论文标题
高维相关矩阵系数的非参数符号预测
Nonparametric sign prediction of high-dimensional correlation matrix coefficients
论文作者
论文摘要
我们介绍了一种方法来预测哪些相关矩阵系数可能会在未来的高维度中改变其迹象，即当特征数量大于每个功能的样本数量时。相关迹象的稳定性（两乘两个关系）被发现取决于在该制度中受到史德社会凝聚力理论启发的三偏三关系。我们将我们的方法应用于我们和香港股票的历史数据，以说明相关矩阵的结构如何影响其系数符号的稳定性。
We introduce a method to predict which correlation matrix coefficients are likely to change their signs in the future in the high-dimensional regime, i.e. when the number of features is larger than the number of samples per feature. The stability of correlation signs, two-by-two relationships, is found to depend on three-by-three relationships inspired by Heider social cohesion theory in this regime. We apply our method to US and Hong Kong equities historical data to illustrate how the structure of correlation matrices influences the stability of the sign of its coefficients.
