Publication Type
Journal Article
Version
publishedVersion
Publication Date
12-2019
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
Discipline
Management Sciences and Quantitative Methods | Technology and Innovation
Research Areas
Finance
Publication
International Journal of Management and Applied Science
Volume
5
Issue
12
First Page
37
Last Page
40
ISSN
2394-7926
Citation
LIM, Kian Guan and LIM, Michelle S. J..
Machine learning using instruments for text selection: Predicting innovation performance. (2019). International Journal of Management and Applied Science. 5, (12), 37-40.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/6988
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.