Finding Weakly Correlated Nodes in Random Variable Networks

The issue of identifying sets of weakly correlated stocks is explored. Four distinct methods for constructing these sets are compared: the traditional approach using Pearson correlation, the traditional approach using Kendall correlation, and multiple hypothesis testing methods, which apply both Pearson and Kendall correlations. To derive specific findings, we analyze daily returns of a selection of stocks listed on the Frankfurt (FWB), London (LSE), and Paris (Euronext Paris) stock exchanges. Our results reveal a significant difference between the identified sets of weakly correlated stocks in Pearson and Kendall correlation networks. Notably, this difference is more substantial in the statistically significant sets of weakly correlated stocks derived from multiple hypothesis testing methods than in those obtained through traditional procedures. We recommend for the use of multiple hypothesis testing methods based on Kendall correlation for analyzing market data.