ABSTRACT
In 1993, Austria had the highest number of bankruptcies since 1945. The total liabilities came to approximately US$3 billion.
Powerful tools for the early detection of company risks are very important to avoid high economic losses. Artificial neural networks (ANN) are suitable for many tasks in pattern recognition and machine learning. In this paper we present an ANN for early detection of company failures using balance sheet ratios. The neural network has been successfully tested with real data of Austrian private limited companies. The research activities included the design of an APL application with a graphical user interface to find out the relevant input data and tune the ANN.
The developed APL workspace takes advantage of modern windowing features running on IBM compatible computers.
- Alf91.M. Alfonseca. Advanced applications of APL: logic programming, neural networks and hypertext. IBM Systems Journal, 30(4):543- 553, 1991. Google ScholarDigital Library
- Ble85.Ernst Bleier. Insolvenzfrtiherkennung mittels praktischer Anwendung der Diskriminanzanalyse. Service Fachverlag an der Wirtschaftsuniversit~t Wien, Augasse 2-6, 1090 Vienna, Austria, 1985.Google Scholar
- Dya91.Dyadic Systems Limited, Riverside View, Basing Road, Old Basing, Basingstoke. Hampshire RG24 0AL, England. Dyalog APL Users Guide for Version 6.1,1991.Google Scholar
- Dya94.Dyadic Systems Limited, Riverside View, Basing Road, Old Basing, Basingstoke, Hampshire RG24 0AL, England. Dyalog APL User Guide, Language Reference, Windows Interface and Outer Products for Version 7.0, 1994.Google Scholar
- ES91.Richard M. Evans and Alvin J. Surkan. Relating Numbers of Processing Elements in a Sparse Distributed Memory Model to Learning Rate and Generalization. A CM APL Quote Quad, 21(4):166-173, 1991. Google ScholarDigital Library
- GTBFS91.P. Gallinari, S. Thiria, F. Badran, and F. Folgelman-Soulie. On the Relations Between Discriminant Analysis and Multilayer Perceptrons. Neural Networks, 4:349-360, 1991. Google ScholarDigital Library
- Hie93.Klaus Hierzenberger. Bericht zur Insovenzstatistik 1993. Kreditschutzverband yon 1870, 1993.Google Scholar
- HKP91.John Hertz, Anders Krogh, and Richard G. Palmer. Introduction to the Theory of Neural Computation. Addison Wesley, Redwood City, California, 1991. Google ScholarDigital Library
- HSW89.Kurt Homik, Maxwell Stinchcombe, and Halbert White. Multilayer Feedforward Networks are Universal Approximators. Neural Networks, 2:359-366, 1989. Google ScholarDigital Library
- KR94.Thomas Kolalik and Gottfried Rudorfer. Time Series Forecasting Using Neural Networks. ACMAPL Quote Qua& 25(1):86-94, 1994. Google ScholarDigital Library
- Nil90.Nils J. Nilsson. The Mathematical Foundations of Learning Machines. Morgan Kaufmann Publishers Inc., San Mateo, 1990. Google ScholarDigital Library
- Pee81.Howard A. Peele. Teaching A Topic in Cybernetics with APL: An Introduction to Neural Net Modelling. A CM APL Quote Quad, 12(1):235-239, 1981. Google ScholarDigital Library
- RHW86.David E. Rumelhart, Cw~offrey E. Hinton, and Ronald J. Williams. Learning representations by backpropagating errors. Nature, 323(9):533- 536, October 1986.Google ScholarCross Ref
- RW94.Gottfried Rudorfer and Harald Wenisch. Isolvenzprognose mit Ktinsflichen Neuronalen Netzen. University of Economics and Business Administration, Augasse 2-6, 1090 Wienna, 1994.Google Scholar
- SS93.Alexei N. Skurihin and Alvin J. Surkan. Identification of Parallelism in Neural Networks by Simulation with Language J. ACM APL Quote Quad, 24(1):230-237, 1993. Google ScholarDigital Library
- TK90.Kar Yan Tam and Melody Kiang. Predicting Bank Failures: A Neural Network Approach. Applied Artificial Intelligence, 4:265-282, 1990. Google ScholarDigital Library
Index Terms
- Early bankruptcy detection using neural networks
Recommendations
Early bankruptcy detection using neural networks
In 1993, Austria had the highest number of bankruptcies since 1945. The total liabilities came to approximately US$3 billion.
Powerful tools for the early detection of company risks are very important to avoid high economic losses. Artificial neural ...
Training Many-to-Many Recurrent Neural Networks with Target Propagation
Artificial Neural Networks and Machine Learning – ICANN 2021AbstractDeep neural networks trained with back-propagation have been the driving force for the progress in fields such as computer vision, natural language processing. However, back-propagation has often been criticized for its biological implausibility. ...
Comparative Fault Tolerance of Parallel Distributed Processing Networks
We propose a method for evaluating and comparing the fault tolerance of a wide variety of parallel distributed processing networks (more commonly referred to as artificial neural networks). Despite the fact that these computing networks are biologically ...
Comments