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

Anatoli Nachev

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.

References

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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