Expensive software developed around the traditional notion of optimization would have to be replaced. Critics of the mean-variance paradigm would have to step forward and be counted. Notwithstanding practical realities, optimization has the potential to make investing easier. In his new book, Dr. Richard O. Michaud shows how, emphasizing that optimization is a statistical process and not an exact science. Resampling confronts the overfitting of data that plagues mean-variance optimization and is shown empirically to better reflect investment performance more often than the traditional approach.
Additional insights focus on ways to improve input estimations.
This is especially meaningful given the stated acute sensitivity of optimization outputs to model inputs. Other chapters are dedicated to a discussion of tying optimization to a selected benchmark and enhancing data forecasts, respectively. Two nice features include a review of portfolio theory at the outset, followed by myriad examples that illustrate the limits of the typical mean- variance procedure. What's also nice is the author's conclusion that no perfect solution exists. A better mousetrap is still not infallible.