投资组合优化:一种基于尖端神经网络的神经动力学方法
Ameer Hamza Khan1,2, Aquil Mirza Mohammed3, Shuai Li4
1School of Artificial Intelligence (AI), Taizhou University, Taizhou 318000, China.
Biomimetics (Basel, Switzerland)
|December 24, 2025
概括
本研究介绍了一种生物仿真方法,使用尖端神经网络 (SNN) 来优化投资组合. 与传统方法相比,SNN解决方案实现了更高的回报,为高效的实时财务管理铺平了道路.
科学领域:
- 计算金融是指计算金融.
- 生物模拟学是一种生物模拟学.
- 人工智能的人工智能
背景情况:
- 投资组合优化对于平衡金融风险和回报至关重要.
- 结合交易成本和核心值限制,它变成了一个复杂的混合整数二次数程序.
- 传统的方法难以满足这些复杂的优化问题的计算需求.
研究的目的:
- 通过从生物神经系统中汲取灵感,为复杂的投资组合优化问题开发高效的算法.
- 将混合整数二进制编程问题重新构成一个受约束的二进制程序,可以通过增强神经网络动态来解决.
- 为解决计算密集型财务优化任务建立一个仿生途径.
主要方法:
- 重构了投资组合优化作为一个受约束的二进制程序.
- 开发了一个使用灵感来源于漏洞集成和发射尖端神经网络 (SNN) 的动态的解决方案.
- 实现了一个在连续梯度流和离散约束投影之间交替的溶解器.
- 在使用5年的市场数据对5到50个资产的投资组合进行SNN解决方案的评估,与ECOS_BB,OSQP和粒子群优化 (PSO) 进行比较.
主要成果:
- 在50个资产组合中,SNN解决方案实现了最高的每日预期回报率 (0.261%),超过了精确的混合整数二次编程 (MIQP) (0.225%) 和PSO (0.092%).
- 运行时间从小型投资组合的0.5秒到大型高质量计划的8.4秒不等.
- 虽然目前的Python运行时间与现有方法相比,但该方法在专业的神经形态硬件上显示了显著加快速度的潜力.
结论:
- 尖端神经网络动态为解决复杂的投资组合优化问题提供了一个有希望的仿生方法.
- 这种方法通过在神经形态硬件上潜在的部署,为实现实时投资组合再平衡提供了一条途径.
- 该SNN解决方案表明了竞争性表现和未来在金融优化方面的进步的潜力.
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