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This paper examines historical patterns of ROA (return on assets) for acohort of 53,038 publicly traded firms across 93 countries, measured over thepast 45 years. Our goal is to screen for firms whose ROA trajectories suggestthat they have systematically outperformed their peer groups over time. Such aproject faces at least three statistical difficulties: adjustment for relevantcovariates, massive multiplicity, and longitudinal dependence. We concludethat, once these difficulties are taken into account, demonstrably superiorperformance appears to be quite rare. We compare our findings with other recentmanagement studies on the same subject, and with the popular literature oncorporate success. Our methodological contribution is to propose a new class ofpriors for use in large-scale simultaneous testing. These priors are based onthe hypergeometric inverted-beta family, and have two main attractive features:heavy tails and computational tractability. The family is a four-parametergeneralization of the normal/inverted-beta prior, and is the natural conjugateprior for shrinkage coefficients in a hierarchical normal model. Our resultsemphasize the usefulness of these heavy-tailed priors in large multiple-testingproblems, as they have a mild rate of tail decay in the marginal likelihood$m(y)$---a property long recognized to be important in testing.

Good, great, or lucky? Screening for firms with sustained superior performance using heavy-tailed priors