Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/53022
Título: | Portfolio implementation risk management using evolutionary multiobjective optimization |
Autor(es): | Quintana, David Denysiuk, Roman Garcia-Rodriguez, Sandra Gaspar-Cunha, A. |
Palavras-chave: | robustness multi-objective optimization evolutionary computation portfolio optimization |
Data: | 2017 |
Editora: | MDPI AG |
Revista: | Applied Sciences |
Resumo(s): | Portfoliomanagementbasedonmean-varianceportfoliooptimizationissubjecttodifferent sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancybetweentargetandpresentportfolios,causedbytradingstrategies,mayexposeinvestors to undesired risks. This study proposes an evolutionary multiobjective optimization algorithm aiming at regions with solutions more tolerant to these deviations and, therefore, more reliable. The proposed approach incorporates a user’s preference and seeks a fine-grained approximation of the most relevant efficient region. The computational experiments performed in this study are based on a cardinality-constrained problem with investment limits for eight broad-category indexes and 15 years of data. The obtained results show the ability of the proposed approach to address the robustness issue and to support decision making by providing a preferred part of the efficient set. The results reveal that the obtained solutions also exhibit a higher tolerance to prediction errors in asset returns and variance–covariance matrix. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/53022 |
DOI: | 10.3390/app7101079 |
ISSN: | 2076-3417 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | IPC - Artigos em revistas científicas internacionais com arbitragem |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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applsci-07-01079.pdf | 473,29 kB | Adobe PDF | Ver/Abrir |