Reliability of MST identification in correlation-based market networks

TL;DR

This paper introduces a framework to measure the uncertainty in maximum spanning tree (MST) identification in market networks, analyzing different correlation measures and their reliability using the False Discovery Rate (FDR).

Contribution

It proposes a novel approach to quantify MST uncertainty, compares correlation measures, and demonstrates the robustness of Kendall correlation in market network analysis.

Findings

FDR is the most suitable measure of MST uncertainty.

Kendall correlation network shows the lowest and most stable FDR.

FDR in Pearson network depends on return distribution, unlike in Fechner and Kendall networks.

Abstract

Maximum spanning tree (MST) is a popular tool in market network analysis. Large number of publications are devoted to the MST calculation and it's interpretation for particular stock markets. However, much less attention is payed in the literature to the analysis of uncertainty of obtained results. In the present paper we suggest a general framework to measure uncertainty of MST identification. We study uncertainty in the framework of the concept of random variable network (RVN). We consider different correlation based networks in the large class of elliptical distributions. We show that true MST is the same in three networks: Pearson correlation network, Fechner correlation network, and Kendall correlation network. We argue that among different measures of uncertainty the FDR (False Discovery Rate) is the most appropriated for MST identification. We investigate FDR of Kruskal algorithm for MST identification and show that reliability of MST identification is different in these three networks. In particular, for Pearson correlation network the FDR essentially depends on distribution of stock returns. We prove that for market network with Fechner correlation the FDR is non sensitive to the assumption on stock's return distribution. Some interesting phenomena are discovered for Kendall correlation network. Our experiments show that FDR of Kruskal algorithm for MST identification in Kendall correlation network weakly depend on distribution and at the same time the value of FDR is almost the best in comparison with MST identification in other networks. These facts are important in practical applications.