Network topology of economic sectors

A lot of studies dealing with stock network analysis, where each individual stock is represented by a univariate time series of its closing price, have been published. In these studies, the similarity of two different stocks is quantified using a Pearson correlation coefficient on the logarithmic pr...

Full description

Saved in:
Bibliographic Details
Main Authors: Djauhari, M. A., Gan, S. L.
Format: Article
Published: Institute of Physics Publishing 2016
Subjects:
Online Access:http://eprints.utm.my/id/eprint/72093/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988470939&doi=10.1088%2f1742-5468%2f2016%2f09%2f093401&partnerID=40&md5=96e301e3d8a3aaf5e37783ad21c871f3
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknologi Malaysia
id my.utm.72093
record_format eprints
spelling my.utm.720932017-11-23T04:17:44Z http://eprints.utm.my/id/eprint/72093/ Network topology of economic sectors Djauhari, M. A. Gan, S. L. QA Mathematics A lot of studies dealing with stock network analysis, where each individual stock is represented by a univariate time series of its closing price, have been published. In these studies, the similarity of two different stocks is quantified using a Pearson correlation coefficient on the logarithmic price returns. In this paper, we generalize the notion of similarity between univariate time series into multivariate time series which might be of different dimensions. This allows us to deal with economic sector network analysis, where the similarity between economic sectors is defined using Escoufier's vector correlation RV. To the best of our knowledge, there is no study dealing with this notion of economic sector similarity. Two examples of data from the New York stock exchange will be presented and discussed, and some important results will be highlighted. Institute of Physics Publishing 2016 Article PeerReviewed Djauhari, M. A. and Gan, S. L. (2016) Network topology of economic sectors. Journal of Statistical Mechanics: Theory and Experiment, 2016 (9). ISSN 1742-5468 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988470939&doi=10.1088%2f1742-5468%2f2016%2f09%2f093401&partnerID=40&md5=96e301e3d8a3aaf5e37783ad21c871f3
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Djauhari, M. A.
Gan, S. L.
Network topology of economic sectors
description A lot of studies dealing with stock network analysis, where each individual stock is represented by a univariate time series of its closing price, have been published. In these studies, the similarity of two different stocks is quantified using a Pearson correlation coefficient on the logarithmic price returns. In this paper, we generalize the notion of similarity between univariate time series into multivariate time series which might be of different dimensions. This allows us to deal with economic sector network analysis, where the similarity between economic sectors is defined using Escoufier's vector correlation RV. To the best of our knowledge, there is no study dealing with this notion of economic sector similarity. Two examples of data from the New York stock exchange will be presented and discussed, and some important results will be highlighted.
format Article
author Djauhari, M. A.
Gan, S. L.
author_facet Djauhari, M. A.
Gan, S. L.
author_sort Djauhari, M. A.
title Network topology of economic sectors
title_short Network topology of economic sectors
title_full Network topology of economic sectors
title_fullStr Network topology of economic sectors
title_full_unstemmed Network topology of economic sectors
title_sort network topology of economic sectors
publisher Institute of Physics Publishing
publishDate 2016
url http://eprints.utm.my/id/eprint/72093/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988470939&doi=10.1088%2f1742-5468%2f2016%2f09%2f093401&partnerID=40&md5=96e301e3d8a3aaf5e37783ad21c871f3
_version_ 1643656355249127424