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Wavelet analysis of intraday share prices
Dissertation (MBA)--University of Pretoria, 2014.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2015
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| _version_ | 1867613503348539392 |
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| access_status_str | Open Access |
| author2 | Muller, Chris |
| author_browse | Muller, Chris |
| author_facet | Muller, Chris |
| 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 (MBA)--University of Pretoria, 2014. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/43977 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:11.117Z |
| 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:2263/43977 Wavelet analysis of intraday share prices Muller, Chris ichelp@gibs.co.za Stoffberg, Pieter UCTD Algorithms Stocks -- Prices Dissertation (MBA)--University of Pretoria, 2014. This research tested whether wavelet based algorithms can improve the performance of intraday share trading algorithms. The trading algorithms investigated, each consisted of two parts: the first part performed share price prediction and the second part traded based on the prediction. All the trades in the shares BTI, MTN, NPN and SBK through 2013 on the JSE with the associated time stamps, transaction share prices and volumes, served as the basic sample. The sample was further reduced by using end-of-interval transaction share prices at intervals of one, two, five and ten minutes throughout the trade days. Three types of prediction algorithms were employed: auto regressive moving average (ARMA), wavelet-ARMA and wavelet regressive algorithms. The wavelet based algorithms were further broken down by using up to six different levels of scales in each of the algorithms. These algorithms were fitted using the first half year of data while the tests were conducted on the second half year of data. Two trade algorithms were created by the researcher: One algorithm for buyand- sell and another for short-and-close. Both algorithms used the predicted share price one and two intervals ahead as input and took transaction cost into account. The trade algorithms entered the market daily after opening time and exited the market before closing time. The wavelet based algorithms were not found to improve the accuracy of share price prediction. However, in agreement with previous research, wavelet based algorithms were found to improve the accuracy of predicting the direction of the share prices. The wavelet based algorithms were also found to improve trading performance. Short-and-close algorithms outperformed buy-and-sell. None of the intraday trade algorithms were found to outperform buy-and-hold over the test period. This study contributes to academic research regarding the manner in which wavelet based and ARMA algorithms were combined, the application of a wavelet-regressive prediction method to financial time series and the application of wavelet based trading algorithms on an intraday time scale. lmgibs2015 Gordon Institute of Business Science (GIBS) MBA Unrestricted 2015-03-13T11:14:45Z 2015-03-13T11:14:45Z 2015-03-24 2014 Mini Dissertation Stoffberg, P 2014, Wavelet analysis of intraday share prices, MBA Mini Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/43977> http://hdl.handle.net/2263/43977 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 Algorithms Stocks -- Prices Wavelet analysis of intraday share prices |
| title | Wavelet analysis of intraday share prices |
| title_full | Wavelet analysis of intraday share prices |
| title_fullStr | Wavelet analysis of intraday share prices |
| title_full_unstemmed | Wavelet analysis of intraday share prices |
| title_short | Wavelet analysis of intraday share prices |
| title_sort | wavelet analysis of intraday share prices |
| topic | UCTD Algorithms Stocks -- Prices |
| url | http://hdl.handle.net/2263/43977 |