Trade policy uncertainty and the patent bubble in China: evidence from machine learning

Author

Xingnan XUE
Peng LIANG
Fujing XUE
Nan HU, Singapore Management UniversityFollow
Ling LIU

Publication Type

Journal Article

Publication Date

1-2024

Abstract

This paper draws upon resource dependence theory and investigates how trade policy uncertainty affects firm strategic innovation management in China. Adopting a novel machine learning approach called Word2Vec, we construct and validate a measure of firm-level managers’ perceived trade policy uncertainty (TPU). We find that TPU has a positive effect on the number of total patent applications, but this positive effect is totally driven by low-quality patents instead of high-quality patents. Moreover, we document that firms have stronger incentives for such strategic inno-vation behavior when the underlying firms are more financially con-strained, and/or when the management is more myopic.

Keywords

Machine learning, patent bubble, Resource dependence theory, strategic innovation management, Trade policy uncertainty

Discipline

Asian Studies | Databases and Information Systems

Research Areas

Information Systems and Management

Publication

Asia-Pacific Journal of Accounting & Economics

First Page

1

Last Page

22

ISSN

1608-1625

Identifier

10.1080/16081625.2023.2298934

Publisher

Taylor and Francis Group

Citation

XUE, Xingnan; LIANG, Peng; XUE, Fujing; HU, Nan; and LIU, Ling. Trade policy uncertainty and the patent bubble in China: evidence from machine learning. (2024). Asia-Pacific Journal of Accounting & Economics. 1-22.
Available at: https://ink.library.smu.edu.sg/sis_research/8662

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1080/16081625.2023.2298934