Using Complex Networks to Uncover Interaction in Stock Markets

Abdulrahman Alossaimy

Research output: ThesisDoctoral Thesis

51 Downloads (Pure)

Abstract

We introduced a variety of complex network models to study a stock market. We observe a change in the complex network structure and properties to detect the influence of financial crises on the stock market. The scale-free complex network model was introduced and used to detect the sectors of the stock market utilising the community structure. The same sign of the volatility method was introduced to identify the connection between the nodes of a weighted network. The complex network shows a capacity to clearly detect the response of stock markets to political events (Brexit and the 2016 US elections) and examine the propagation of these crises.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Award date14 Oct 2019
DOIs
Publication statusUnpublished - 2019

Fingerprint

Complex networks
Financial markets

Cite this

@phdthesis{dce038e5ae0d4479961196e97f90ddca,
title = "Using Complex Networks to Uncover Interaction in Stock Markets",
abstract = "We introduced a variety of complex network models to study a stock market. We observe a change in the complex network structure and properties to detect the influence of financial crises on the stock market. The scale-free complex network model was introduced and used to detect the sectors of the stock market utilising the community structure. The same sign of the volatility method was introduced to identify the connection between the nodes of a weighted network. The complex network shows a capacity to clearly detect the response of stock markets to political events (Brexit and the 2016 US elections) and examine the propagation of these crises.",
keywords = "complex network, stock market, financial crisis, instability, scale-free network, political event, weighted network",
author = "Abdulrahman Alossaimy",
year = "2019",
doi = "10.26182/5dc4ffadc60e4",
language = "English",
school = "The University of Western Australia",

}

Alossaimy, A 2019, 'Using Complex Networks to Uncover Interaction in Stock Markets', Doctor of Philosophy, The University of Western Australia. https://doi.org/10.26182/5dc4ffadc60e4

Using Complex Networks to Uncover Interaction in Stock Markets. / Alossaimy, Abdulrahman.

2019.

Research output: ThesisDoctoral Thesis

TY - THES

T1 - Using Complex Networks to Uncover Interaction in Stock Markets

AU - Alossaimy, Abdulrahman

PY - 2019

Y1 - 2019

N2 - We introduced a variety of complex network models to study a stock market. We observe a change in the complex network structure and properties to detect the influence of financial crises on the stock market. The scale-free complex network model was introduced and used to detect the sectors of the stock market utilising the community structure. The same sign of the volatility method was introduced to identify the connection between the nodes of a weighted network. The complex network shows a capacity to clearly detect the response of stock markets to political events (Brexit and the 2016 US elections) and examine the propagation of these crises.

AB - We introduced a variety of complex network models to study a stock market. We observe a change in the complex network structure and properties to detect the influence of financial crises on the stock market. The scale-free complex network model was introduced and used to detect the sectors of the stock market utilising the community structure. The same sign of the volatility method was introduced to identify the connection between the nodes of a weighted network. The complex network shows a capacity to clearly detect the response of stock markets to political events (Brexit and the 2016 US elections) and examine the propagation of these crises.

KW - complex network

KW - stock market

KW - financial crisis

KW - instability

KW - scale-free network

KW - political event

KW - weighted network

U2 - 10.26182/5dc4ffadc60e4

DO - 10.26182/5dc4ffadc60e4

M3 - Doctoral Thesis

ER -