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Metaheuristics application on a financial forecasting problem

Smonou, D and Kampouridis, M and Tsang, E (2013) Metaheuristics application on a financial forecasting problem. In: UNSPECIFIED, ? - ?.

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Abstract

EDDIE is a Genetic Programming (GP) tool, which is used to tackle problems in the field of financial forecasting. The novelty of EDDIE is in its grammar, which allows the GP to look in the space of technical analysis indicators, instead of using pre-specified ones, as it normally happens in the literature. The advantage of this is that EDDIE is not constrained to use pre-specified indicators; instead, thanks to its grammar, it can choose any indicators within a pre-defined range, leading to new solutions that might have never been discovered before. However, a disadvantage of the above approach is that the algorithm's search space is dramatically larger, and as a result good solutions can sometimes be missed due to ineffective search. This paper presents an attempt to deal with this issue by applying to the GP three different meta-heuristics, namely Simulated Annealing, Tabu Search, and Guided Local Search. Results show that the algorithm's performance significantly improves, thus making the combination of Genetic Programming and meta-heuristics an effective financial forecasting approach. © 2013 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2013 IEEE Congress on Evolutionary Computation, CEC 2013
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 18 Sep 2015 17:17
Last Modified: 04 Feb 2019 11:16
URI: http://repository.essex.ac.uk/id/eprint/14857

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