior post-IPO operating performance.

We present a novel methodology for the out-of-sample forecast of the equity premium. Our predictive slope coefficient is a conservative constant that has a lower bias than the zero slope employed by the historical average, but has the same variance. We demonstrate that, theoretically and empirically, our method dominates the historical average in forecast performance. Our methodology establishes a simple yet powerful paradigm for exploiting the real-time equity premium predictability derived from a predictor. Applications of our method reveal that many predictors can forecast the equity premium, and that parameter estimates in previous studies add value to out-of-sample forecasts.