Topological characteristics of the Hong Kong stock market : a test-based P-threshold approach to understanding network complexity

Ronghua XU, Wing Keung WONG, Guanrong CHEN, Shuo HUANG

Research output: Journal PublicationsJournal Article (refereed)

8 Citations (Scopus)

Abstract

In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understanding network complexity by examining the Hong Kong stock market. By comparing with other filtering methods, we find that the P-threshold approach extracts purely and significantly correlated stock pairs, which reflect the well-defined hierarchical structure of the market. In analyzing the dynamic stock networks with fixed-size moving windows, our results show that three global financial crises, covered by the long-range time series, can be distinguishingly indicated from the network topological and evolutionary perspectives. In addition, we find that the assortativity coefficient can manifest the financial crises and therefore can serve as a good indicator of the financial market development.
Original languageEnglish
JournalScientific Reports
Volume7
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes

Fingerprint

Hong Kong stock market
Connectivity
Financial time series
Coefficients
Financial crisis
Global financial crisis
Hierarchical structure
Evolutionary
Financial market development
T-test
Filter
Market index
P value

Cite this

@article{dfa36f105e4c427abc0c39e5d804b5da,
title = "Topological characteristics of the Hong Kong stock market : a test-based P-threshold approach to understanding network complexity",
abstract = "In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understanding network complexity by examining the Hong Kong stock market. By comparing with other filtering methods, we find that the P-threshold approach extracts purely and significantly correlated stock pairs, which reflect the well-defined hierarchical structure of the market. In analyzing the dynamic stock networks with fixed-size moving windows, our results show that three global financial crises, covered by the long-range time series, can be distinguishingly indicated from the network topological and evolutionary perspectives. In addition, we find that the assortativity coefficient can manifest the financial crises and therefore can serve as a good indicator of the financial market development.",
author = "Ronghua XU and WONG, {Wing Keung} and Guanrong CHEN and Shuo HUANG",
year = "2017",
month = "1",
day = "1",
doi = "10.1038/srep41379",
language = "English",
volume = "7",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",

}

Topological characteristics of the Hong Kong stock market : a test-based P-threshold approach to understanding network complexity. / XU, Ronghua; WONG, Wing Keung; CHEN, Guanrong; HUANG, Shuo.

In: Scientific Reports, Vol. 7, 01.01.2017.

Research output: Journal PublicationsJournal Article (refereed)

TY - JOUR

T1 - Topological characteristics of the Hong Kong stock market : a test-based P-threshold approach to understanding network complexity

AU - XU, Ronghua

AU - WONG, Wing Keung

AU - CHEN, Guanrong

AU - HUANG, Shuo

PY - 2017/1/1

Y1 - 2017/1/1

N2 - In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understanding network complexity by examining the Hong Kong stock market. By comparing with other filtering methods, we find that the P-threshold approach extracts purely and significantly correlated stock pairs, which reflect the well-defined hierarchical structure of the market. In analyzing the dynamic stock networks with fixed-size moving windows, our results show that three global financial crises, covered by the long-range time series, can be distinguishingly indicated from the network topological and evolutionary perspectives. In addition, we find that the assortativity coefficient can manifest the financial crises and therefore can serve as a good indicator of the financial market development.

AB - In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understanding network complexity by examining the Hong Kong stock market. By comparing with other filtering methods, we find that the P-threshold approach extracts purely and significantly correlated stock pairs, which reflect the well-defined hierarchical structure of the market. In analyzing the dynamic stock networks with fixed-size moving windows, our results show that three global financial crises, covered by the long-range time series, can be distinguishingly indicated from the network topological and evolutionary perspectives. In addition, we find that the assortativity coefficient can manifest the financial crises and therefore can serve as a good indicator of the financial market development.

UR - http://commons.ln.edu.hk/sw_master/6725

U2 - 10.1038/srep41379

DO - 10.1038/srep41379

M3 - Journal Article (refereed)

VL - 7

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

ER -