BANKRUPTCY PREDICTION BASED ON INDEPENDENT COMPONENT ANALYSIS

Ning Chen, Armando Vieira

2009

Abstract

Bankruptcy prediction is of great importance in financial statement analysis to minimize the risk of decision strategies. It attempts to separate distress companies from healthy ones according to some financial indicators. Since the real data usually contains irrelevant, redundant and correlated variables, it is necessary to reduce the dimensionality before performing the prediction. In this paper, a hybrid bankruptcy prediction algorithm is proposed based on independent component analysis and learning vector quantization. Experiments show the algorithm is effective for high dimensional bankruptcy data and therefore improve the capability of prediction.

Paper Citation

in Harvard Style

Chen N. and Vieira A. (2009). BANKRUPTCY PREDICTION BASED ON INDEPENDENT COMPONENT ANALYSIS . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 150-155. DOI: 10.5220/0001536301500155

in Bibtex Style

@conference{icaart09,
author={Ning Chen and Armando Vieira},
title={BANKRUPTCY PREDICTION BASED ON INDEPENDENT COMPONENT ANALYSIS},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2009},
pages={150-155},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001536301500155},
isbn={978-989-8111-66-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - BANKRUPTCY PREDICTION BASED ON INDEPENDENT COMPONENT ANALYSIS
SN - 978-989-8111-66-1
AU - Chen N.
AU - Vieira A.
PY - 2009
SP - 150
EP - 155
DO - 10.5220/0001536301500155