使用登和简单的人类学习优化算法作为决策支持系统的股票组合优化.
Suyash S Satpute1, Amol C Adamuthe2, Pooja Bagane3
1Department of CSE, Kasegaon Education Society's Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale, MS 415414, India.
MethodsX
|June 30, 2025
概括
这项研究开发了一种使用混合算法的股票投资组合优化系统. 基本上低估的投资组合表现明显优于增长组合和美国市场指数.
科学领域:
- 量化金融 量化金融
- 计算金融是指计算金融.
- 投资管理 投资管理
背景情况:
- 股票投资组合优化旨在最大限度地提高回报,同时最大限度地降低风险.
- 传统方法可能无法完全捕捉复杂的市场动态.
- 整合基本面分析为股票选择提供了一个强大的方法.
研究的目的:
- 为股票投资组合优化开发一个决策支持系统 (DSS).
- 通过基本分析模块将自然启发的算法 (山登和SHLO) 混合.
- 对各种风险概况和市场指数进行优化投资组合的绩效评估.
主要方法:
- 开发了一个DSS集成内在价值和金融健康分析模块.
- 使用历史基本库存数据设计定制数据集.
- 采用了新的健身功能与登和SHLO算法进行优化.
主要成果:
- 优化的投资组合显示,随着风险承受能力的增加,回报率从55%下降到24%.
- 增加投资组合核心价值导致回报率下降.
- 与增长投资组合相比,基本上被低估的投资组合表现出更好的表现.
结论:
- 开发的DSS有效优化基于基本面分析的股票投资组合.
- 优化投资组合的表现始终优于美国市场指数 (>80%的时间).
- 混合自然启发的算法可以提高投资组合选择的准确性和风险管理.
关键词:
决策支持系统 (DSS) 是一个决策支持系统.金融健康分析 金融健康分析登山算法 登山算法 登山算法股票的内在价值 股票的内在价值简单的人类学习优化算法股票投资组合优化 股票投资组合优化使用登和简单的人类学习优化算法作为决策支持系统的股票组合优化.更多相关视频
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