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

Ron Bekkerman, Eliezer M Fich, Natalya V Khimich and Jeffrey Pontiff

The Review of Asset Pricing Studies, 2023, vol. 13, issue 1, 99-145

Abstract: 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. (JEL G11, G12, G14, O31, C55)Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Date: 2023
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