Article1998

Rank Transformations and the Prediction of Corporate Failure*

Gregory D. Kane · Frederick M. Richardson · NANCY L. MEADE

50 citations0/0 files storedContemporary Accounting Research

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Financial Distress and Bankruptcy Prediction Corporate Insolvency and Governance Forecasting Techniques and Applications

Abstract

Abstract Rank transformation of observations has been shown to be useful in linear modeling because the models so constructed are less sensitive to outliers and/or non‐normal distributions than are models constructed using standard methods. In the present study, we apply rank transformations to financial ratios to improve the predictive usefulness of standard failure prediction models. Kane, Richardson, and Graybeal (1996) have shown that failure prediction can be improved by conditioning accounting‐based statistical models on the occurrence of recession. Our results suggest that rank‐ transformed data models show additional improvement in prediction without the added cost of having to predict recession for the companies undergoing testing for potential failure.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 1986Predicting takeover targets 1972A Discriminant Analysis of Predictors of Business Failure 1974Failing Company Discriminant Analysis 1994A Reexamination of Auditor versus Model Accuracy within the Context of the Going‐Concern Opinion Decision*1976Distributions of Financial Accounting Ratios: Some Empirical Evidence.

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2023A literature survey of corporate failure prediction models 1998Ratio Analysis Using Rank Transformation 2002The Interaction of Solvency with Liquidity and its Association with Bankruptcy Emergence 2007Improving performance of corporate rating prediction models by reducing financial ratio heterogeneity 2005An Indonesian study of the use of CAMEL(S) ratios as predictors of bank failure 2008

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