Statistical arbitrage on the FTSE/JSE TOP 40 index

Show simple item record Ngcobo-Koyana, Mandlenkosi Svato 2017-09-20T11:52:44Z 2017-09-20T11:52:44Z 2017
dc.identifier.citation Ngcobo-Koyana, Mandlenkosi Svato (2017) Statistical arbitrage on the FTSE/JSE TOP 40 index, University of the Witwatersrand, Johannesburg, <>
dc.description Submitted as a Requirement of the Master of Management (Finance and Investment Management) University of the Witwatersrand Business School Johannesburg en_ZA
dc.description.abstract The mid 2000’s saw the materialization of research into the financial engineering field of high frequency trading. It is arguable that the most prominent model to emerge from the research has been pairs trading. This idea can be extended to allow for more than two assets in a modelling method now known as statistical arbitrage. The research identifies a collection of assets with a deterministic component; it then follows a multiple linear regression to exploit persistent mispricings among these assets. Further, multiple linear regression metrics are used to identify the analytic form of the trading rule and to validate the performance of the model. The first part of model constructs combinations of assets which contain a significant predictable component by co-integration, the second part builds a predictive models for the dynamics of the mispricing using statistical model. The success of the model is demonstrated with reference to a statistical analysis of 5-minute closing prices on the Johannesburg Stock Exchange (JSE) TOP40 Index and the constituent shares of the JSE TOP40 Index. en_ZA
dc.format.extent Online resource (44 leaves)
dc.language.iso en en_ZA
dc.subject.lcsh Stocks--Prices--South Africa
dc.subject.lcsh Johannesburg Stock Exchange
dc.subject.lcsh Arbitrage
dc.subject.lcsh Finance--Mathematical models
dc.title Statistical arbitrage on the FTSE/JSE TOP 40 index en_ZA
dc.title.alternative An empirical approach to statistical arbitrage on the FTSE/JSE TOP 40 Index
dc.type Thesis en_ZA
dc.description.librarian MT2017 en_ZA

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