Portfolio optimization with incorporation of preferences and many criteria

Portfolio Optimization is one of the most addressed areas in Operations Research, mainly because of its practical relevance and interesting theoretical challenges. Recently, Solares et al. (2018) have proposed using probabilistic confidence intervals as criteria to select the most convenient portfolio. An approach following this idea allows the investor to consider not only the expected impact of the portfolios but also the risk of not obtaining that expected impact. Moreover, it identifies the behavior of the investor in presence of risk and aiding her/him depending on her/his own preferences.

On the other hand, there are situations where the investor is not satisfied with the knowledge provided by probabilistic information (e.g., such information is precarious or the investor gives importance to other information, such as financial data). In this case, the investor may be interested in considering many criteria in order to select the most convenient portfolio. However, this is not a trivial task since the cognitive limitations make it very difficult for the investor to consistently select the best compromise in presence of many criteria. Bearing this in mind, Fernandez et al. (2018) proposed an approach that aggregates the many criteria on the basis of the investor’s particular system of preferences producing a selective pressure towards the most preferred portfolio while the investor’s cognitive effort in the final selection is reduced.