FORECASTING WITH ARTMAP-IC NEURAL NETWORKS - An Application Using Corporate Bankruptcy Data

Anatoli Nachev

2008

Abstract

Financial diagnosis and prediction of corporate bankruptcy can be viewed as a pattern recognition problem. This paper proposes a novel approach to solution based on ARTMAP-IC - a general-purpose neural network system for supervised learning and recognition. For a popular dataset, with proper preprocessing steps, the model outperforms similar techniques and provides prediction accuracy equal to the best one obtained by a backpropagation MLPs. An advantage of the proposed model over the MLPs is the short online learning, fast adaptation to novel patterns and scalability.

Paper Citation

in Harvard Style

Nachev A. (2008). FORECASTING WITH ARTMAP-IC NEURAL NETWORKS - An Application Using Corporate Bankruptcy Data . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 167-172. DOI: 10.5220/0001680201670172

in Bibtex Style

@conference{iceis08,
author={Anatoli Nachev},
title={FORECASTING WITH ARTMAP-IC NEURAL NETWORKS - An Application Using Corporate Bankruptcy Data},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2008},
pages={167-172},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001680201670172},
isbn={978-989-8111-37-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - FORECASTING WITH ARTMAP-IC NEURAL NETWORKS - An Application Using Corporate Bankruptcy Data
SN - 978-989-8111-37-1
AU - Nachev A.
PY - 2008
SP - 167
EP - 172
DO - 10.5220/0001680201670172