Title:
Learning zero-cost portfolio selection with pattern matching for intraday
trading
Speaker:
Tim Gebbie
Authors:
Tim Gebbie, Fayyaaz Loonat
Abstract:
We consider and extend an adversarial agent-based learning approach to the
situation of zero-cost portfolio selection in the domain of quantitative
trading. The algorithms are directly compared to standard NYSE test cases from
prior literature. The learning algorithm is used to select parameters for
agents (or experts) generated by pattern matching past dynamics using a simple
nearest-neighbour search algorithm. We demonstrate that patterns in financial
time-series on the JSE can be systematically exploited in collective, but that
this does not imply predictability of the individual asset time-series
themselves. We show that these types of machine learning algorithms are well
suited for intra-day quantitative trading.
http://www.exbo.co.za/REG/dynamic_forms/generated_forms/safa_2017_register.php
http://uct-cmc.co.za/conferences/southern-african-finance-association-safa-conference/
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