Variable selection for financial distress classification using a genetic algorithm

R. K. H. Gãlvao; Victor Manuel Becerra; M. Abou-Seada

doi:10.1109/CEC.2002.1004550

Variable selection for financial distress classification using a genetic algorithm

R. K. H. Gãlvao, Victor Manuel Becerra, M. Abou-Seada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.

Original language	English
Title of host publication	Congress on evolutionary computation: CEC '02
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2000-2005
Number of pages	6
DOIs	https://doi.org/10.1109/CEC.2002.1004550
Publication status	Published - 2002

Keywords

corporate distress classification, discriminant analysis, financial distress, financial ratios, genetic algorithm, prediction models, ratio selection, variable selection

Access to Document

10.1109/CEC.2002.1004550

Cite this

@inproceedings{855d3dcae3d945f888d11dd231fe848b,

title = "Variable selection for financial distress classification using a genetic algorithm",

abstract = "This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.",

keywords = "corporate distress classification, discriminant analysis, financial distress, financial ratios, genetic algorithm, prediction models, ratio selection, variable selection",

author = "G{\~a}lvao, \{R. K. H.\} and Becerra, \{Victor Manuel\} and M. Abou-Seada",

year = "2002",

doi = "10.1109/CEC.2002.1004550",

language = "English",

pages = "2000--2005",

booktitle = "Congress on evolutionary computation: CEC '02",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

TY - GEN

T1 - Variable selection for financial distress classification using a genetic algorithm

AU - Gãlvao, R. K. H.

AU - Becerra, Victor Manuel

AU - Abou-Seada, M.

PY - 2002

Y1 - 2002

N2 - This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.

AB - This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.

KW - corporate distress classification, discriminant analysis, financial distress, financial ratios, genetic algorithm, prediction models, ratio selection, variable selection

U2 - 10.1109/CEC.2002.1004550

DO - 10.1109/CEC.2002.1004550

M3 - Conference contribution

SP - 2000

EP - 2005

BT - Congress on evolutionary computation: CEC '02

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Variable selection for financial distress classification using a genetic algorithm

Abstract

Keywords

Access to Document

Fingerprint

Cite this