A multiobjective credibilistic portfolio selection model. Empirical study in the Latin American Integrated Market

[EN] This paper extends the stochastic mean-semivariance model to a fuzzy multiobjective model, where apart from return and risk, also liquidity is considered to measure the performance of a portfolio. Uncertainty of future return and liquidity of each asset are modeled using L-R type fuzzy numbers that belong to the power reference function family. The decision process of this novel approach takes into account not only the multidimensional nature of the portfolio selection problem but also realistic constraints by investors. Particularly, it optimizes the expected return, the semivariance and the expected liquidity of a given portfolio, considering cardinality constraint and upper and lower bound constraints. The constrained portfolio optimization problem resulting is solved using the algorithm NSGA-II. As a novelty, in order to select the optimal portfolio, this study defines the credibilistic Sortino ratio as the ratio between the credibilistic risk premium and the credibilistic semivariance. An empirical study is included to show the effectiveness and efficiency of the model in practical applications using a data set of assets from the Latin American Integrated Market.García García, F.; Gonzalez-Bueno, J.; Guijarro, F.; Oliver-Muncharaz, J. (2020). A multiobjective credibilistic portfolio selection model. Empirical study in the Latin American Integrated Market. Enterpreneurship and Sustainability Issues. 8(2):1027-1046. https://doi.org/10.9770/jesi.2020.8.2(62)S102710468

What is the cost of maximizing ESG performance in the portfolio selection strategy? The case of The Dow Jones Index average stocks

[EN] Portfolio selection is one of the main financial topics. The original portfolio selection problem dealt with the trade-off between return and risk, measured as the mean returns and the variance, respectively. For investors more variables other than return and risk are considered to select the stocks to be included in the portfolio. Nowadays, many investors include corporate social responsibility as one eligibility criterion. Additionally, other return and risk measures are being employed. All of this, together with further constraints such as portfolio cardinality, which mirror real-world demands by investors, have made the multicriteria portfolio selection problem to be NP-hard. To solve this problem, heuristics such as the non-dominated sorting genetic algorithm II have been developed. The aim of this paper is to analyse the trade-off between return, risk and corporate social responsibility. To this end, we construct pareto efficient portfolios using a fuzzy multicriteria portfolio selection model with real-world constraints. The model is applied on a set of 28 stocks which are constituents of the Dow Jones Industrial Average stock index. The analysis shows that portfolios scoring higher in corporate social responsibility obtain lower returns. As of the risk, the riskier portfolios are those with extreme (high or low) corporate social responsibility scores. Finally, applying the proposed portfolio selection methodology, it is possible to build investment portfolios that dominate the benchmark. That is, socially responsible portfolios, measured by ESG scores, must not necessarily be penalized in terms of return or risk.García García, F.; Gankova-Ivanova, T.; González-Bueno, J.; Oliver-Muncharaz, J.; Tamosiuniene, R. (2022). What is the cost of maximizing ESG performance in the portfolio selection strategy? The case of The Dow Jones Index average stocks. Enterpreneurship and Sustainability Issues. 9(4):178-192. https://doi.org/10.9770/jesi.2022.9.3(9)1781929

Multiobjective Approach to Portfolio Optimization in the Light of the Credibility Theory

[EN] 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.García García, F.; González-Bueno, J.; Guijarro, F.; Oliver-Muncharaz, J.; Tamosiuniene, R. (2020). Multiobjective Approach to Portfolio Optimization in the Light of the Credibility Theory. Technological and Economic Development of Economy (Online). 26(6):1165-1186. https://doi.org/10.3846/tede.2020.13189S11651186266Acerbi, C., & Tasche, D. (2002). On the coherence of expected shortfall. Journal of Banking & Finance, 26(7), 1487-1503. doi:10.1016/s0378-4266(02)00283-2Ahmed, A., Ali, R., Ejaz, A., & Ahmad, I. (2018). 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Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics. Neural Computing and Applications, 30(8), 2625-2641. doi:10.1007/s00521-017-2882-2García, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2018). HYBRID FUZZY NEURAL NETWORK TO PREDICT PRICE DIRECTION IN THE GERMAN DAX-30 INDEX. Technological and Economic Development of Economy, 24(6), 2161-2178. doi:10.3846/tede.2018.6394Goel, A., Sharma, A., & Mehra, A. (2018). Index tracking and enhanced indexing using mixed conditional value-at-risk. Journal of Computational and Applied Mathematics, 335, 361-380. doi:10.1016/j.cam.2017.12.015González-Bueno, J. (2019). Optimización multiobjetivo para la selección de carteras a la luz de la teoría de la credibilidad. Una aplicación en el mercado integrado latinoamericano. Editorial Universidad Pontificia Bolivariana.Gupta, P., Inuiguchi, M., & Mehlawat, M. K. (2011). 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OPTIMIZACIÓN MULTIOBJETIVO PARA LA SELECCIÓN DE CARTERAS A LA LUZ DE LA TEORÍA DE LA CREDIBILIDAD: UNA APLICACIÓN EN EL MERCADO INTEGRADO LATINOAMERICANO

