利用机器学习和 10-K 申报衡量创新

Essi Nousiainen, Mikko Ranta, M. Ylinen, Marko Järvenpää
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引用次数: 0

摘要

本文的目的是在美国上市公司 45,409 份 10-K 文件样本中,利用潜在 Dirichlet 分配法开发并验证一种基于文本的创新度量方法。我们希望基于文本的创新度量与创新相关联,并可用于度量没有专利或大量研发支出的公司的创新情况。实证结果与这些假设相符,但表明需要进行全面的初步测试以确保稳健性。本研究扩展了有关创新衡量和公司信息披露的研究,并提供了一种利用公司信息披露评估创新的新方法。
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Using machine learning and 10‐K filings to measure innovation
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.
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