Abstract and Keywords
This article addresses the basic question of whether optimization helps or hurts in the construction of equity portfolios, which helps in determining whether one should optimize or not optimize when building equity portfolios. Markowitz's mean-variance optimization (MVO) is essentially the formulation of an optimization problem that resolves the issue of optimal tradeoff of risk and return by modeling expected returns as a linear function in the weights of the portfolio, and risk, or variance, as a quadratic function of those weights. The MVO model contains two critical parameters that need to be estimated which include expected returns, which are used to represent the forecasted return of the portfolio, and the covariance of returns that is used to compute the risk of the portfolio. The passive portfolio construction involves tracing a benchmark as closely as possible, so as to provide investors with returns, which are equivalent, if not equal, to the return of the benchmark. The easiest way to track a benchmark is to hold all of the securities in the benchmark with their corresponding weights. The objective in a passive strategy is to minimize the predicted tracking error to the benchmark, so an optimized portfolio will tend to have a lower predicted tracking error than a portfolio that is produced heuristically.
Access to the complete content on Oxford Handbooks Online requires a subscription or purchase. Public users are able to search the site and view the abstracts and keywords for each book and chapter without a subscription.
If you have purchased a print title that contains an access token, please see the token for information about how to register your code.