Abstract
To adequately perform their duties, bank lending officers, financial analysts, and auditors must accurately assess companies’ financial performance. Stockholders also have a financial incentive to monitor companies’ financial performance. Accurately predicting financial distress is an important part of the assessment and monitoring process. For decades, these individuals used traditional statistical techniques such as regression analysis, logit regression (LR) models, or discriminant analysis to try to predict which companies are likely to be healthy and which ones will go bankrupt. In the last several years, one can observe a growing interest in the use of relatively new data mining tools such as artificial neural networks (NNs) for the tasks of prediction, classification, and clustering.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Altman, E.I., Marco, G., and Varetto, F., 1994, Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (the Italian Experience), Journal of Banking and Finance, Vol. 18, No. 3, 505–529.
Back, B., Laitinen, T., and Sere, K., 1996, Neural Networks and Bankruptcy Prediction: Fund Flows, Accrual Ratios, and Accounting Data, Advances in Accounting, Vol. 14, 23–37.
Barney, D.K., Graves, O.F., and Johnson, J.D., 1999, The Farmers Home Administration and Farm Debt Failure Prediction, Journal of Accounting and Public Policy, Vol. 18, 99–139.
Berry, M.J. and Linoff, G.S., 1997, Data Mining Techniques for Marketing, Sales, and Customer Support, John Wiley & Sons, Inc., New York.
Coats, P.K. and Fant, F.L., 1991-1992, A Neural Network Approach to Forecasting Financial Distress, Journal of Business Forecasting, Vol. 10, No. 4, Winter, 9–12.
Coats, P.K. and Fant, F.L., 1993, Recognizing Financial Distress Patterns Using a Neural Network Tool, Financial Management, Vol. 22, September, 142–150.
Fletcher, D. and Goss, E., 1993, Forecasting with Neural Networks: An Application Using Bankruptcy Data, Information & Management, Vol. 24, No. 3, 159–167.
Greenstein, M.M. and Welsh, M.J., 2000, Bankruptcy Prediction Using Ex Ante Neural Network and Realistically Proportioned Testing Sets, Artificial Intelligence in Accounting and Auditing, Vol. 6 (forthcoming).
Jain, A.K., Mao, J., and Mohiuddin, K.M., 1996, Artificial Neural Networks: A Tutorial, Computer, Vol. 29, No. 3, 31–44.
Koh, H.C. and Tan, S.S., 1999, A Neural Network Approach to the Prediction of Going Concern Status, Accounting and Business Research, Vol. 29, No. 3, 211–216.
Lenard, M.J., Alam, P., and Madey, G.R., 1995, The Application of Neural Networks and a Qualitative Response Model to the Auditor’s Going Concern Uncertainty Decision, Decision Sciences, Vol. 26, No. 2, 209–227.
Salchenberger, L.M., Cinar, E.M., and Lash, N.A., 1992, Neural Networks: A New Tool for Predicting Thrift Failures, Decision Sciences, Vol. 23, No. 4, 899–916.
Ward, T.J. and Foster, B.P., 1996, An Empirical Analysis of Thomas’s Financial Accounting Allocation Fallacy Theory in a Financial Distress Context, Accounting and Business Research, Vol. 26, No. 2, 137–152.
Wilson, R.L. and Sharda, R., 1994, Bankruptcy Prediction Using Neural Networks, Decision Support Systems, Vol. 11,545–557.
Zurada, J., Foster, B.P., Ward, T.J., and Barker, R.M., 1997, A Comparison of the Ability of Neural Networks and Logit Regression Models to Predict Levels of Financial Distress, Systems Development Methods for the 21 st Century, (G. Wojtkowski, W. Wojtkowski, S. Wrycza, and J. Zupancic, eds.), Plenum Press, New York, pp. 291–295.
Zurada, J., Foster, B.P., Ward, T.J., and Barker, R.M., Winter 1998-1999, Neural Networks versus Logit Regression Models for Predicting Financial Distress Response Variables, The Journal of Applied Business Research, Vol. 15, No. 1, 21–29.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer Science+Business Media New York
About this chapter
Cite this chapter
Zurada, J., Foster, B.P., Ward, T.J. (2001). Artificial Neural Networks in Predicting a Dichotomous Level of Financial Distress for Uneven Training and Testing Samples. In: Sein, M.K., et al. Contemporary Trends in Systems Development. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1341-4_15
Download citation
DOI: https://doi.org/10.1007/978-1-4615-1341-4_15
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5506-9
Online ISBN: 978-1-4615-1341-4
eBook Packages: Springer Book Archive