Machine learning using instruments for text selection: Predicting innovation performance

• Main Authors: LIM, Kian Guan, LIM, Michelle S. J.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Machine Learning; R&D Reporting; Textual Analyses; Firm Innovation; Management Sciences and Quantitative Methods; Technology and Innovation
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/6988; https://ink.library.smu.edu.sg/context/lkcsb_research/article/7987/viewcontent/14_622_158107802237_40_pvoa.pdf
• Institution: Singapore Management University

In machine learning we utilize the idea of employing instrumental variable such as patent records to train the texts. Patent records are highly correlated with R&D expenditures, but are not necessarily correlated with performance residuals not linked to R&D. Thus, using instrumental patent records to train word counts of selected texts to serve as a proxy for firm R&D expenditure, we show that the texts and associated word counts provide effective prediction of firm innovation performances such as firm market value and total sales growth.