Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitz’s Model with Application to the Caracas Stock Exchange

Duran, Feijoo E. Colomine; Cotta, Carlos; Fernández, Antonio J.

doi:10.1007/978-3-642-00267-0_18

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…Such constraints make the use of the CLA very difficult or impracticable. In this kind of problems, both SPEA 2 and NSGA II have been references in the literature, and they usually present consistently better results than alternative techniques - (Duran, Cotta, & Fernández, 2009), (Mishra, Panda, Meher, & Sahu, 2009), (Metaxiotis & Liagkouras, 2012).…”

Section: Multiobjective Evolutionary Algorithms Applied To Portfolio mentioning

confidence: 99%

Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules

Macedo

Godinho

Alves

2017

Expert Systems with Applications

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Recent work has been devoted to study the use of multiobjective evolutionary algorithms (MOEAs) in stock portfolio optimization, within a common mean-variance framework. This article proposes the use of a more appropriate framework, mean-semivariance framework, which takes into account only adverse return variations instead of overall variations. It also proposes the use and comparison of established Technical Analysis (TA) indicators in pursuing better outcomes within the risk-return relation. Results show there is some difference in the performance of the two selected MOEAs-Non-dominated Sorting Genetic Algorithm II (NSGA II) and Strength Pareto Evolutionary Algorithm 2 (SPEA 2)within portfolio optimization. In addition, when used with four TA based strategies-Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), Contrarian Bollinger Bands (CBB) and Bollinger Bands (BB), the two selected MOEAs achieve solutions with interesting in-sample and out-of-sample outcomes for the BB strategy.

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Section: Multiobjective Evolutionary Algorithms Applied To Portfolio mentioning

confidence: 99%

Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules

Macedo

Godinho

Alves

2017

Expert Systems with Applications

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“…Also, evolutionary algorithms can easily be used with various risk measures because, contrary to methods such as linear or quadratic programming, they do not rely heavily on specific properties of the evaluation function. Apart from optimization of portfolio parameters [11], evolutionary portfolio optimization has been used in the literature for other problems such as tracking a stock market index [2] or replicating the behaviour of an investment fund [17].…”

Section: Introductionmentioning

confidence: 99%

Selecting Best Investment Opportunities from Stock Portfolios Optimized by a Multiobjective Evolutionary Algorithm

Michalak

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Multiobjective optimization of portfolios aims at finding sets of stocks which are expected to provide a possibly high return while retaining a moderate level of risk. The Pareto front of portfolios generated by the optimization algorithm represents attainable trade-offs between returns obtained by the portfolios and the level of risk involved in the investment. This paper studies the relationship between location of portfolios in the Pareto front and future returns of these portfolios. It is observed that the highest future returns can be obtained for the portfolios with the highest return and risk measures observed in the past but also for those with the lowest return and risk in the Pareto front. Neither constantly selecting portfolios with high return on historical data nor, conversely, those with low historical risk (but also low return) yields high future returns. Based on these observations a method is proposed for adaptively selecting the best portfolios for investment from solutions contained in the Pareto front. The proposed method selects high-return (but also high-risk) portfolios or low-risk (but also low-return) portfolios based on the behaviour of the stock marked index in the time period preceding the moment of investment. The proposed method outperforms both the strategy of always selecting the portfolios with the highest return from the past and the risk-averse strategy of selecting portfolios with low risk measure.

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“…The results were shown that MOCeLL outperforms the other algorithms. Duran et al (2009) performed a comparison of different evolutionary multiobjective approaches, namely NSGA-II, SPEA2, and indicator-based evolutionary algorithm (IBEA) (Zitzler and Kunzli, 2004). They have used a binary solution representation for all algorithms.…”

Section: The Classical Mean-risk Portfolio Problemmentioning

confidence: 99%

Financial portfolio optimization using multiobjective evolutionary algorithms

Mamanis¹,

Μαμάνης²

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This thesis focuses particularly on financial portfolio optimization problems using multiobjective evolutionary algorithms. The thesis is organized in eight chapters: seven chapters consisting of the main results of our research and a final chapter named Epilogue which contains a summary of the main results of this work, and outlines some ideas and further directions for future research. Each chapter of this thesis has been extracted from autonomous research papers.Chapter 1 provides an overview of the main portfolio selection models with complex constraints and/or multiple objectives and surveys the literature on the use of multiobjective metaheuristics (MOMHs) applied in complex portfolio optimization problems.Chapter 2 investigates the ability and compares the effectiveness of state-of-the-art multiobjective evolutionary algorithms (MOEAs), to solve complex portfolio optimization problems.Chapter 3 considers a mean-variance portfolio optimization model extended to include class, cardinality and quantity constraints. This chapter proposes a novel, generalized form of class constraints OUTLINE OF THE THESIS xii that both limit the proportion of the portfolio invested in certain classes and impose lower and upper bounds on the number of classes that the portfolio is being -exposed‖. Three of the main MOEAs with a problem specific data structure for chromosome representation and specialized variation operators are implemented.Chapter 4 compares the effectiveness of state-of-the-art MOEAs together with a steady-state evolutionary algorithm on the mean-variance cardinality constrained portfolio optimization problem (MVCCPO).Chapter 5 solves different constrained mean-risk portfolio optimization models using a recently developed simulated annealing-based multiobjective optimization algorithm.Chapter 6 formulates the portfolio selection as a tri-objective optimization problem so as to find tradeoffs between risk, return and the number of securities in the portfolio. Furthermore, quantity and class constraints are introduced into the model in order to limit the proportion of the portfolio invested in assets with common characteristics and to avoid very small holdings. The problem is solved using state-of-the-art multiobjective evolutionary algorithms.Chapter 7 considers a large-scale three-objective mean-variance-skewness portfolio selection model with additional real-world constraints which limit the number of assets in the portfolio and the amount invested in these assets. The problem is solved using the best multiobjective evolutionary algorithm, namely SPEA2.

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Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitz’s Model with Application to the Caracas Stock Exchange

Cited by 8 publications

References 27 publications

Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules

Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules

Selecting Best Investment Opportunities from Stock Portfolios Optimized by a Multiobjective Evolutionary Algorithm

Financial portfolio optimization using multiobjective evolutionary algorithms

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