Modelo Multiobjetivo para Seleção de Portfólios com Restrição de Cardinalidade, Custo de Transação e Valor em Risco Condicional
DOI:
https://doi.org/10.5540/tema.2016.017.03.0353Keywords:
Multiobjective Optimization, Portfolio Selection, CVaR, Multiobjective Genetic Algorithms.Abstract
Este trabalho apresenta um modelo multiobjetivo para seleção de portfólios de ações do mercado financeiro, que leva em consideração a restrição de cardinalidade, os custos de transação e os limites de investimento para cada ativo e para grupos de ativos. As funções-objetivo consideram o valor em risco condicional (CVAR – Conditional Value-at-Risk) como medida de risco e o valor esperado dos retornos históricos ponderados pelas proporções de investimento, descontados os custos de transação. Para a otimização do modelo foi utilizado um algoritmo genético multiobjetivo. Resultados mostram a capacidade do algoritmo em encontrar várias soluções eficientes, bem como a capacidade do modelo em auxiliar a tomada de decisão na escolha de portfólios que apresentem uma boa relação entre risco e retorno, para uma dada cardinalidade.
References
G.J. Alexander & A.M. Baptista. A comparison of var and cvar constraints on portfolio selection with the mean-variance model. Management Science, 50(9) (2004), 1261–1273.
K.P. Anagnostopoulos & G. Mamanis. A portfolio optimization model with three objectives and discrete variables. Computers & Operations Research, 37(7) (2010), 1285–1297.
L.P. Bloomberg. “Historical Stock Price for Ibovespa (data)”, 1/10/13 a 30/12/13. Retirado em 15/12/15, Bloomberg Database.
Índice Bovespa, Abril 2016, disponível em <http://www.bmfbovespa.com.br/pt br/produtos/indices/ indices-amplos/indice-ibovespa-ibovespa-composicao-da-carteira.htm>. Acesso em: 1 de Abril de 2016.
J. Branke. Consideration of partial user preferences in evolutionary multiobjective optimization, em “Multiobjective optimization”, pp. 157–178, Springer Berlin, Heidelberg (2008).
T-J Chang, N. Meade, J.E. Beasley & Y.M. Sharaiha. Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Research, 27(13) (2000), 1271–1302.
S.C. Chiam, K.C. Tan & A. Al Mamum. Evolutionary multi-objective portfolio optimization in practical context. International Journal of Automation and Computing, 5(1) (2008), 67–80.
K. Deb. Multi-objective optimization using evolutionary algorithms. John Wiley & Sons (2001).
R.M. Escudero, R.R. Torrubiano & A. Sua ́rez. Selection of optimal investment portfolios with cardinality constraints, em “IEEE Congress on Evolutionary Computation”, pp. 2382–2388, Vancouver, Canada ́ (2006).
P. Krokhmal, J. Palmquist & S. Uryasev. Portfolio optimization with conditional value-at-risk objective and constraints. Journal of Risk, 4 (2002), 43–68.
H. Markowitz. Portfolio selection. Journal of Finance, 7 (1952), 77–91.
N.J. Radcliffe. Genetic set recombination. Foundations of Genetic Algorithms, 2 (1992), 203–220.
R.T. Rockafellar & S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2 (2000), 21–42.
R.T. Rockafellar & S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking & Finance, 26(7) (2002), 1443–1471.
D. Roman, K. Darby-Dowman & G. Mitra. Mean-risk models using two risk measures: a multi- objective approach. Quantitative Finance, 7(4) (2007), 443–458.
B. Sawik. Downside risk approach for multi-objective portfolio optimization, em “Operations Rese- arch Proceedings”, Springer Berlin Heidelberg, (2012), 191–196.
H. Soleimani, H.R. Golmakani & M.H. Salimi. Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm. Expert Systems with Applications, 36(3) (2009), 5058–5063.
R.R. Torrubiano & A. Sua ́rez. A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs. Applied Soft Computing, 36 (2015), 125–142.
E. Zitzler & S. Ku ̈nzli. Indicator-based selection in multiobjective search, em “International Confe- rence on Parallel Problem Solving from Nature”, Springer, (2004), 832–842.
E. Zitzler, M. Laumanns & L. Thiele et al. Spea2: Improving the strength pareto evolutionary algo- rithm. Eurogen, 3242(103) (2001), 95–100.
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