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

62 Pages Posted: 28 Mar 2018 Last revised: 8 Jun 2021

See all articles by Ron Bekkerman

Ron Bekkerman

Cherre Inc

Eliezer M. Fich

Drexel University - Department of Finance

Natalya V. Khimich

Drexel University

Date Written: June 7, 2021

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. These results suggest investor inattention explains technology momentum. Unlike momentum stemming from simpler class-based technological links, our Big Data text-based return predictability remains active.

Keywords: Big Data; Textual analysis; Patents; Innovation similarity; Investor attention; Return predictability; Technological diversification

JEL Classification: G11; G12; G14; O31; C55

Suggested Citation

Bekkerman, Ron and Fich, Eliezer M. and Khimich, Natalya V., The effect of Innovation Similarity on Asset Prices: Evidence from Patents’ Big Data (June 7, 2021). Available at SSRN: https://ssrn.com/abstract=3147218 or http://dx.doi.org/10.2139/ssrn.3147218

Ron Bekkerman

Cherre Inc

575 8th Ave
New York, NY 10018
United States

HOME PAGE: http://cherre.com/

Eliezer M. Fich

Drexel University - Department of Finance ( email )

LeBow College of Business
3220 Market Street – 11th Floor
Philadelphia, PA 19104
(215) 895-2304 (Phone)

Natalya V. Khimich (Contact Author)

Drexel University ( email )

3141 Chestnut St
Philadelphia, PA 19104
United States

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