Predicting stock return correlations with brief company descriptions

Abstract

ABSTRACTA series of influential papers by Hoberg and Phillips measure the similarity of pairs of companies based on a textual analysis of their business descriptions and show these measures to be useful in a variety of research contexts in finance. Hoberg and Phillips derive the similarity measures from a comparison of word lists extracted from extensive business descriptions contained in US companies’ electronic 10-K filings. Unfortunately, this method is of little use in non-US settings, where lengthy English-language company self-descriptions are not available on a consistent basis. Instead, we use semantic fingerprinting to extract such similarity measures from much shorter but globally available third-party company descriptions. We show that our approach significantly predicts stock return correlations even after controlling for past correlations and for membership in the same industry. Remarkably, company similarity measures based on brief third-party company descriptions predict stock return correlations significantly better than those based on much longer company self-descriptions.