The Effect of Innovation Similarity on Asset Prices: Evidence from Patents’ Big Data

Ron Bekkerman, Eliezer M. Fich, Natalya V. Khimich

Research output: Contribution to journalReview articlepeer-review


Through textual analyses of 7.7 million patents, we develop a novel intercompany innovation similarity measure which enables us to find that technologically connected firms cross-predict one another’s returns. Investors impound information about firms’ technological connectedness, although not immediately and fully. Buying (shorting) shares of technological peers earning high (low) returns during the previous month yields a 1.29% monthly return. Firms’ return predictability increases with patent complexity or limited technological disclosures but decreases with better information transparency. Results suggest that investor inattention explains technology momentum. Unlike momentum stemming from simpler, class-based technological links, our Big Data text-based return predictability remains active.

Original languageEnglish
Pages (from-to)99-145
Number of pages47
JournalReview of Asset Pricing Studies
Issue number1
StatePublished - 1 Mar 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s) 2022. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved.

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics


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