Active portfolio management from a fuzzy multi-objective programming perspective

Authors:
Nikos S. Thomaidis
Affiliations:
Management and Engineering Laboratory, Department of Financial & Management Engineering, University of the Aegean, Chios, Greece
Venue:
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
Year:
2010

Citing 3
Cited 1

An introduction to genetic algorithms

An introduction to genetic algorithms
Swarm intelligence

Swarm intelligence
Index tracking with constrained portfolios: Research Articles

International Journal of Intelligent Systems in Accounting and Finance Management

Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the problem of structuring a portfolio that outperforms a benchmark index, assuming restrictions on the total number of tradable assets. We experiment with non-standard formulations of active portfolio management, outside the mean-variance framework, incorporating approximate (fuzzy) investment targets and portfolio constraints. To deal with the inherent computational difficulties of cardinality-constrained active allocation problems, we apply three nature-inspired optimisation procedures: simulated annealing, genetic algorithms and particle swarm optimisation. Optimal portfolios derived from these methods are benchmarked against the Dow Jones Industrial Average index and two simpler heuristics for detecting good asset combinations, based on Monte-Carlo simulation and fundamental analysis.