Predicting stock return correlations with brief company descriptions

Feriha Ibriyamova, Samuel Kogan, Galla Salganik-Shoshan, David Stolin

Research output: Contribution to journalArticlepeer-review

Abstract

A 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.

Original languageEnglish
Pages (from-to)88-102
Number of pages15
JournalApplied Economics
Volume51
Issue number1
DOIs
StatePublished - 2 Jan 2019

Keywords

  • Textual analysis
  • industries
  • semantic fingerprint
  • stock return correlations

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