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Machine learning using instruments for text selection: Predicting innovation performance

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journal contribution
posted on 30.03.2021, 12:49 by Kian Guan LIM, Michelle S. J. LIM

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.

History

Publication Date

01/12/2019

Journal

International Journal of Management and Applied Science

Volume

5

Issue

12

Pages

37-40

ISSN

2394-7926

School

Lee Kong Chian School of Business

IRIS ID

109776888

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