Agent-based learning for pattern matching in high-frequency trade data

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dc.contributor.authorLoonat, Fayyaaz
dc.date.accessioned2017-12-21T06:18:47Z
dc.date.available2017-12-21T06:18:47Z
dc.date.issued2017
dc.descriptionA dissertation submitted in fulfilment of the requirements for the degree of Master of Science to the Faculty of Science, University of the Witwatersrand, 2017en_ZA
dc.description.abstractPreviousresearchofsequentialinvestmentstrategiesforportfolioselectionhaveshownthatthereare strategies that exist that can beat the best stock in the market. In this dissertation, an algorithm is presented that uses a nearest neighbour approach similar to the one used by Gy¨orfi et al [20, 21, 22]. Theapproachishoweverextendedtoincludezero-costportfoliosandusesaquadraticapproximation, instead of an optimisation step, to determine how capital should be allocated in the portfolio based on the neighbours that have been found. A portfolio that results in an increase in the investor’s capitalandcomparesfavourablytocertainbenchmarks,suchasthebeststock,indicatesthatthereare patternsinthetimeseriesdata. Otherfeaturesofthealgorithmpresentedistoallowforthedatatobe clustered by a selection of stocks or partitioned based on time. The algorithm is tested on synthetic datasetsthatdepictdifferentmarkettypesandisshowntoaccuratelydeterminetrendsinthedata. The algorithm is then tested on real data from the New York Stock Exchange (NYSE) and data from the JohannesburgStockExchange(JSE).Theresultsofthealgorithmfromtherealdatasetsarecompared to implemented versions of past strategies from the literature and compares favourably.en_ZA
dc.description.librarianXL2017en_ZA
dc.format.extentOnline resource (xiv, 102 leaves)
dc.identifier.citationLoonat, Fayyaaz (2017) Agent-based learning for pattern matching in high-frequency trade data, University of the Witwatersrand, Johannesburg, http://hdl.handle.net/10539/23537
dc.identifier.urihttp://hdl.handle.net/10539/23537
dc.language.isoenen_ZA
dc.subject.lcshJohannesburg Stock Exchange
dc.subject.lcshInvestment analysis
dc.subject.lcshPortfolio management
dc.titleAgent-based learning for pattern matching in high-frequency trade dataen_ZA
dc.typeThesisen_ZA
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