Multivariate time series similarity-based complex network in stocks market analysis: case of NYSE during global crisis 2008

Long before we started with the 21st millennium, Stephen Hawking saw the current millennium as the millennium of complex systems. Until present, he was right due to the fast growing technology in computer. Nowadays, in the era of digital world where big data is our daily menu, we cannot escape from...

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Main Authors: Djauhari, Maman Abdurachman, Gan, Siew Lee
Format: Conference or Workshop Item
Language:English
Published: 2015
Online Access:http://psasir.upm.edu.my/id/eprint/66890/1/MyStats%202015-1.pdf
http://psasir.upm.edu.my/id/eprint/66890/
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spelling my.upm.eprints.668902019-03-06T05:25:11Z http://psasir.upm.edu.my/id/eprint/66890/ Multivariate time series similarity-based complex network in stocks market analysis: case of NYSE during global crisis 2008 Djauhari, Maman Abdurachman Gan, Siew Lee Long before we started with the 21st millennium, Stephen Hawking saw the current millennium as the millennium of complex systems. Until present, he was right due to the fast growing technology in computer. Nowadays, in the era of digital world where big data is our daily menu, we cannot escape from complex systems. As big data is characterized by “4V” (Variety, Velocity, Veracity and Volume), statistics such as practiced in traditional way is not enough and sometime is not apt to be used to understand the most important information contained in big data. What people call now data analytics needs to be used as the only complementary. It is mathematically dominated by multivariate data analysis (MVDA) in the French way. Traditional statistics, which is based on mathematical statistics, is to do confirmatory analysis while data analytics is to do exploratory analysis. The former is to do hypothesis testing (micro analysis) and the latter is for hypothesis generation (macro analysis). Macro analysis is more appropriate to deal with big data. The principal mathematical tool to do macro analysis is MVDA in the French way where big data is considered as a complex system. In this regards, the main problem is to define the similarity among objects of the study such as stocks, economic sectors, currencies, and other commodities in financial industry, which are statistically a multivariate time series. Furthermore, the principal tools to filter the important information contained in a complex system are complex network and social network analysis. To demonstrate the advantages of complex network approach in stocks market analysis, in this paper the behaviour of economic sectors played in NYSE during global crisis in 2008 will be presented and discussed. By nature, all stocks are a multivariate time series. Therefore, in that example, we show that the use of Pearson correlation coefficient is useless to define the similarity among them. We use Escoufier’s vector correlation coefficient instead. 2015 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/66890/1/MyStats%202015-1.pdf Djauhari, Maman Abdurachman and Gan, Siew Lee (2015) Multivariate time series similarity-based complex network in stocks market analysis: case of NYSE during global crisis 2008. In: Third National Statistics Conference (MyStats 2015), 17 Nov. 2015, Sasana Kijang, Bank Negara Malaysia. (pp. 83-88).
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Long before we started with the 21st millennium, Stephen Hawking saw the current millennium as the millennium of complex systems. Until present, he was right due to the fast growing technology in computer. Nowadays, in the era of digital world where big data is our daily menu, we cannot escape from complex systems. As big data is characterized by “4V” (Variety, Velocity, Veracity and Volume), statistics such as practiced in traditional way is not enough and sometime is not apt to be used to understand the most important information contained in big data. What people call now data analytics needs to be used as the only complementary. It is mathematically dominated by multivariate data analysis (MVDA) in the French way. Traditional statistics, which is based on mathematical statistics, is to do confirmatory analysis while data analytics is to do exploratory analysis. The former is to do hypothesis testing (micro analysis) and the latter is for hypothesis generation (macro analysis). Macro analysis is more appropriate to deal with big data. The principal mathematical tool to do macro analysis is MVDA in the French way where big data is considered as a complex system. In this regards, the main problem is to define the similarity among objects of the study such as stocks, economic sectors, currencies, and other commodities in financial industry, which are statistically a multivariate time series. Furthermore, the principal tools to filter the important information contained in a complex system are complex network and social network analysis. To demonstrate the advantages of complex network approach in stocks market analysis, in this paper the behaviour of economic sectors played in NYSE during global crisis in 2008 will be presented and discussed. By nature, all stocks are a multivariate time series. Therefore, in that example, we show that the use of Pearson correlation coefficient is useless to define the similarity among them. We use Escoufier’s vector correlation coefficient instead.
format Conference or Workshop Item
author Djauhari, Maman Abdurachman
Gan, Siew Lee
spellingShingle Djauhari, Maman Abdurachman
Gan, Siew Lee
Multivariate time series similarity-based complex network in stocks market analysis: case of NYSE during global crisis 2008
author_facet Djauhari, Maman Abdurachman
Gan, Siew Lee
author_sort Djauhari, Maman Abdurachman
title Multivariate time series similarity-based complex network in stocks market analysis: case of NYSE during global crisis 2008
title_short Multivariate time series similarity-based complex network in stocks market analysis: case of NYSE during global crisis 2008
title_full Multivariate time series similarity-based complex network in stocks market analysis: case of NYSE during global crisis 2008
title_fullStr Multivariate time series similarity-based complex network in stocks market analysis: case of NYSE during global crisis 2008
title_full_unstemmed Multivariate time series similarity-based complex network in stocks market analysis: case of NYSE during global crisis 2008
title_sort multivariate time series similarity-based complex network in stocks market analysis: case of nyse during global crisis 2008
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/66890/1/MyStats%202015-1.pdf
http://psasir.upm.edu.my/id/eprint/66890/
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