Reliability of MST identification in correlation-based market networks

by   V. A. Kalyagin, et al.

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.


page 1

page 2

page 3

page 4


Testing new property of elliptical model for stock returns distribution

Wide class of elliptically contoured distributions is a popular model of...

Forecasting Financial Market Structure from Network Features using Machine Learning

We propose a model that forecasts market correlation structure from link...

Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets

Stock prices are influenced by numerous factors. We present a method to ...

Stock Prices as Janardan Galton Watson Process

Janardan (1980) introduces a class of offspring distributions that sandw...

Financial Return Distributions: Past, Present, and COVID-19

We analyze the price return distributions of currency exchange rates, cr...

Should You Take Investment Advice From WallStreetBets? A Data-Driven Approach

Reddit's WallStreetBets (WSB) community has come to prominence in light ...

Clustering Structure of Microstructure Measures

This paper builds the clustering model of measures of market microstruct...

Please sign up or login with your details

Forgot password? Click here to reset