GASP 2020 - Gizem Korkmaz

Gizem Korkmaz- Biocomplexity Institute, University of Virginia

Devika Mahoney-Nair- Biocomplexity Institute & Initiative, University of Virginia ,

Neil Alexander Kattampallil- Biocomplexity Institute & Initiative, University of Virginia,

Gary Anderson -National Center for Science and Engineering Statistics, National Science Foundation

Title: Detecting Pharmaceutical Innovations in News Articles and Financial Filings

Abstract:

Innovation is traditionally measured through surveys of selected companies such as the Business R&D and Innovation Survey (BRDIS) that focuses on innovation incidence, i.e., the number of innovating firms. This paper aims to develop natural language processing and machine learning methods to measure business innovation using non-traditional data sources to enrich and complement innovation measures obtained through these surveys.

We focus on product innovation in the pharmaceutical sector (drugs and medical devices) that is heavily regulated by the Food and Drug Administration (FDA). The non-traditional data sources include publicly available opportunity and administrative data such as financial filings, and news articles obtained from Dow Jones, a business news and data provider. We collect and scrape around 3K filings from the U.S. Securities and Exchange Commission (SEC), and parse 2M news articles, and develop text-mining methods

to link datasets by companies. We develop machine learning and natural language processing methods to estimate the number of new products companies introduce to the market. Our findings are compared to the publicly available approval data provided by the FDA to study the fraction of innovation activity that could be captured using these novel data sources.

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