Full Text Available

Note: Clicking the button above will open the full text document at the original institutional repository in a new window.

An investigation of artificial neural networks applied to pairs trading in South Africa

Dissertation (MSc)--University of Pretoria, 2014.

Saved in:
Bibliographic Details
Other Authors: Mare, Eben
Format: Thesis
Language:English
Published: University of Pretoria 2015
Subjects:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1867613501338419200
access_status_str Open Access
author2 Mare, Eben
author_browse Mare, Eben
author_facet Mare, Eben
collection Thesis
dc_rights_str_mv © 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
description Dissertation (MSc)--University of Pretoria, 2014.
format Thesis
id oai:repository.up.ac.za:2263/43296
institution University of Pretoria (South Africa)
language English
last_indexed 2026-06-10T12:37:09.154Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
publishDate 2015
publishDateRange 2015
publishDateSort 2015
publisher University of Pretoria
publisherStr University of Pretoria
record_format dspace
source_str UPSpace — University of Pretoria Institutional Repository
spelling oai:repository.up.ac.za:2263/43296 An investigation of artificial neural networks applied to pairs trading in South Africa Mare, Eben Buhr, Richard Otto UCTD Dissertation (MSc)--University of Pretoria, 2014. The use of artificial intelligence models could enhance the process by which decisions are made for trading in the financial markets. In this project the aim was to define, create, apply and analyse the results of an artificial neural network based solution for so-called pairs trading. A literature study is presented where a brief summary is given of the current body of knowl- edge with regards to artificial neural networks and their application in finance. Additionally an introduction of the software packages used in this project is given. In a separate chapter a short introduction on trading strategies and pairs trading, specifically, is included. The design of the artificial neural network is discussed in detail with both training and execu- tion results from experiments critically examined. Finally the question ‘Are artificial neural networks a viable tool applied to pairs trading in the South African market?’ is answered based on the empirical evidence. lk2014 Materials Science and Metallurgical Engineering MSc Unrestricted 2015-01-19T12:13:25Z 2015-01-19T12:13:25Z 2014/12/12 2014 Dissertation Buhr, RO 2014, An investigation of artificial neural networks applied to pairs trading in South Africa, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd<http://hdl.handle.net/2263/43296> M14/9/145 http://hdl.handle.net/2263/43296 en © 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria
spellingShingle UCTD
An investigation of artificial neural networks applied to pairs trading in South Africa
title An investigation of artificial neural networks applied to pairs trading in South Africa
title_full An investigation of artificial neural networks applied to pairs trading in South Africa
title_fullStr An investigation of artificial neural networks applied to pairs trading in South Africa
title_full_unstemmed An investigation of artificial neural networks applied to pairs trading in South Africa
title_short An investigation of artificial neural networks applied to pairs trading in South Africa
title_sort investigation of artificial neural networks applied to pairs trading in south africa
topic UCTD
url http://hdl.handle.net/2263/43296