Fad or future? Automated analysis of financial text and its implications for corporate reporting
Year: 2019
Type: article
Abstract: This paper describes the current state of natural language processing (NLP) as it applies to corporate reporting. We document dramatic increases in the quantity of verbal content that is an integral part of company reporting packages, as well as the evolution of text analytic approaches being employed to analyse this content. We provide intuitive descriptions of the leading analytic approaches applied in the academic accounting and finance literatures. This discussion includes key word searches and counts, attribute dictionaries, naïve Bayesian classification, cosine similarity, and latent Dirichlet allocation. We also discuss how increasing interest in NLP processing of the corporate reporting package could and should influence financial reporting regulation and note that textual analysis is currently more of an afterthought, if it is even considered. Opportunities for improving the usefulness of NLP processing are discussed, as well as possible impediments.
Source: Accounting and Business Research
Authors: Craig Lewis, Steven Young
Institutions: Vanderbilt University, Lancaster University
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,970
Awards: Understanding the Influences of Financial Reporting, Corporate Disclosures and Financial Media on the Corporate Financial Information Environment, ESRC Centre for Corpus Approaches to Social Science (CASS), Analysing Narrative Aspects of UK Preliminary Earnings Announcements and Annual Reports: Tools and Insights for Researchers and Regulators, ESRC Centre for Corpus Approaches to Social Science (Transition Review), Intelligent Language Processing for Understanding Financial Text
OpenAlex ID: W2947060296