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{{Techreport
 
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|Title=Portfolio Optimization with an Envelope-based Multi-objective Evolutionary Algorithm
 
|Title=Portfolio Optimization with an Envelope-based Multi-objective Evolutionary Algorithm
 
|Year=2007
 
|Year=2007
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|Month=August
 
|Address=76128 Karlsruhe, Germany
 
|Address=76128 Karlsruhe, Germany
 
|Institution=University of Karlsruhe, Institute AIFB
 
|Institution=University of Karlsruhe, Institute AIFB
 
|Number=503
 
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In this paper, we propose to integrate an active set algorithm optimized for
 
In this paper, we propose to integrate an active set algorithm optimized for
 
portfolio selection into a multi-objective evolutionary algorithm (MOEA). The idea is to let the MOEA come up with some convex subsets of the set of all feasible portfolios, solve a critical line algorithm for each subset, and then merge the partial solutions to form the solution of the original non-convex problem. We show that the resulting envelope-based MOEA significantly outperforms existing MOEAs.
 
portfolio selection into a multi-objective evolutionary algorithm (MOEA). The idea is to let the MOEA come up with some convex subsets of the set of all feasible portfolios, solve a critical line algorithm for each subset, and then merge the partial solutions to form the solution of the original non-convex problem. We show that the resulting envelope-based MOEA significantly outperforms existing MOEAs.
 
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|Download=2007_1483_Branke_Portfolio_Optim_1.pdf
 
|Download=2007_1483_Branke_Portfolio_Optim_1.pdf
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|Forschungsgruppe=Effiziente Algorithmen
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|Forschungsgebiet=Multikriterielle Optimierung
 
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{{Forschungsgebiet Auswahl
 
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|Forschungsgebiet=Aktienkursanalyse
 
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Aktuelle Version vom 1. Oktober 2009, 14:41 Uhr


Portfolio Optimization with an Envelope-based Multi-objective Evolutionary Algorithm




Published: 2007 August
Nummer: 503
Institution: University of Karlsruhe, Institute AIFB
Erscheinungsort / Ort: 76128 Karlsruhe, Germany
Archivierungsnummer: 1483

BibTeX



Kurzfassung
The problem of portfolio selection is a standard problem in financial engineering and has received a lot of attention in recent decades. Classical mean-variance portfolio selection aims at simultaneously maximizing the expected return of the portfolio and minimizing portfolio variance. In the case of linear constraints, the problem can be solved efficiently by parametric quadratic programming (i.e., variants of Markowitz' critical line algorithm). However, there are many real-world constraints that lead to a non-convex search space, e.g. cardinality constraints which limit the number of different assets in a portfolio, or minimum buy-in thresholds. As a consequence, the efficient approaches for the convex problem can no longer be applied, and new solutions are needed.

In this paper, we propose to integrate an active set algorithm optimized for portfolio selection into a multi-objective evolutionary algorithm (MOEA). The idea is to let the MOEA come up with some convex subsets of the set of all feasible portfolios, solve a critical line algorithm for each subset, and then merge the partial solutions to form the solution of the original non-convex problem. We show that the resulting envelope-based MOEA significantly outperforms existing MOEAs.

Download: Media:2007_1483_Branke_Portfolio_Optim_1.pdf



Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet

Evolutionäre Algorithmen, Multikriterielle Optimierung, Naturanaloge Algorithmen, Aktienkursanalyse