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

Author

Listed:
  • Ron Bekkerman
  • Eliezer M Fich
  • Natalya V Khimich
  • Jeffrey Pontiff

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.

Suggested Citation

  • Ron Bekkerman & Eliezer M Fich & Natalya V Khimich & Jeffrey Pontiff, 2023. "The Effect of Innovation Similarity on Asset Prices: Evidence from Patents’ Big Data," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 13(1), pages 99-145.
    • Handle: RePEc:oup:rasset:v:13:y:2023:i:1:p:99-145.
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    File URL: http://hdl.handle.net/10.1093/rapstu/raac014
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    Citations

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    Cited by:

    1. Jiang, Christine & Wu, Yiyin & Zhu, John Qi, 2025. "A revisit to the IPO spillover effect: On the importance of technological proximity," Journal of Banking & Finance, Elsevier, vol. 181(C).
    2. Eric McKee & Yuanyang Liu & Gautam Pant & Shagun Pant, 2025. "Human capital migration networks and stock returns," Review of Financial Economics, John Wiley & Sons, vol. 43(3), pages 297-316, July.
    3. Dragos Gorduza & Yaxuan Kong & Xiaowen Dong & Stefan Zohren, 2024. "Extracting Alpha from Financial Analyst Networks," Papers 2410.20597, arXiv.org.
    4. Lan, Qiujun & Li, Haojie & Mi, Xianhua & Zhang, Chunyu, 2025. "Optimizing investment strategies: Harnessing the power of K-line complex networks," International Review of Economics & Finance, Elsevier, vol. 99(C).
    5. Glaeser, Stephen & Lang, Mark, 2024. "Measuring innovation and navigating its unique information issues: A review of the accounting literature on innovation," Journal of Accounting and Economics, Elsevier, vol. 78(2).
    6. Jiang, Wenbo & Hong, Yong & Cui, Miao & Qu, Yongyi, 2026. "To be similar or unique? Exploring firms' pursuit of optimal technological relatedness to enhance technological impact: The role of firm size and competitor density," Technological Forecasting and Social Change, Elsevier, vol. 222(C).
    7. Lin, Mei-Chen, 2024. "Shared analyst coverage, 52-week high, and cross-firm return predictability," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    8. Lisheng Yu & Zehao Wang & Wentao Li, 2026. "Analysts' Technological Proximity Knowledge and Earnings Forecasts," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 66(1), pages 238-258, March.

    More about this item

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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