A Text-Based Analysis of Corporate Innovation

• 成果类型: Article
• 署名作者: Bellstam, Gustaf; Bhagat, Sanjai; Cookson, J. Anthony
• 署名单位: Facebook Inc; University of Colorado System; University of Colorado Boulder
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3682
• 发表日期: 2021
• 页码: 4004-4031
• 关键词: innovation; Textual analysis; Machine Learning; Natural Language Processing; latent Dirchlet allocation

摘要

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.