Agent-Based Learning for Pattern Matching in High-Frequency Trade Data
| Autor: | Loonat, Fayyaaz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Druh dokumentu: | Diplomová práce |
| Popis: | A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfilment of the requirements for the degree of Master of Science, 2017 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. XL2017 |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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