Nonparametric Bayesian multiple testing for longitudinal performance stratification

TL;DR

This paper introduces a Bayesian nonparametric framework for multiple hypothesis testing on autoregressive time series, enabling robust detection of companies with significantly different long-term performance.

Contribution

It develops a flexible Bayesian nonparametric approach for longitudinal performance stratification, combining frequentist and Bayesian methods for improved Type-I error control.

Findings

Successfully applied to 50 years of corporate data on 24,157 companies

Identified companies with statistically significant performance deviations

Enhanced robustness of multiple testing in longitudinal data

Abstract

This paper describes a framework for flexible multiple hypothesis testing of autoregressive time series. The modeling approach is Bayesian, though a blend of frequentist and Bayesian reasoning is used to evaluate procedures. Nonparametric characterizations of both the null and alternative hypotheses will be shown to be the key robustification step necessary to ensure reasonable Type-I error performance. The methodology is applied to part of a large database containing up to 50 years of corporate performance statistics on 24,157 publicly traded American companies, where the primary goal of the analysis is to flag companies whose historical performance is significantly different from that expected due to chance.