Lagrangian relaxation procedure for cardinality-constrained portfolio optimization

Authors:
Dong X. Shaw;Shucheng Liu;Leonid Kopman
Affiliations:
Paradigm Asset Management, New York, NY, USA;MSCI Barra, Berkeley, CA, USA;MSCI Barra, Berkeley, CA, USA
Venue:
Optimization Methods & Software
Year:
2008

Citing 5
Cited 8

Integer and combinatorial optimization

Integer and combinatorial optimization
Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market

Management Science
Computational study of a family of mixed-integer quadratic programming problems

Mathematical Programming: Series A and B
Heuristics for cardinality constrained portfolio optimisation

Computers and Operations Research
Algorithm for cardinality-constrained quadratic optimization

Computational Optimization and Applications

A Stochastic-Goal Mixed-Integer Programming approach for integrated stock and bond portfolio optimization

Computers and Industrial Engineering
Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review

Expert Systems with Applications: An International Journal
A Stochastic Mixed-Integer Programming approach to the energy-technology management problem

Computers and Industrial Engineering
A local relaxation method for the cardinality constrained portfolio optimization problem

Computational Optimization and Applications
A polynomial case of the cardinality-constrained quadratic optimization problem

Journal of Global Optimization
Convex relaxations and MIQCQP reformulations for a class of cardinality-constrained portfolio selection problems

Journal of Global Optimization
Construction of Risk-Averse Enhanced Index Funds

INFORMS Journal on Computing
A hybrid algorithm for constrained portfolio selection problems

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper studies a portfolio-selection problem subject to a cardinality constraint, that is, the number of securities in a portfolio is restricted to a certain limit. The problem is formulated as a cardinality-constrained quadratic programming problem, and a dedicated Lagrangian relaxation method is developed. In contrast to many existing Lagrangian relaxation methods, the approach presented in the paper is able to take advantage of the special structure of the objective function rather than the special structure of the constraints. The algorithm developed here has been applied to track the major market indices, such as the S&P 500, S&P 100, FTSE 100, and FTSE 250, using real data, and the computational results are promising.