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dc.contributor.authorÖzün, Alper
dc.date.accessioned2014-08-13T10:55:14Z
dc.date.available2014-08-13T10:55:14Z
dc.date.issued2006-07
dc.identifier.issn1303-2739
dc.identifier.urihttp://hdl.handle.net/11413/371
dc.description.abstractLack of deef trading volüme, increasing levels of hot Money and information, flexibilities of hedge funds an volatilitiesin emerging markets interrupt the linear relationship between risk and return. Due to corupted risk perspectives and irrational risk appettites of the market participants, chaotic patterns and non-linear behaviours in financial time series might ocur in emerging markets. Traditional econometric models are not able to capture chaotic natüre of the markets due to their strick assumptions on time series such as requirement of normal distribution fort he series. Recently, artificial neural networks, wavelets, fuzzy logic and genetic algortihms have been uset to model chaotic behaviours. This paper discusses the reasons of emergence of chaotic patterns and algorithms of modeling of those patterns. Neural networks and wavelets are introduced as modeling methods with a simple simulations based on feedforward neural networks. The paper concludes that successfully designed hybrid intelligent models might capture the chaos and non-linearities in the markets.en
dc.language.isoen_UStr_TR
dc.publisherİstanbul Kültür Üniversitesi Yayınlarıtr_TR
dc.subjectChaotic Marketstr_TR
dc.subjectNonlinear Dynamicstr_TR
dc.subjectNeural Networkstr_TR
dc.subjectWaveletstr_TR
dc.subjectKaotik Piyasalartr_TR
dc.subjectDoğrusal Olmayan Dinamikleritr_TR
dc.subjectSinir Ağlarıtr_TR
dc.subjectDalgalanmatr_TR
dc.titleModeling Chaotic Behaviours In Financial Marketstr_TR
dc.typeArticletr_TR


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