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

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.

Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm

“…Macedo et al (2017) presented a hybrid evolutionary algorithm for multi-objective stock portfolio optimization with the risk considered as semivariance. In this study, the results of NSGA-II and SPEA were first evaluated in terms of quality and then subjected to a technical analysis [18]. The review of the most contributed researchers in the field of portfolio selection shows that only a few researchers have shown interest in using the robust optimization approach for portfolio selection under uncertainty.…”

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

“…The authors conclude that SPEA2 seems to be better, but all multi-objective approaches perform better than single-objective ones. A mean-semivariance framework, taking into account adverse return variations only, is described in [83]. Two different Genetic Algorithms are compared (NSGAII and SPEA2), embedding the use of technical analysis indicators.…”

Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures

“…Evidence of the robustness of the algorithm is accomplished in out-of-sample testing during both bull and bear market conditions on the FTSE 100. Macedo et al (2017) compare the non-dominated sorting genetic algorithm II (NSGA-II, Deb et al (2002)) and the strength Pareto evolutionary Algorithm 2 (SPEA 2, Zitzler et al (2001)) within the mean-semi-variance portfolio optimization framework. Numerical experiments indicate that NSGA-II outperforms SPEA 2 in-sample.…”