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A Comparative Study on the Use of Classification Algorithms in Financial Forecasting

Otero, Fernando E.B. and Kampouridis, Michael (2014) A Comparative Study on the Use of Classification Algorithms in Financial Forecasting. In: Applications of Evolutionary Computation 17th European Conference. Lecture Notes in Computer Science . Springer-Verlag, Berlin, Germany, pp. 276-287. ISBN 978-3-662-45522-7. E-ISBN 978-3-662-45523-4. (doi:10.1007/978-3-662-45523-4_23) (KAR id:42142)

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Financial forecasting is a vital area in computational finance, where several studies have taken place over the years. One way of viewing financial forecasting is as a classification problem, where the goal is to find a model that represents the predictive relationships between predictor attribute values and class attribute values. In this paper we present a comparative study between two bio-inspired classification algorithms, a genetic programming algorithm especially designed for financial forecasting, and an ant colony optimization one, which is designed for classification problems. In addition, we compare the above algorithms with two other state-of-the-art classification algorithms, namely C4.5 and RIPPER. Results show that the ant colony optimization classification algorithm is very successful, significantly outperforming all other algorithms in the given classification problems, which provides insights for improving the design of specific financial forecasting algorithms.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-662-45523-4_23
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: 07 Aug 2014 19:31 UTC
Last Modified: 01 Aug 2019 10:37 UTC
Resource URI: (The current URI for this page, for reference purposes)
Otero, Fernando E.B.:
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