A Text-Based Analysis of Corporate Innovation

Gustaf Bellstam (), Sanjai Bhagat () and J. Anthony Cookson ()
Additional contact information
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

Management Science, 2021, vol. 67, issue 7, 4004-4031

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.

Keywords: innovation; textual analysis; machine learning; natural language processing; latent Dirichlet allocation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (23)

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