Value Relevance Of Textual Characteristics: Evidence from Annual Reports of U.S. Listed Firms(SIDREA Series in Accounting and Business Administration)

This book explores the evolution of value relevance in finance and accounting. While synthesizing existing literature to provide a robust theoretical framework, it also extends the discourse by incorporating textual characteristics into the traditional model of value relevance. The authors integrate two pivotal but often disparate research domains: value relevance and linguistic analysis in accounting disclosures. This inclusion enriches our understanding of how qualitative factors, such as tone and readability in corporate disclosures, interact with quantitative measures to influence market behavior and investor decision-making. The result is a nuanced, multi-dimensional analysis that not only bridges gaps in current academic research but also offers new avenues for practical application.  The book starts by laying the groundwork for the concept of value relevance, discussing its evolution in the academic literature, theoretical models, their assumptions, and limitations. It then turns the spotlight onto corporate disclosure theories, thereby illuminating the strategies and psychological factors that influence how companies report their financials and accompanying narrative discussions. The authors demonstrate how the information is strategically disclosed and received, adding another layer of complexity to the concept of value relevance. The novel contribution of this manuscript comes from the methodological approach, incorporating textual analysis techniques into a widely-accepted Ohlson value relevance model. The authors empirically integrate textual elements like tone and readability and demonstrate how they can play a significant role in how investors perceive a company. The empirical section of the book provides robust evidence to support the central thesis—that qualitative elements in financial disclosures are not merely supplementary but are indeed value-relevant. This enhances the book's applicability across both academic and practical settings. In conclusion, this book serves as a comprehensive guide for those interested in the complexities of value relevance as well as the dynamics of financial disclosures. It offers valuable insights that are both theoretically robust and empirically validated, making it a useful resource for academics, practitioners, and regulators. It enriches the academic discourse by offering new pathways for research and practical application in a world where financial information is increasingly digitized.  The target audience for this book includes academics in the fields of accounting and finance, as well as practitioners and regulators who are keen on understanding the evolving dynamics of how financial information impacts the capital markets. 

Table of Contents:
Introduction.- Theoretical Framework of Value Relevance.- Disclosure Theories.- Textual Analysis.- Measures in Finance and Accounting.- The Value Relevance of Textual Analysis.- Empirical Analysis of the Annual Reports.- Conclusion.

About the Author :
Olga Bogachek is an Assistant Professor of Accounting at the Free University of Bolzano (Italy) since 2019. She holds a Ph.D. in Economics and Finance from Bocconi University, as well as a BSc. and a MSc. in Mathematics and an MBA from Lobachevsky University, Russia. Prior to academia, Olga has over 15 years of professional experience with Intel in the US and Europe, in various financial, compliance and strategic roles. Her current research interests blend her corporate expertise and rigorous mathematical knowledge and focus on auditing, textual analysis of disclosures and economic consequences of blockchain technology. Her research has been published in the Journal of Risk Research, presented at international conferences, and featured in various news outlets.  Francesco Grossetti is an Assistant Professor of Accounting Analytics and Data Science at Bocconi University in Milano (Italy). He graduated in Astrophysics and Space Physics from Università Degli Studi di Milano-Bicocca and obtained a Ph.D. in Applied and Computational Statistics from Politecnico di Milano. His research employs computational and statistical approaches to gain insights into the behavior of companies and investors' perceptions. Specifically, he utilizes and enhances Natural Language Processing techniques and language models to examine the impact of financial and non-financial disclosures on key financial performance. Additionally, he leverages Deep Learning-based Computer Vision methods to extract visual information from financial disclosures. Furthermore, he investigates the economic implications of Blockchain and other Distributed Ledger Systems, exploring their potential to foster novel research avenues and revolutionize accounting practices. Francesco also actively contributes to the development of open-source tools.