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Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

G. A. Rekha Pai, G. A. Vijayalakshmi Pai
Copyright: © 2010 |Pages: 19
ISBN13: 9781615206292|ISBN10: 1615206299|ISBN13 Softcover: 9781616923105|EISBN13: 9781615206308
DOI: 10.4018/978-1-61520-629-2.ch008
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MLA

Pai, G. A. Rekha, and G. A. Vijayalakshmi Pai. "Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models." Business Intelligence in Economic Forecasting: Technologies and Techniques, edited by Jue Wang and Shouyang Wang, IGI Global, 2010, pp. 138-156. https://doi.org/10.4018/978-1-61520-629-2.ch008

APA

Pai, G. A. & Pai, G. A. (2010). Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models. In J. Wang & S. Wang (Eds.), Business Intelligence in Economic Forecasting: Technologies and Techniques (pp. 138-156). IGI Global. https://doi.org/10.4018/978-1-61520-629-2.ch008

Chicago

Pai, G. A. Rekha, and G. A. Vijayalakshmi Pai. "Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models." In Business Intelligence in Economic Forecasting: Technologies and Techniques, edited by Jue Wang and Shouyang Wang, 138-156. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-629-2.ch008

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Abstract

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.

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