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Using machine learning and 10-K filings to measure innovation

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Název:	Using machine learning and 10-K filings to measure innovation
Autoři:	Nousiainen, Essi, Ranta, Mikko, Ylinen, Mika, Järvenpää, Marko
Zdroj:	Accounting and Finance. 64(4):3211-3239
Témata:	10-K, disclosure of innovation, innovation, text analysis, topic modelling
Popis:	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.
Popis souboru:	print
Přístupová URL adresa:	https://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-70030 https://doi.org/10.1111/acfi.13245
Databáze:	SwePub

Popis
Abstrakt:	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