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.
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Thomaidis, N.S. (2010). Active Portfolio Management from a Fuzzy Multi-objective Programming Perspective. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12242-2_23
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DOI: https://doi.org/10.1007/978-3-642-12242-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12241-5
Online ISBN: 978-3-642-12242-2
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