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.

Traditional approaches to the study of technical analysis (TA) often focus on the performance of a single indicator, which seems to fall short in scope and depth. We use a genetic algorithm (GA) to optimize trading strategies in the three major Forex markets, in order to verify the adequacy of TA strategies and rules to attain consistent superior returns, by comparing momentum, trend and breakout indicators. The indicators with the parameters generated through our GA consistently outperform the equivalent indicators applying parameters commonly used by the trading industry. EUR/USD and GBP/USD markets present interesting return figures before trading costs. The inclusion of spreads and commissions deteriorates returns substantially, suggesting these markets, under a more realistic set of assumptions, may be efficient. Trend indicators generate better outcomes and GBP/USD qualifies as the most profitable market. Different aggregate returns in different markets may stand as evidence of distinct maturation stages under an evolving efficiency market perspective. Our GA is able to search a wider solution space than traditional configurations and presents the possibility of recovering latent data, avoiding premature convergence.

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