El presente trabajo de investigación doctoral tiene como fin optimizar carteras multiobjetivo a la luz de la teoría de la credibilidad. Con el fin de cumplir con este propósito, se propone un novedoso modelo difuso de optimización denominado "Modelo Credibilístico Multiobjetivo de Media-Semivarianza-Liquidez para la Selección de Carteras". La incertidumbre de la liquidez y el rendimiento futuro de cada activo se modela por medio de números difusos L-R con funciones de referencia tipo potencia. Con el objetivo de conseguir un modelo más realista se considera la restricción de cardinalidad que limita el número de activos que participan en las carteras y las restricciones de cotas superiores e inferiores que permiten combinaciones de activos que respetan las preferencias del inversor. Con el propósito de seleccionar la cartera óptima, esta investigación define por primera vez el ratio de Sortino en un entorno credibilístico. El problema de optimización multiobjetivo resultante es lineal y convexo, y la introducción de restricciones realistas convierte el modelo de un problema de optimización cuadrática clásica (classical quadratic optimization problem) a un problema de programación cuadrática de enteros mixtos (quadratic mixed-integer problem) que es NP-hard. Para superar este inconveniente se aplica el Non-dominated Sorting Genetic Algorithm (NSGAII), MOEA que ha sido utilizado con éxito en la generación de soluciones eficientes en varios modelos multiobjetivos de selección de carteras. Finalmente, se demuestra la efectividad y eficiencia del modelo en aplicaciones prácticas, asumiendo por primera vez la toma de decisiones de inversión en el Mercado Integrado Latinoamericano (MILA), que integra los mercados bursátiles de Chile, Colombia, México y Perú.The present doctoral dissertation aims to optimize multiobjective portfolio in the light of credibility theory. In order to meet this purpose, a novel fuzzy optimization model called "Multiobjective Credibilistic Mean-Semivariance-Liquidity Portfolio Selection Model" is proposed. The uncertainty of the future return and liquidity of each asset are modeled by means of LR-fuzzy numbers belonging to the power family. In order to make a more realistic model, it is considered the cardinality constraint limiting the number of assets participating in the portfolios, and upper and lower bound constraints allowing assets combinations which respect the investor's wishes. In the interest of selecting the optimal portfolio, this research defines for the first time, the Sortino ratio under a credibilistic environment. The resulting multiobjective optimization problem is linear and convex, and the introduction of realistic constraints into the portfolio optimization problem convert the model from a classical quadratic optimization problem to a quadratic mixed-integer problem (QMIP) that is NP-hard. To overcome this drawback, it is applied the Non-dominated Sorting Genetic Algorithm (NSGAII), MOEA that has been used successfully in the generation of efficient solutions in several multi-objective portfolio selection models. Finally, an empirical study is included to demonstrate the effectiveness and efficiency of the model in practical applications using for the first time a dataset of assets from the Latin American Integrated Market (MILA by its Spanish acronym), which integrates the stock exchange markets of Chile, Colombia, Mexico, and Peru.El present treball d'investigació doctoral té com a finalitat optimitzar carteres multiobjectiu a la llum de la teoria de la credibilitat. Per tal de complir amb aquest propòsit, es proposa un nou model difús d'optimització denominat "Model Credibilístic multiobjectiu de Mitjana-Semivarianza-Liquiditat per a la Selecció de Carteres". La incertesa de la liquiditat i el rendiment futur de cada actiu es modela per mitjà de nombres difusos L-R amb funcions de referència tipus potència. Amb l'objectiu d'aconseguir un model més realista es considera la restricció de cardinalitat que limita el nombre d'actius que participen en les carteres i les restriccions de cotes superiors i inferiors que permeten combinacions d'actius que respecten les preferències de l'inversor. Amb el propòsit de seleccionar la cartera òptima, aquesta investigació defineix per primera vegada la ràtio de Sortino en un entorn credibilístic. El problema d'optimització multiobjectiu resultant és lineal i convex, la introducció de restriccions realistes converteix el model d'un problema d'optimització quadràtica clàssica (classical quadratic optimization problem), a un problema de programació quadràtica d'enters mixtes (quadratic mixed-integer problem) que és NP-hard. Per superar aquest inconvenient s'aplica el Non-dominated Sorting Genetic Algorithm (NSGAII), MOEA que ha estat utilitzat amb èxit en la generació de solucions eficients en diversos models multiobjectiu de selecció de carteres. Finalment, es demostra l'efectivitat i eficiència del model en aplicacions pràctiques, assumint per primera vegada la presa de decisions d'inversió al Mercat Integrat Llatinoamericà (MILA), que integra els mercats borsaris de Xile, Colòmbia, Mèxic i Perú.González Bueno, JA. (2018). OPTIMIZACIÓN MULTIOBJETIVO PARA LA SELECCIÓN DE CARTERAS A LA LUZ DE LA TEORÍA DE LA CREDIBILIDAD: UNA APLICACIÓN EN EL MERCADO INTEGRADO LATINOAMERICANO [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/102362TESI