Multidimensional stock network analysis: an escoufier's RV coefficient approach

The current practice of stocks network analysis is based on the assumption that the time series of closed stock price could represent the behaviour of the each stock. This assumption leads to consider minimal spanning tree (MST) and sub-dominant ultrametric (SDU) as an indispensible tool to filter t...

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Main Authors: Lee, G. S., Djauhari, M. A.
Format: Conference or Workshop Item
Published: 2013
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Online Access:http://eprints.utm.my/id/eprint/51186/
http://dx.doi.org/10.1063/1.4823975
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.511862017-09-17T08:15:43Z http://eprints.utm.my/id/eprint/51186/ Multidimensional stock network analysis: an escoufier's RV coefficient approach Lee, G. S. Djauhari, M. A. Q Science The current practice of stocks network analysis is based on the assumption that the time series of closed stock price could represent the behaviour of the each stock. This assumption leads to consider minimal spanning tree (MST) and sub-dominant ultrametric (SDU) as an indispensible tool to filter the economic information contained in the network. Recently, there is an attempt where researchers represent stock not only as a univariate time series of closed price but as a bivariate time series of closed price and volume. In this case, they developed the so-called multidimensional MST to filter the important economic information. However, in this paper, we show that their approach is only applicable for that bivariate time series only. This leads us to introduce a new methodology to construct MST where each stock is represented by a multivariate time series. An example of Malaysian stock exchange will be presented and discussed to illustrate the advantages of the method. 2013 Conference or Workshop Item PeerReviewed Lee, G. S. and Djauhari, M. A. (2013) Multidimensional stock network analysis: an escoufier's RV coefficient approach. In: AIP Conference Proceedings. http://dx.doi.org/10.1063/1.4823975
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 Q Science
spellingShingle Q Science
Lee, G. S.
Djauhari, M. A.
Multidimensional stock network analysis: an escoufier's RV coefficient approach
description The current practice of stocks network analysis is based on the assumption that the time series of closed stock price could represent the behaviour of the each stock. This assumption leads to consider minimal spanning tree (MST) and sub-dominant ultrametric (SDU) as an indispensible tool to filter the economic information contained in the network. Recently, there is an attempt where researchers represent stock not only as a univariate time series of closed price but as a bivariate time series of closed price and volume. In this case, they developed the so-called multidimensional MST to filter the important economic information. However, in this paper, we show that their approach is only applicable for that bivariate time series only. This leads us to introduce a new methodology to construct MST where each stock is represented by a multivariate time series. An example of Malaysian stock exchange will be presented and discussed to illustrate the advantages of the method.
format Conference or Workshop Item
author Lee, G. S.
Djauhari, M. A.
author_facet Lee, G. S.
Djauhari, M. A.
author_sort Lee, G. S.
title Multidimensional stock network analysis: an escoufier's RV coefficient approach
title_short Multidimensional stock network analysis: an escoufier's RV coefficient approach
title_full Multidimensional stock network analysis: an escoufier's RV coefficient approach
title_fullStr Multidimensional stock network analysis: an escoufier's RV coefficient approach
title_full_unstemmed Multidimensional stock network analysis: an escoufier's RV coefficient approach
title_sort multidimensional stock network analysis: an escoufier's rv coefficient approach
publishDate 2013
url http://eprints.utm.my/id/eprint/51186/
http://dx.doi.org/10.1063/1.4823975
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