用基本面,技术和基于的策略预测股票回报的人工智能模型:一个语义增强的混合方法
Gil Cohen1, Avishay Aiche1, Ron Eichel1
1School of Management, Western Galilee Academic College, Acre 2412101, Israel.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
本研究探讨了将大型语言模型 (LLM) 与机器学习 (ML) 结合起来,用于NASDAQ-100股票预测. 专业的LLM增强了基础分析,而ML则在技术策略方面表现出色,显示定制的AI融合提高了投资组合的绩效.
科学领域:
- 量化金融 量化金融
- 金融领域的人工智能
- 计算经济学计算经济学
背景情况:
- 传统的机器学习 (ML) 模型在捕捉细微的市场情绪方面存在局限性.
- 大型语言模型 (LLM) 提供先进的语义理解,可能改善财务预测.
- 预测性投资组合策略需要整合各种数据源和分析方法.
研究的目的:
- 评估将LLM衍生的语义智能与传统的ML算法相结合的协同效应.
- 开发和测试纳斯达克100股的新型预测投资组合策略.
- 在不同的预测框架中确定ML和LLM洞察力的最佳融合方法.
主要方法:
- 使用了三个预测框架:基础,技术和基于的.
- 集成的ML算法与从LLM (例如,ChatGPT-4o) 衍生的语义指标.
- 分析了2020-2025年NASDAQ-100股票数据,每月进行再平衡.
主要成果:
- 技术方法在单独使用ML预测方面表现最好,累计回报率为1978%.
- 基本方法在主要使用LLM衍生的语义见解时显示出最大的潜力.
- 随着ML和LLM信号的平衡混合,透方法得到了改进,证明了LLM的上下文价值.
结论:
- 在预测投资组合策略中,ML和LLM的最佳结合取决于方法.
- 法律法规为复杂的市场互动提供解释性背景,增强预测能力.
- 根据数据性质和投资视野调整语义-算法融合对于有效的投资组合管理至关重要.
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