一个多因素的动态时间序列测量,用于股票相关性分析
PloS one
|December 15, 2025
概括
本研究引入了一种新的多因素动态时间相似度测量 (MFDTSM),通过考虑多维数据和时间延迟效应来改进库存相关性分析. 这种新的方法提高了行业,线性和价格相关性的准确性.
科学领域:
- 量化金融 量化金融
- 计算经济学的计算经济学
- 数据科学数据科学数据科学
背景情况:
- 传统的股票相关性分析往往无法捕捉到股票数据的多维性和动态时间滞后效应 (TLE).
- 现有的相似度指标缺乏足够的复杂性来解决影响股票行为随时间推移的因素的复杂相互作用.
研究的目的:
- 为更准确的股票相关性分析提出一种新的多因素动态时间相似度测量 (MFDTSM).
- 解决现有方法在处理多维库存数据和TLE在相位差异方面的局限性.
主要方法:
- 开发了一种增强的极端梯度增强 (XGBoost) 模型,与Shapley添加式扩展 (SHAP) 集成,以评估股票因素的影响.
- 使用SHAP值的聚类来进行库存分类和因子异质性的分析.
- 使用累积距离矩阵和最佳时间序列对齐路径量化TLE相差.
主要成果:
- 与现有方法相比,MFDTSM方法在行业相关性 (10%),线性相关性 (16%),和股票相关性定价 (5%) 中表现出更好的准确性.
- 有效地对股票进行了分类,并揭示了因素影响的异质性.
- 成功量化了TLE的动态相位差异,提高了相似度测量的准确性.
结论:
- 通过结合多维数据和TLE,MFDTSM在分析复杂的股票市场动态方面取得了重大进展.
- 该方法被证明是高效和稳定的,在各种相关性分析中表现优于现有技术.
- 强调考虑动态时间方面和因素相互作用的重要性,以获得可靠的股票市场见解.
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