Rank Transformations and the Prediction of Corporate Failure*
研究将秩变换应用于财务比率，以改进标准失败预测模型的预测效果，发现秩变换数据模型能在不增加预测衰退成本的情况下提升预测准确性。
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.