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Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm

Aluko, B and Smonou, D and Kampouridis, M and Tsang, E (2014) Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm. In: UNSPECIFIED, ? - ?.

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Abstract

© 2014 IEEE. Hyper-heuristics have successfully been applied to a vast number of search and optimization problems. One of the novelties of hyper-heuristics is the fact that they manage and automate the meta-heuristic's selection process. In this paper, we implemented and analyzed a hyper-heuristic framework on three meta-heuristics namely Simulated Annealing, Tabu Search, and Guided Local Search, which had successfully been applied in the past to a Financial Forecasting algorithm called EDDIE. EDDIE uses Genetic Programming to extract and learn from historical data in order to predict future financial market movements. Results show that the algorithm's effectiveness has been improved, thus making the combination of meta-heuristics under a hyper-heuristic framework an effective Financial Forecasting approach.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 05 Dec 2014 14:11
Last Modified: 17 Aug 2017 17:43
URI: http://repository.essex.ac.uk/id/eprint/12009

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