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Learning zero-cost portfolio selection with pattern matching

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  • Fayyaaz Loonat
  • Tim Gebbie

Abstract

We replicate and extend the adversarial expert based learning approach of Györfi et al to the situation of zero-cost portfolio selection implemented with a quadratic approximation derived from the mutual fund separation theorems. The algorithm is applied to daily sampled sequential Open-High-Low-Close data and sequential intraday 5-minute bar-data from the Johannesburg Stock Exchange (JSE). Statistical tests of the algorithms are considered. The algorithms are directly compared to standard NYSE test cases from prior literature. The learning algorithm is used to select parameters for experts generated by pattern matching past dynamics using a simple nearest-neighbour search algorithm. It is shown that there is a speed advantage associated with using an analytic solution of the mutual fund separation theorems. We argue that the strategies are on the boundary of profitability when considered in the context of their application to intraday quantitative trading but demonstrate that patterns in financial time-series on the JSE could be systematically exploited in collective and that they are persistent in the data investigated. We do not suggest that the strategies can be profitably implemented but argue that these types of patterns may exists for either structural of implementation cost reasons.

Suggested Citation

  • Fayyaaz Loonat & Tim Gebbie, 2018. "Learning zero-cost portfolio selection with pattern matching," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-38, September.
  • Handle: RePEc:plo:pone00:0202788
    DOI: 10.1371/journal.pone.0202788
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    References listed on IDEAS

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    1. D. Hendricks & T. Gebbie & D. Wilcox, 2016. "Detecting intraday financial market states using temporal clustering," Quantitative Finance, Taylor & Francis Journals, vol. 16(11), pages 1657-1678, November.
    2. Jim Gatheral, 2010. "No-dynamic-arbitrage and market impact," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 749-759.
    3. Diane Wilcox & Tim Gebbie, 2008. "Serial Correlation, Periodicity And Scaling Of Eigenmodes In An Emerging Market," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 11(07), pages 739-760.
    4. Jarrow, Robert & Teo, Melvyn & Tse, Yiu Kuen & Warachka, Mitch, 2012. "An improved test for statistical arbitrage," Journal of Financial Markets, Elsevier, vol. 15(1), pages 47-80.
    5. László Györfi & András Urbán & István Vajda, 2007. "Kernel-Based Semi-Log-Optimal Empirical Portfolio Selection Strategies," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 10(03), pages 505-516.
    6. Tim Gebbie & Fayyaaz Loonat, 2016. "Learning zero-cost portfolio selection with pattern matching," Papers 1605.04600, arXiv.org.
    7. Györfi László & Udina Frederic & Walk Harro, 2008. "Nonparametric nearest neighbor based empirical portfolio selection strategies," Statistics & Risk Modeling, De Gruyter, vol. 26(2), pages 145-157, March.
    8. Jason E. Cross & Andrew R. Barron, 2003. "Efficient Universal Portfolios for Past‐Dependent Target Classes," Mathematical Finance, Wiley Blackwell, vol. 13(2), pages 245-276, April.
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