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A Text-Based Analysis of Corporate Innovation

Author

Listed:
  • Gustaf Bellstam

    (Facebook, Seattle, Washington 98109)

  • Sanjai Bhagat

    (Leeds School of Business, University of Colorado, Boulder, Colorado 80309)

  • J. Anthony Cookson

    (Leeds School of Business, University of Colorado, Boulder, Colorado 80309)

Abstract

We develop a new measure of innovation using the text of analyst reports of S&P 500 firms. Our text-based measure gives a useful description of innovation by firms with and without patenting and R&D (research and development). For nonpatenting firms, the measure identifies innovative firms that adopt novel technologies and innovative business practices (e.g., Walmart’s cross-geography logistics). For patenting firms, the text-based measure strongly correlates with valuable patents, which likely capture true innovation. The text-based measure robustly forecasts greater firm performance and growth opportunities for up to four years, and these value implications hold just as strongly for innovative nonpatenting firms.

Suggested Citation

  • Gustaf Bellstam & Sanjai Bhagat & J. Anthony Cookson, 2021. "A Text-Based Analysis of Corporate Innovation," Management Science, INFORMS, vol. 67(7), pages 4004-4031, July.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:7:p:4004-4031
    DOI: 10.1287/mnsc.2020.3682
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    References listed on IDEAS

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    Cited by:

    1. Xu, Yong & Yuan, Ling & Khalfaoui, Rabeh & Radulescu, Magdalena & Mallek, Sabrine & Zhao, Xin, 2023. "Making technological innovation greener: Does firm digital transformation work?," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    2. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, March.
    3. Fengler, Matthias & Phan, Minh Tri, 2023. "A Topic Model for 10-K Management Disclosures," Economics Working Paper Series 2307, University of St. Gallen, School of Economics and Political Science.
    4. Li, Fengyu & Yang, Mozhu & Zhang, Tong, 2023. "Does prospectus readability matter for bond issuance pricing? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    5. Jiang, Hongxun & Fan, Shaokun & Zhang, Nan & Zhu, Bin, 2023. "Deep learning for predicting patent application outcome: The fusion of text and network embeddings," Journal of Informetrics, Elsevier, vol. 17(2).

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