Portfolio optimization emerged with the seminal paper of Markowitz (1952). The original mean-variance framework is appealing because it is very efficient from a computational point of view. However, it also has one well-established failing since it can lead to portfolios that are not optimal...

This paper incorporates Bayesian estimation and optimization into portfolio selection framework, particularly for high-dimensional portfolio in which the number of assets is larger than the number of observations. We leverage a constrained 𝓁1 minimization approach, called linear programming...

The Bayes-Stein model provides a framework for remedying parameter uncertainty in the Markowitz mean-variance portfolio optimization. The classical version, however, suffers from estimation errors of model components and fails to consistently outperform the naive 1/N asset allocation rule. We...

This chapter discusses computational methods for approximating portfolio and asset pricing problems. Formulation of these problems is usually specified along with components, preferences, payoffs, etc., that are analytic functions. This implies that the solutions to these problems acquire this...

There is a substantial theoretical literature on the location of retail facilities in space assuming that the spatial demand curve is known, or that the ideal data needed to estimate such a curve is available. This study shows how to implement a formal location analysis for a retail activity...

This article investigates the usefulness of combining traditional factors with ESG data when building optimal equity portfolios. Our contribution departs from the traditional literature by focusing on allocations designed to adjust benchmark policies. We allow compositions to be embedded in a...