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Multi-objective heuristic algorithms for practical portfolio optimization and rebalancing with transaction cost
Published in Elsevier Ltd
Volume: 67
Pages: 865 - 894
Portfolio optimization is the process of allocating capital among a universe of assets to achieve better risk–return trade-off. Due to the dynamic nature of financial markets, the portfolio needs to be rebalanced to retain the desired risk–return characteristics. The process of rebalancing requires buying or selling of assets that incur transaction costs. This study proposes a tri-objective portfolio optimization model with risk, return and transaction cost as the objectives. Various practical constraints like cardinality, self-financing, quantity, pre-assignment and cost related constraints are included in the proposed model. Three popular risk measures namely variance, Value-At-Risk (VaR) and Conditional Value-At-Risk (CVaR) are studied in the proposed work. The emphasis of the study is on handling equality constraints like self-financing constraint and the constraints arising from the inclusion of transaction cost models using multi-objective evolutionary algorithms (MOEAs). A novel repair algorithm is proposed that can effectively handle equality constraints without any requirement of any constraint handling technique. The proposed repair algorithm is suitable for a larger class of separable transaction cost model. The theoretical proof is given to ensure the validity of our claim. To verify the effectiveness of the proposed approach three algorithms from different multi-objective evolutionary frameworks are adapted and compared. In empirical study, we discuss the performances of algorithms over both in-sample and out-sample data. © 2017 Elsevier B.V.
About the journal
JournalData powered by TypesetApplied Soft Computing Journal
PublisherData powered by TypesetElsevier Ltd