Benchmarking Historical Corporate Performance

TL;DR

This paper introduces a Bayesian tree model approach for benchmarking corporate performance across diverse industries, enabling formal comparisons and identifying outliers, revealing that systematic outperformance is rare globally.

Contribution

It develops a novel Bayesian tree modeling framework for benchmarking in complex data settings with diverse dependence structures.

Findings

Systematic outperformance among firms is rare worldwide.

The method effectively identifies firms outperforming their peer groups.

Bayesian models provide a formal basis for cross-industry corporate performance comparison.

Abstract

This paper uses Bayesian tree models for statistical benchmarking in data sets with awkward marginals and complicated dependence structures. The method is applied to a very large database on corporate performance over the last four decades. The results of this study provide a formal basis for making cross-peer-group comparisons among companies in very different industries and operating environments. This is done by using models for Bayesian multiple hypothesis testing to determine which firms, if any, have systematically outperformed their peer groups over time. We conclude that systematic outperformance, while it seems to exist, is quite rare worldwide.