Centrality Metrics’ Performance Comparisons on Stock Market Datasets

Hua, J; Huang, M; Huang, C

Centrality Metrics’ Performance Comparisons on Stock Market Datasets

Hua, J

Huang, M

Huang, C

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Symmetry, 2019, 11 (7), pp. ? - ? (15)
Issue Date:: 2019-07-15

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Field	Value	Language
dc.contributor.author	Hua, J https://orcid.org/0000-0002-3409-2076	en_US
dc.contributor.author	Huang, M https://orcid.org/0000-0002-6896-6480	en_US
dc.contributor.author	Huang, C	en_US
dc.date.issued	2019-07-15	en_US
dc.identifier.citation	Symmetry, 2019, 11 (7), pp. ? - ? (15)	en_US
dc.identifier.issn	2073-8994	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134593
dc.description.abstract	<jats:p>The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks.</jats:p>	en_US
dc.language	en	en_US
dc.publisher	MDPI AG	en_US
dc.relation.ispartof	Symmetry	en_US
dc.relation.isbasedon	10.3390/sym11070916	en_US
dc.title	Centrality Metrics’ Performance Comparisons on Stock Market Datasets	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	11	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.consider-herdc	false	en_US
pubs.issue	7	en_US
pubs.publication-status	Published online	en_US
pubs.volume	11	en_US

Abstract:

The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks.

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http://hdl.handle.net/10453/134593