research-article

Discovering causes of financial distress by combining evolutionary algorithms and artificial neural networks

Authors:

Antonio M. Mora García,

Pedro A. Castillo Valdivieso,

Juan J. Merelo Guervós,

Eva Alfaro Cid,

Anna I. Esparcia-Alcázar,

Ken SharmanAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1243 - 1250

https://doi.org/10.1145/1389095.1389337

Published: 12 July 2008 Publication History

Abstract

In this work we compare two soft-computing methods for producing models that are able to predict whether a company is going to have book losses: artificial neural networks (ANNs) and genetic programming (GP). In order to build prediction models that can be applied to an extensive number of practical cases, we need simple models which require a small amount of data. Kohonen's self-organizing map (SOM) is a non-supervised neural network that is usually used as a clustering tool. In our case a SOM has been used to reduce the dimensions of the prediction problem. Traditionally, ANNs have been considered able to produce better classifier structures than GP. In this work we merge the capability of GP for generating classification trees and the feature extraction abilities of SOM, obtaining a classification tool that beats the results yielded using an evolutionary ANN method.

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Cited By

Kuizinienė DSavickas PKunickaitė RJuozaitienė RDamaševičius RMaskeliūnas RKrilavičius T(2024)A comparative study of feature selection and feature extraction methods for financial distress identificationPeerJ Computer Science10.7717/peerj-cs.195610(e1956)Online publication date: 30-Apr-2024
https://doi.org/10.7717/peerj-cs.1956
Kuizinienė DKrilavičius T(2024)Balancing Techniques for Advanced Financial Distress Detection Using Artificial IntelligenceElectronics10.3390/electronics1308159613:8(1596)Online publication date: 22-Apr-2024
https://doi.org/10.3390/electronics13081596
Kuizinienė DKrilavičius TDamaševičius RMaskeliūnas R(2022)Systematic Review of Financial Distress Identification using Artificial Intelligence MethodsApplied Artificial Intelligence10.1080/08839514.2022.213812436:1Online publication date: 18-Nov-2022
https://doi.org/10.1080/08839514.2022.2138124
Show More Cited By

Index Terms

Discovering causes of financial distress by combining evolutionary algorithms and artificial neural networks
1. Applied computing
  1. Law, social and behavioral sciences
2. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Kuizinienė DSavickas PKunickaitė RJuozaitienė RDamaševičius RMaskeliūnas RKrilavičius T(2024)A comparative study of feature selection and feature extraction methods for financial distress identificationPeerJ Computer Science10.7717/peerj-cs.195610(e1956)Online publication date: 30-Apr-2024
https://doi.org/10.7717/peerj-cs.1956
Kuizinienė DKrilavičius T(2024)Balancing Techniques for Advanced Financial Distress Detection Using Artificial IntelligenceElectronics10.3390/electronics1308159613:8(1596)Online publication date: 22-Apr-2024
https://doi.org/10.3390/electronics13081596
Kuizinienė DKrilavičius TDamaševičius RMaskeliūnas R(2022)Systematic Review of Financial Distress Identification using Artificial Intelligence MethodsApplied Artificial Intelligence10.1080/08839514.2022.213812436:1Online publication date: 18-Nov-2022
https://doi.org/10.1080/08839514.2022.2138124
Mora AHerrera LUrquiza JRojas IMerelo J(2010)Applying support vector machines and mutual information to book losses predictionThe 2010 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2010.5596710(1-7)Online publication date: Jul-2010
https://doi.org/10.1109/IJCNN.2010.5596710
Butler MDaniyal ARothlauf F(2009)Multi-objective optimization with an evolutionary artificial neural network for financial forecastingProceedings of the 11th Annual conference on Genetic and evolutionary computation10.1145/1569901.1570096(1451-1458)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1569901.1570096

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