Content area
Full text
(ProQuest: ... denotes formulae omitted.)
Optimal portfolio construction, the process of efficiently allocating wealth among asset classes and securities, has a long-standing history in the academic literature. Over 50 years ago, Markowitz [1952, 1956] formalized the problem in a mean-variance framework where one assumes that the rational investor seeks to maximize the expected return for a given volatility level. While powerful and elegant, this solution is known to suffer from serious drawbacks in its practical implementation. First, optimal portfolios tend to be excessively concentrated in a hmited subset of the full set of assets or securities. Second, the mean-variance solution is overly sensitive to the input parameters. Small changes in those parameters, most notably in expected returns (Merton [1980]), can lead to significant variations in the composition of the portfolio.
Alternative methods to deal with these issues have been suggested in the literature, such as portfolio resampling (Michaud [1989]) or robust asset allocation (Tütüncü and Koenig [2004]), but have their own disadvantages. On top of those shortcomings is the additional computational burden forced upon investors because of the need to compute solutions across a large set of scenarios. Moreover, it can be shown that these approaches can be restated as shrinkage estimator problems ( Jorion [1986]) and that their out-of-sample performance is not superior to traditional approaches (Scherer [2007a, 2007b]). Looking at the marketplace, it also appears that a large fraction of investors prefers more heuristic solutions, which are computationally simple to implement and are presumed robust as they do not depend on expected returns.
Two well-known examples of such techniques are the minimum-variance portfolio and the equally weighted portfolio. The former is a specific portfolio on the mean-variance efficient frontier. Equity funds applying this principle have been launched in recent years. This portfolio is easy to compute because the solution is unique. As the only mean-variance efficient portfolio not incorporating information on the expected returns as a criterion, it is also recognized as robust; however, minimum-variance portfolios generally suffer from the drawback of portfolio concentration. A simple and natural way to resolve this issue is to attribute the same weight to all the assets considered for inclusion in the portfolio. Equally weighted or "1/n" portfolios are widely used in practice (Bernartzi and Thaler [2001]...