利用增强的egret swarm优化算法和人工智能驱动的提示策略来进行投资组合选择
Zhendai Huang1, Zhen Zhang2, Cheng Hua1
1College of Computer Science and Engineering, Jishou University, Jishou, 416000, China.
Scientific reports
|November 4, 2024
概括
本研究介绍了在股票选择中用于生成AI的结构化提示框架,提高了投资者的解释性. 一个新的算法,NBESOA,在严格的约束下优化投资组合以获得更高的夏普比率.
科学领域:
- 量化金融 量化金融
- 金融领域的人工智能
- 投资组合优化 投资组合优化
背景情况:
- 高效的投资组合构建在金融中至关重要,涉及资产选择和配置.
- 生成型人工智能 (AI) 和大型语言模型 (LLM) 提供了先进的功能,但往往缺乏解释性.
- 现有的AI工具需要代微调,阻碍投资者直接应用.
研究的目的:
- 为生成性AI开发一个结构化的提示框架,以实现直接和可解释的股票选择.
- 提出一个新的优化算法,用于与交易成本和枢纽性约束 (MVPS-TCCC) 的平均变量投资组合选择问题.
- 用真实的股票市场数据评估AI推的投资组合的表现.
主要方法:
- 创建代表性场景和实验案例,以测试快速框架对AI反应的影响.
- 开发非线性激活甲虫天线搜索策略与虫群优化算法 (NBESOA) 结合使用.
- 应用NBESOA以使用真实股票市场数据和人工智能生成的建议来解决MVPS-TCCC问题.
主要成果:
- 结构化提示框架提高了用于股票选择的生成AI输出的可解释性.
- 与其他算法相比,NBESOA在优化投资组合配置方面表现优越.
- 在严格的交易成本和枢纽性约束下,NBESOA实现了更高的夏普比率,接近有效边界.
结论:
- 提议的快速框架提供了一种实际方法,用于利用生成AI进行可解释的库存选择.
- NBESOA为解决复杂的投资组合优化问题提供了一种有效的方法.
- 这项研究弥合了先进的人工智能能力和实际投资决策之间的差距.
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