This paper presents an alternate method for transforming financial ratios. Ratios are ranked and scaled into a uniform distribution with boundaries between 0 and 1. Conceptually, we suggest that this method solves a number of methodological problems associated with ratios, including constrained choice of regression models, ratio outliers, negative ratios, and non-normal distributions. Scaled ranks of financial ratios are also conceptually appealing because they appear to capture comparative ordinal data about cross-sectional relationships between firms.
The study empirically tests scaled rank transformations by examining the association of the transformations with stock returns. Results show that models using relative ranked accounting ratios have more explanatory and predictive power than untransformed, log-transformed and square-root transformed ratios.
Rank(s) - transformation(s) - regression analysis - analysis - ratio(s)