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On the investigation of hyper-heuristics on a financial forecasting problem

Kampouridis, Michael, Alsheddy, Abdullah, Tsang, Edward (2013) On the investigation of hyper-heuristics on a financial forecasting problem. Annals of Mathematics and Artificial Intelligence, 68 (4). pp. 225-246. ISSN 1573-7470. (doi:10.1007/s10472-012-9283-0) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:40193)

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Financial forecasting is a really important area in computational finance, with numerous works in the literature. This importance can be reflected in the literature by the continuous development of new algorithms. Hyper-heuristics have been successfully used in the past for a number of search and optimization problems, and have shown very promising results. To the best of our knowledge, they have not been used for financial forecasting. In this paper we present pioneer work, where we use different hyper-heuristics frameworks to investigate whether we can improve the performance of a financial forecasting tool called EDDIE 8. EDDIE 8 allows the GP (Genetic Programming) to search in the search space of indicators for solutions, instead of using pre-specified ones; as a result, its search area has dramatically increased and sometimes solutions can be missed due to ineffective search. We apply 14 different low-level heuristics to EDDIE 8, to 30 different datasets, and examine their effect to the algorithm’s performance. We then select the most prominent heuristics and combine them into three different hyper-heuristics frameworks. Results show that all three frameworks are competitive, and are able to show significantly improved results, especially in the case of best results. Lastly, analysis on the weights of the heuristics shows that there can be a constant swinging among some of the low-level heuristics, which denotes that the hyper-heuristics frameworks are able to ‘know’ the appropriate time to switch from one heuristic to the other, based on their effectiveness

Item Type: Article
DOI/Identification number: 10.1007/s10472-012-9283-0
Additional information: <30> This paper presents pioneering research, by being the first to combine hyper-heuristics with a Genetic Programming (GP) financial forecasting algorithm. As demonstrated in the paper, this combination improved the algorithm’s search effectiveness, and consequently led to significant improvements in forecasting accuracy. This work opens new research paths, as it demonstrates the strength of combining GP with hyper-heuristics for financial forecasting. It also indicates that more research should take place in the above direction, as this yields very promising results. This research attracted interest from the press, and was featured in the “Automated Trader”, a specialised magazine in financial trading.;
Subjects: H Social Sciences > HG Finance
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Stewart Brownrigg
Date Deposited: 07 Mar 2014 00:05 UTC
Last Modified: 16 Nov 2021 10:15 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Kampouridis, Michael.

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