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Rank Transformations and the Prediction of Corporate Failure *
Author(s) -
KANE GREGORY D.,
RICHARDSON FREDERICK M.,
MEADE NANCY L.
Publication year - 1998
Publication title -
contemporary accounting research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.769
H-Index - 99
eISSN - 1911-3846
pISSN - 0823-9150
DOI - 10.1111/j.1911-3846.1998.tb00553.x
Subject(s) - outlier , rank (graph theory) , econometrics , recession , transformation (genetics) , statistics , mathematics , economics , keynesian economics , chemistry , combinatorics , gene , biochemistry
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.