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Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules
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DOI：10.1016/j.eswa.2017.02.033.png)
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摘要
En 中文
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. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Multiobjective optimization
Evolutionary algorithms
Stock portfolio
Mean-semivariance
Technical analysis
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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引用论文
Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
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分享收藏Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation.png)
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JOURNAL OF FINANCEIF9.5
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