Hurst exponent, fractals and neural networks for forecasting financial asset returns in Brazil

João Nunes de Mendonça Neto; Luiz Paulo Lopes Fávero; Renata Turola Takamatsu

Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/63914

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DC Field	Value	Language
dc.creator	João Nunes de Mendonça Neto	pt_BR
dc.creator	Luiz Paulo Lopes Fávero	pt_BR
dc.creator	Renata Turola Takamatsu	pt_BR
dc.date.accessioned	2024-02-09T12:54:47Z	-
dc.date.available	2024-02-09T12:54:47Z	-
dc.date.issued	2018	-
dc.citation.volume	3	pt_BR
dc.citation.issue	1	pt_BR
dc.citation.spage	29	pt_BR
dc.citation.epage	49	pt_BR
dc.identifier.doi	10.1504/IJDS.2018.090625	pt_BR
dc.identifier.issn	2053082X	pt_BR
dc.identifier.uri	http://hdl.handle.net/1843/63914	-
dc.description.resumo	Our scope is to verify the existence of a relationship between long-term memory in fractal time series and the prediction error of financial asset returns obtained by artificial neural networks (ANNs). We expect that the fractal time series with larger memory can achieve predictions with lower error, since the correlation between elements of the series favours the quality of ANN prediction. As a long-term memory measure, the Hurst exponent of each time series was calculated, and the root mean square error (RMSE) produced by ANN in each time series was used to measure the prediction error. Hurst exponent computation was conducted through the rescaled range analysis (R/S) algorithm. The ANN's architecture used time-lagged feedforward neural networks (TLFN), with backpropagation supervised learning process and gradient descent for error minimisation. Brazilian financial assets traded at BM&FBovespa, specifically public companies shares and real estate investment funds were considered.	pt_BR
dc.language	eng	pt_BR
dc.publisher	Universidade Federal de Minas Gerais	pt_BR
dc.publisher.country	Brasil	pt_BR
dc.publisher.department	FCE - DEPARTAMENTO DE CIÊNCIAS CONTÁBEIS	pt_BR
dc.publisher.initials	UFMG	pt_BR
dc.relation.ispartof	International Journal of Data Science	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.subject	Hurst exponent	pt_BR
dc.subject	Fractals	pt_BR
dc.subject	ANNs	pt_BR
dc.subject	Artificial neural networks	pt_BR
dc.subject	Time series forecasting	pt_BR
dc.subject	financial assets	pt_BR
dc.subject.other	Administração financeira	pt_BR
dc.subject.other	Finanças	pt_BR
dc.title	Hurst exponent, fractals and neural networks for forecasting financial asset returns in Brazil	pt_BR
dc.type	Artigo de Periódico	pt_BR
dc.url.externa	https://www.inderscience.com/info/inarticle.php?artid=90625	pt_BR
Appears in Collections:	Artigo de Periódico

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