DOIONLINE 
DOIONLINE Publish In |
International Journal of Management and Applied Science (IJMAS)-IJMAS |
 Journal Home Volume Issue |
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Issue |
Volume-5,Issue-12 ( Dec, 2019 ) | |||||||||
Paper Title |
Machine Learning using Instruments for Text Selection: Predicting Innovation Performance | |||||||||
Author Name |
Kian-Guan Lim, Michelle S.J. Lim | |||||||||
Affilition |
Singapore Management University | |||||||||
Pages |
37-40 | |||||||||
Abstract |
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. Keywords - Machine Learning; R&D Reporting; Textual Analyses; Firm Innovation | |||||||||
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