Predicting CEO Compensation in Non-Controlled Public Corporations with the Canonical Regression Quantile Method

TL;DR

This paper introduces the Canonical Regression Quantiles Index as a novel statistical method to predict CEO compensation and future performance in non-controlled public companies, offering an unbiased alternative to traditional peer group comparisons.

Contribution

The paper presents the Canonical Regression Quantiles Index as a new approach for predicting CEO pay and assessing over/underpayment, potentially replacing subjective peer group methods.

Findings

The Index indicates CEO compensation influences future company performance.

It provides a method to estimate CEO pay for the next 1-2 years.

The method is currently statistically weak but promising with larger data sets.

Abstract

The use of the Canonical Regression Quantiles Index proved that non-controlled companies that engage in long-term operational and financial goals post superior future performance. The Index indicates that current CEO compensation influences future performance. The Index provides a method for determining CEO pay for the next 1-2 year and is a useful method to distinguish over/underpaid CEOs as an unbiased alternative to the peer groups comparison used by most compensation consultants. This determination is statistically weak, but future research using the Canonical Regression Quantiles with a larger data set may lead to increased sensitivity and a powerful unbiased method for replacing compensation consultants who are responsible for the decoupling of CEO compensation and corporate performance.