Title: Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules
Authors: Macedo, Luís Lobato 
Godinho, Pedro 
Alves, Maria João 
Keywords: Multiobjective optimization;Evolutionary algorithms;Stock portfolio;Mean-semivariance;Technical analysis
Issue Date: 15-Aug-2017
Publisher: Elsevier
Abstract: 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.
URI: http://hdl.handle.net/10316/45579
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2017.02.033
Rights: embargoedAccess
Appears in Collections:I&D CeBER - Artigos em Revistas Internacionais

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