In EDS ansehen

Using machine learning and 10-K filings to measure innovation

Gespeichert in:
Bibliographische Detailangaben
Titel: Using machine learning and 10-K filings to measure innovation
Autoren: Nousiainen, Essi, Ranta, Mikko, Ylinen, Mika, Järvenpää, Marko
Quelle: Accounting and Finance. 64(4):3211-3239
Schlagwörter: 10-K, disclosure of innovation, innovation, text analysis, topic modelling
Beschreibung: The purpose of this paper is to develop and validate a text-based measure of innovation using latent Dirichlet allocation on a sample of 45,409 10-K filings from US listed companies. We expect that the text-based innovation measure is associated with innovation and can be used to measure innovation for companies without patents or significant research and development expenditures. The empirical results are consistent with these assumptions, but reveal that thorough initial testing is required to ensure robustness. This study extends the research on innovation measurement and company disclosures, and provides a new method for assessing innovation using company disclosures.
Dateibeschreibung: print
Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-70030
https://doi.org/10.1111/acfi.13245
Datenbank: SwePub
Beschreibung
Abstract:The purpose of this paper is to develop and validate a text-based measure of innovation using latent Dirichlet allocation on a sample of 45,409 10-K filings from US listed companies. We expect that the text-based innovation measure is associated with innovation and can be used to measure innovation for companies without patents or significant research and development expenditures. The empirical results are consistent with these assumptions, but reveal that thorough initial testing is required to ensure robustness. This study extends the research on innovation measurement and company disclosures, and provides a new method for assessing innovation using company disclosures.
ISSN:08105391
1467629X
DOI:10.1111/acfi.13245