Essays on Corporate Textual Disclosure
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Compilation dissertation's summary section is licensed under Creative Commons Attribution 4.0 International.
This is a compilation dissertation exploring textual accounting disclosures with a focus on innovation, ESG (environmental, social and governance) and blockchain technology disclosures. Using computational methods such as Latent Dirichlet Allocation (LDA) and sentiment analysis, the research analyzes voluntary disclosures in U.S. public companies' 10-K and S-1 filings. The study highlights the importance of non-financial disclosures in corporate reporting and their role in a broader context. The dissertation draws from legitimacy and signaling theories to explain why companies engage in voluntary disclosure despite its cost and the lack of statutory requirements.
In Essay I, innovation disclosure is explored as an alternative to traditional patent-based methods for measuring a company's innovative activities. The research proposes and validates a novel text-based approach that identifies innovation in firms and without relying on patents. Essay II investigates ESG disclosures, revealing that companies seeking acquisitions often increase their ESG-related disclosures without a corresponding improvement in their actual ESG performance. This highlights the strategic use of ESG disclosures as a tool to enhance market perceptions and legitimacy, even when the underlying performance does not align with the narrative. Blockchain disclosures are analyzed in Essay III to understand how emerging companies present their engagement with this technology during the IPO process. The research reveals a shift towards more cautious and uncertain language, especially in relation to cryptocurrencies, reflecting market and regulatory challenges. However, confidence in blockchain technology solutions has increased over time.
Overall, this dissertation contributes to the accounting literature on non-financial disclosure by providing new knowledge on textual disclosure and its implications for society, investors, stakeholders and the companies themselves. Furthermore, the research highlights the importance of transparency in voluntary disclosures and the potential of machine learning and natural language processing techniques to provide more nuanced assessments of innovation, ESG, and technology topics. Understanding the textual disclosure of companies in transitional phases can be insightful to investors and stakeholders.
URI
DOI
Parent publication
ISBN
978-952-395-187-7
ISSN
2323-9123
0355-2667
0355-2667
Topic
Series
Acta Wasaensia|552
MinEdu publication type
G5 Doctoral dissertation (article)
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