Using Canonical Quantile Regression to predict company performance: better prediction than using CEO compensation

Abstract

In using multiple regression methods for prediction, one often considers the linear combination of explanatory variables as an index. Seeking a single such index when there are multiple responses is rather more complicated. One classical approach is to use the coefficients from the leading Canonical Correlation. However, methods based on variances are unable to disaggregate responses by quantile effects, lack robustness, and rely on normal assumptions for inference. To address these problems, a novel regression quantile approach will be applied to an empirical study of the performance of large publicly held companies and CEO compensation.