Multiobjective approach to portfolio optimization in the light of the credibility theory
Abstract
The present research proposes a novel methodology to solve the problems faced by investors who take into consideration different investment criteria in a fuzzy context. The approach extends the stochastic mean-variance model to a fuzzy multiobjective model where liquidity is considered to quantify portfolio’s performance, apart from the usual metrics like return and risk. The uncertainty of the future returns and the future liquidity of the potential assets are modelled employing trapezoidal fuzzy numbers. The decision process of the proposed approach considers that portfolio selection is a multidimensional issue and also some realistic constraints applied by investors. Particularly, this approach optimizes the expected return, the risk and the expected liquidity of the portfolio, considering bound constraints and cardinality restrictions. As a result, an optimization problem for the constraint portfolio appears, which is solved by means of the NSGA-II algorithm. This study defines the credibilistic Sortino ratio and the credibilistic STARR ratio for selecting the optimal portfolio. An empirical study on the S&P100 index is included to show the performance of the model in practical applications. The results obtained demonstrate that the novel approach can beat the index in terms of return and risk in the analyzed period, from 2008 until 2018.
First published online 8 October 2020
Keyword : evolutionary multiobjective optimization, fuzzy portfolio selection, mean-CVaR-liquidity, mean-semivariance-liquidity, trapezoidal fuzzy numbers, NSGA-II, credibilistic sortino ratio, credibilistic STARR ratio
How to Cite
Garcia, F., González-Bueno, J., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2020). Multiobjective approach to portfolio optimization in the light of the credibility theory. Technological and Economic Development of Economy, 26(6), 1165-1186. https://doi.org/10.3846/tede.2020.13189
This work is licensed under a Creative Commons Attribution 4.0 International License.
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García, F., González-Bueno, J., Guijarro, F., & Oliver, J. (2020). Forecasting the environmental, social, and governance rating of firms by using corporate financial performance variables: A rough set approach. Sustainability, 12(8), 3324. https://doi.org/10.3390/su12083324
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García, F., Guijarro, F., & Oliver, J. (2018a). Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics. Neural Computing and Applications, 30, 2625–2641. https://doi.org/10.1007/s00521-017-2882-2
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