Title: Examining corporate bankruptcy: an artificial intelligence approach

Authors: Kee S. Kim

Addresses: Finance and GB Department, Southwest Missouri State University, 901 S. National, Springfield, MO 65804, USA

Abstract: This paper intends to identify empirically the common characteristics of bankrupting firms from accounting and financial data. It provides not only ways of predicting firms headed for bankruptcy, but also identifying the pattern of failing firms over time. The Adaptive Learning Network (ALN), an Artificial Intelligence technique, is used in this research because it is known to handle data that are inconsistent, unreliable, and incomplete. In addition, the financial data of the bankrupting firms are non-parametric and correlated among themselves. In this paper, the predictability of bankruptcy is first examined and analysed. This paper also attempts to identify common characteristics and patterns of failing firms as companies approach bankruptcy. The results are very promising; the model was successful not only in predicting bankruptcy, but also provided important common characteristics of failing firms.

Keywords: corporate bankruptcy; business failure; insolvency; failure prediction; artificial intelligence; bankrupcy prediction; adaptive learning network; failing firms.

DOI: 10.1504/IJBPM.2005.006718

International Journal of Business Performance Management, 2005 Vol.7 No.3, pp.241 - 254

Published online: 04 Apr 2005 *

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