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Patent Valuation with Forecasts of Forward Citations

Author

Listed:
  • Falk Nathan

    (Claremont McKenna College, Claremont, CA, USA)

  • Train Kenneth

    (University of California, Berkeley, CA, USA)

Abstract

Patents without established market values (e. g., no negotiated royalty rates) are often valued by comparing the number of citations the patent has received to the numbers received by other patents whose market values are established. For recently-issued patents, which have not had time to accumulate citations, this procedure can be noisy or even inapplicable. The current paper generalizes this valuation method to incorporate patent characteristics that relate to the number of citations the patent is expected to obtain in the future. We estimate statistical models in which the explanatory variables are observable characteristics of the patent at a given time, and the dependent variable is the number of citations that the patent receives after that date. Using several examples, we demonstrate a procedure for patent valuation that incorporates the statistical results, such that the valuation reflects the number of citations the patent has already received as well as the number it is expected, based on its characteristics, to receive in the future.

Suggested Citation

  • Falk Nathan & Train Kenneth, 2017. "Patent Valuation with Forecasts of Forward Citations," Journal of Business Valuation and Economic Loss Analysis, De Gruyter, vol. 12(1), pages 101-121, February.
    • Handle: RePEc:bpj:jbvela:v:12:y:2017:i:1:p:101-121:n:4
    DOI: 10.1515/jbvela-2016-0002
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    References listed on IDEAS

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    Cited by:

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    2. Yang, Guancan & Lu, Guoxuan & Xu, Shuo & Chen, Liang & Wen, Yuxin, 2023. "Which type of dynamic indicators should be preferred to predict patent commercial potential?," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    3. Keungoui Kim & Alberto Nonnis & Altay Özaygen & Dieter F. Kogler, 2023. "Green-tech firm creation in Germany: the role of regional knowledge," International Entrepreneurship and Management Journal, Springer, vol. 19(1), pages 97-120, March.
    4. Wang, Lin & Tang, Yingkai & Chen, Yaozhi & Wang, Kun, 2021. "Be a better boss. Employee treatment, trust level and family business innovation: Evidence from China," Research in International Business and Finance, Elsevier, vol. 58(C).
    5. Chung, Park & Sohn, So Young, 2020. "Early detection of valuable patents using a deep learning model: Case of semiconductor industry," Technological Forecasting and Social Change, Elsevier, vol. 158(C).

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