使用随机关联的模糊投资组合选择
Gumsong Jo1, Hyokil Kim1, Hoyong Kim1
1Department of International Finance, Faculty of Finance, Kim Il Sung University, Taesong District, Pyongyang, Democratic People's Republic of Korea.
Computational economics
|June 26, 2023
概括
本研究介绍了一种使用随机相关性 (FPSMSC) 的模糊投资组合选择模型,用于增强投资策略. FPSMSC模型优化了股票选择以获得更高的回报率和更平稳的风险回报变化,优于现有方法.
科学领域:
- 金融 金融 金融 金融 金融
- 计算金融是指计算金融.
- 投资管理 投资管理
背景情况:
- 传统的投资组合选择模型在处理模糊和随机不确定性方面存在局限性.
- 整合模糊逻辑和随机过程对于强大的财务建模至关重要.
研究的目的:
- 提出使用随机相关性 (FPSMSC) 的新型模糊投资组合选择模型.
- 通过考虑基于模糊专业知识的未来股票价格变动来增强投资组合优化.
- 评估FPSMSC模型的性能与现有的投资组合选择方法相比.
主要方法:
- 使用随机相关性 (FPSMSC) 开发了一个模糊的投资组合选择模型.
- 优化的投资权重使用18个标普500股的月度回报数据 (2011年10月至2015年9月).
- 使用培训和样本外数据验证模型性能,比较回报率和风险回报平滑度.
主要成果:
- 与模糊和统计模型相比,FPSMSC模型在各种风险水平上实现了更高的回报.
- 在涉及风险厌恶参数 (λ) 的回报变化中表现出优越的平滑性.
- FPSMSC在0-0.3风险厌恶水平中表现特别强,这表明寻求高回报的投资者的效率.
结论:
- 拟议的FPSMSC模型有效地整合了模糊和随机元素,以改善投资组合选择.
- 对于投资者来说,FPSMSC提供了一个强大的框架,旨在在风险管理下实现高回报.
- 该模型能够预测未来的股票走势,并提供更顺的风险回报概况,使其成为投资管理中的一个有价值的工具.
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