Show simple item record

dc.contributor.authorDoherty, K.
dc.contributor.authorAdams, R.G.
dc.contributor.authorDavey, N.
dc.contributor.authorPensuwon, W.
dc.date.accessioned2007-10-01T09:56:54Z
dc.date.available2007-10-01T09:56:54Z
dc.date.issued2005
dc.identifier.citationDoherty , K , Adams , R G , Davey , N & Pensuwon , W 2005 , Hierarchical topological clustering learns stock market sectors . in Procs of 2005 ICSC Congress on Computational Intelligence Methods and Applications . vol. 2005 , IEEE . https://doi.org/10.1109/CIMA.2005.1662299
dc.identifier.isbn1-4244-0020-1
dc.identifier.otherPURE: 425319
dc.identifier.otherPURE UUID: 74401c74-fa5e-45c7-8fce-559268588ead
dc.identifier.otherdspace: 2299/776
dc.identifier.urihttp://hdl.handle.net/2299/776
dc.description.abstractThe breakdown of financial markets into sectors provides an intuitive classification for groups of companies. The allocation of a company to a sector is an expert task, in which the company is classified by the activity that most closely describes the nature of the company's business. Individual share price movement is dependent upon many factors, but there is an expectation for shares within a market sector to move broadly together. We are interested in discovering if share closing prices do move together, and whether groups of shares that do move together are identifiable in terms of industrial activity. Using TreeGNG, a hierarchical clustering algorithm, on a time series of share closing prices, we have identified groups of companies that cluster into clearly identifiable groups. These clusters compare favourably to a globally accepted sector classification scheme, and in our opinion, our method identifies sector structure clearer than a statistical agglomerative hierarchical clustering methoden
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofProcs of 2005 ICSC Congress on Computational Intelligence Methods and Applications
dc.titleHierarchical topological clustering learns stock market sectorsen
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionSchool of Computer Science
rioxxterms.versionofrecordhttps://doi.org/10.1109/CIMA.2005.1662299
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record