Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules |
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| Affiliation: | 1. Revenue & Customs, Deputy Directorate-General for Tax Collection, Lisbon, Portugal;2. CeBER – Center for Business and Economics Research, University of Coimbra, Portugal;3. INESC, Coimbra, Portugal;4. Faculty of Economics, University of Coimbra, Coimbra, Portugal;1. Department of Informatics Engineering, TEI of Crete, 71004 Heraklion, Crete, Greece;2. Department of Business Administration, TEI of Crete, 72100 Agios Nikolaos, Crete, Greece;3. Foundation for Research and Technology-Hellas (FORTH), Institute of Computer Science, 70013 Heraklion, Crete, Greece;1. Institute for Integrated and Intelligent Systems (IIIS), Griffith University, 170 Kessels Rd, Nathan QLD 4111, Australia;2. Department of Mathematical Sciences, Shiraz University of Technology, Shiraz, Iran |
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| 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. |
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