We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., por...

We develop and implement a portfolio optimization method for building investment portfolios that dominate a given benchmark index in terms of third-degree stochastic dominance. Our approach relies on the properties of the semi-variance function, a re nement of an existing `superconvex' dominance condition and quadratic constrained programming. To reduce the computational burden in large-scale a...

This paper considers a multi-objective portfolio selection problem imposed by gaining of portfolio, divided yield and risk control in an ambiguous investment environment, in which the return and risk are characterized by probabilistic numbers. Based on the theory of possibility, a new multi-objective portfolio optimization model with gaining of portfolio, divided yield and risk control is propo...

Conditional Value-at-Risk Constrained Optimization of a Power Portfolio Published in International Journal of Applied Decision Sciences • A predictive and risk analytics tool guiding cash management of warranty reserves, covering contingent liabilities. • Finding a balance between excess reserves causing opportunity cost, and not enough reserves necessitating emergency funds. • The decision mak...

OMID SHAKERNIA is a senior researcher at Research Affiliates, LLC, in Newport Beach, CA. [email protected] Traditional strategic asset allocation theory is deeply rooted in the mean–variance portfolio optimization framework developed by Markowitz [1952] for constructing equity portfolios. However, the mean–variance optimization methodology is diff icult to implement due to the challenges asso...

We consider the dynamic portfolio choice problem in a jump-diffusion model, where an investor may face constraints on her portfolio weights: for instance, no-short-selling constraints. It is a daunting task to use standard numerical methods to solve a constrained portfolio choice problem, especially when there is a large number of state variables. By suitably embedding the constrained problem i...

this paper presents a new meta-heuristic solution to find the efficient frontier using the mean-variance approach. portfolio optimization problem is a quadratic programming model and, changes to np-hard if the number of assets and constraints has increased, and it cannot be solved using common mathematical methods in a reasonable time. therefore, a heuristic or meta-heuristic algorithm should b...

Portfolio management based on mean-variance portfolio optimization is subject to different sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancy between target and present portfolios, caused by trading strategies, may expose investors to unde...