一种集成的TOPSIS和ARAS方法的多标准决策方法,用于优化投资组合,使用目标编程和遗传算法模型
Prajwal Pisal1, Kiran Kumar Reddy2, Jaydeep Kishore3
1Department of Computer Science, California State University (Alumni), Monterey Bay, Seaside, CA, 93955, USA.
Scientific reports
|October 2, 2025
概括
本研究介绍了一种混合组合优化方法,该方法结合了TOPSIS,ARAS,GP和GA. 这种新的方法提高了投资配置准确性和投资者决策建模,以提高财务绩效.
科学领域:
- 量化金融 量化金融
- 计算经济学 计算经济学
- 运营研究 运营研究
背景情况:
- 经典的投资组合优化与复杂的投资者决策和风险耐受性整合作斗争.
- 现有的方法在建模投资者行为,资产属性和风险之间的相互作用方面缺乏效率.
- 准确的投资组合构建需要先进的技术来处理多标准决策和概率元素.
研究的目的:
- 为增强投资组合优化开发一种创新的混合方法.
- 将多标准决策技术与优化算法相结合,以提高准确性.
- 创建一个灵活和计算有效的系统,用于现实的投资建模.
主要方法:
- 一种混合方法,将订单偏好技术与理想解决方案相似 (TOPSIS) 和附加比率评估 (ARAS) 结合起来,用于多标准决策.
- 整合目标编程 (GP) 以使投资决策与投资者期望保持一致,以及用于意识策略的遗传算法 (GA).
- 使用FAR-Trans数据集进行实证测试,包括资产评估,投资者表征和概率投资组合构建.
主要成果:
- 达到了2.241的夏普比率,年化回报率为4.6%,多元化得分为0.845.
- 在TOPSIS-ARAS排名和GP配置之间显示了0.729的相关性,导致投资组合回报率超过30.0%.
- 该系统在各种交易道,风险因素和地理位置上真实地描绘了投资者的行为.
结论:
- 拟议的混合方法显著提高了投资组合排名的准确性和分配效率.
- 整合TOPSIS-ARAS,GP和GA提供了一个灵活,计算高效和现实的投资建模解决方案.
- 这种方法有效地将约束偏差最小化,同时适应复杂的投资者个人资料和市场动态.
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