Willis, A; Patel, S; Clack, CD; (2008) GP age-layer and crossover effects in bid-offer spread prediction. In: Keijzer, M, (ed.) Proceedings of the 10th annual conference on Genetic and evolutionary computation. (pp. pp. 1665-1672). ACM: Association for Computing Machinery: Atlanta, GA, USA.

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Abstract

The bid-offer spread on equity options is a key source of profits for market makers, and a key cost for those trading in the options. Spreads are influenced by dynamic market factors, but is there also a predictable element and can Genetic Programming be used for such prediction? We investigate a standard GP approach and two optimisations . age-layering and a novel crossover operator. If both are beneficial as independent optimisations, will they be mutually beneficial when applied simultaneously? Our experiments show a degree of success in predicting spreads, we demonstrate significant benefits for each optimisation technique used individually, and we show that when both are used together significant detrimental over-fitting can occur.

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