Selecting bankruptcy predictors using a support vector machine approach

Marimuthu Swami Palaniswami

108 citations0/0 files storedInternational Joint Conference on Neural Networks (IJCNN)

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Financial Distress and Bankruptcy Prediction Corporate Insolvency and Governance Artificial Intelligence in Healthcare

Abstract

The conventional neural network approach has been found useful in predicting corporate distress from financial statements. We have adapted a support vector machine approach to the problem. A way of selecting bankruptcy predictors is shown, using the Euclidean distance based criterion calculated within the SVM kernel. A comparative study is provided using three classical corporate distress models and an alternative model based on the SVM approach.

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1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)1993Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting & Avoiding Distress and Profiting from Bankruptcy 1995Effectiveness of neural network types for prediction of business failure

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