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

Copyright Owner and License

Authors

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