Retrieving, Classifying and Analysing Narrative Commentary in Unstructured (Glossy) Annual Reports Published as PDF Files

El Haj, Mahmoud and Alves, Paulo and Rayson, Paul and Walker, Martin and Young, Steven (2020) Retrieving, Classifying and Analysing Narrative Commentary in Unstructured (Glossy) Annual Reports Published as PDF Files. Accounting and Business Research, 50 (1). pp. 6-34. ISSN 0001-4788

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Abstract

We provide a methodological contribution by developing, describing and evaluating a method for automatically retrieving and analysing text from digital PDF annual report files published by firms listed on the London Stock Exchange (LSE). The retrieval method retains information on document structure, enabling clear delineation between narrative and financial statement components of reports, and between individual sections within the narratives component. Retrieval accuracy exceeds 95% for manual validations using a random sample of 586 reports. Large-sample statistical validations using a comprehensive sample of reports published by non-financial LSE firms confirm that report length, narrative tone and (to a lesser degree) readability vary predictably with economic and regulatory factors. We demonstrate how the method is adaptable to non-English language documents and different regulatory regimes using a case study of Portuguese reports. We use the procedure to construct new research resources including corpora for commonly occurring annual report sections and a dataset of text properties for over 26,000 U.K. annual reports.

Item Type: Journal Article
Journal or Publication Title: Accounting and Business Research
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1400/1402
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
Lancaster University Management School > Accounting & Finance
ID Code: 133328
Deposited By: ep_importer_pure
Deposited On: 09 Jul 2019 09:20
Refereed?: Yes
Published?: Published
Last Modified: 22 Feb 2020 05:05
URI: https://eprints.lancs.ac.uk/id/eprint/133328

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