Heuristic procedures for improving the predictability of a genetic programming financial forecasting algorithm

Kampouridis, Michael and Otero, Fernando E.B. (2017) Heuristic procedures for improving the predictability of a genetic programming financial forecasting algorithm. Soft Computing, 21 (2). pp. 295-310. ISSN 1432-7643. E-ISSN 1433-7479. (doi:https://doi.org/10.1007/s00500-015-1614-8) (Full text available)

Abstract

Financial forecasting is an important area in computational finance. Evolutionary Dynamic Data Investment Evaluator (EDDIE) is an established genetic programming (GP) financial forecasting algorithm, which has successfully been applied to a number of international financial datasets. The purpose of this paper is to further improve the algorithm’s predictive performance, by incorporating heuristics in the search. We propose the use of two heuristics: a sequential covering strategy to iteratively build a solution in combination with the GP search and the use of an entropy-based dynamic discretisation procedure of numeric values. To examine the effectiveness of the proposed improvements, we test the new EDDIE version (EDDIE 9) across 20 datasets and compare its predictive performance against three previous EDDIE algorithms. In addition, we also compare our new algorithm’s performance against C4.5 and RIPPER, two state-of-the-art classification algorithms. Results show that the introduction of heuristics is very successful, allowing the algorithm to outperform all previous EDDIE versions and the well-known C4.5 and RIPPER algorithms. Results also show that the algorithm is able to return significantly high rates of return across the majority of the datasets.

Item Type: Article
Uncontrolled keywords: Genetic programming, Financial forecasting, EDDIE, Sequential covering, Dynamic discretisation
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 21 Feb 2015 10:02 UTC
Last Modified: 24 Mar 2017 11:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/47287 (The current URI for this page, for reference purposes)
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