ETD Collection

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Author: search.filters.author.Loonat, FayyaazSubject: search.filters.subject.Johannesburg Stock Exchange

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    Agent-based learning for pattern matching in high-frequency trade data
    (2017) Loonat, Fayyaaz
    Previousresearchofsequentialinvestmentstrategiesforportfolioselectionhaveshownthatthereare 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.