一个适应的黑寡妇优化算法用于金融投资组合优化问题与卡丁数和预算约束.
Rahenda Khodier1, Ahmed Radi1, Basel Ayman1
1Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology, Alexandria, 21934, Egypt.
Scientific reports
|September 28, 2024
概括
本研究介绍了黑寡妇投资组合优化算法 (BWAPO),以解决金融投资组合优化问题 (FPOP). 这种新的方法有效地平衡了风险和回报,特别是在不受约束的场景中表现出色,并提供具有核心限制的竞争性结果.
科学领域:
- 量化金融 量化金融
- 计算金融是指计算金融.
- 运营研究 运营研究
背景情况:
- 金融投资组合优化问题 (FPOP) 对于平衡投资风险和回报至关重要.
- 现有的方法,比如马尔科维茨的平均变量模型,在处理复杂约束方面存在局限性.
- 对于先进的投资组合优化,越来越多地探索了元启发式方法.
研究的目的:
- 引入一种新的元启发式算法,即黑寡妇投资组合优化算法 (BWAPO),用于解决FPOP.
- 适应BWAPO具有特定特征,如交配吸引力和差异进化突变,以提高性能.
- 评估BWAPO在不受约束的,平等的枢纽性受约束的和不平等的枢纽性受约束的FPOP版本中的有效性.
主要方法:
- 开发一个新的黑寡妇算法用于投资组合优化 (BWAPO).
- 在BWAPO中整合交配吸引力和差异进化突变策略.
- 对BWAPO与对基准数据集的现有元启发式方法进行比较分析.
主要成果:
- BWAPO表现出高效率,特别是在不受约束的平均差异投资组合优化方面.
- 该算法在受卡丁度限制的FPOP中实现了竞争性结果,特别是在较小的数据集上.
- 分析表明,与不平等的限制相比,不平等的核心限制会产生更广泛的可行解决方案和潜在的更高的回报.
结论:
- 拟议的BWAPO是解决各种金融投资组合优化问题的有效元启发式.
- 不平等的枢纽制约似乎更有利于最大化投资组合回报.
- 该研究提供了一个全面的数学模型,结合了现实世界的财务约束.
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