Abstract:
In recent years portfolio optimization models that consider more criteria than the standard
expected return and variance objectives of the Markowitz model have become popular. For
such models, two approaches to find a suitable portfolio for an individual investor are possible.
In the multiattribute utility theory (MAUT) approach a utility function is constructed based
on the investor’s preferences and an optimization problem is solved to find a portfolio that
maximizes the utility function. In the multiobjective programming (MOP) approach a set of
efficient portfolios is computed by optimizing a scalarized objective function. The investor
then chooses a portfolio from the efficient set. We outline these two approaches using the
UTADIS method to construct a utility function and present numerical results for an example.
