We study the distributionally robust stable tail adjusted return ratio (DRSTARR) portfolio optimization problem, in which the objective is to maximize the STARR performance measure under data-driven Wasserstein ambiguity. We consider two types of imperfectly known uncertainties, named uncertain...

We investigate a class of fractional distributionally robust optimization problems with uncertain probabilities. They consist in the maximization of ambiguous fractional functions representing reward-risk ratios and have a semi-infinite programming epigraphic formulation. We derive a new fully...

We study distributionally robust chance-constrained programming (DRCCP) optimization problems with data-driven Wasserstein ambiguity sets. The proposed algorithmic and reformulation framework applies to distributionally robust optimization problems subjected to individual as well as joint chance...

We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each period, the risk-aversion coefficient is adjusted automatically according to market trend movements predicted by...

We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each period, the risk-aversion coefficient is adjusted automatically according to market trend movements predicted by...

Moser et al. (2009) provide one formalization of heresthetics – the “art of political strategy” – in collective choice settings. In doing so they introduce the heresthetically stable set as the set of outcomes least susceptible to manipulation of issue dimension. In this paper we examine...