51 Pages Posted: 1 Jul 2016 Last revised: 4 Jul 2016
Date Written: July 1, 2016
We develop, describe and evaluate a web-based software tool for batch extraction and analysis of digital PDF annual report files. The retrieval method retains information on document structure thereby enabling clear delineation between narrative and financial statement components of reports, and between individual sections within the narratives component. Retrieval accuracy exceeds 95% in manual validations and large-sample tests confirm that extracted content varies predictably with economic and regulatory factors. We apply the tool to a comprehensive sample of reports published by U.K. non-financial firms between 2003 and 2014, and examine the incremental predictive power for future earnings of different performance sections from the same report. While performance-related commentaries prepared by management and the independent board chair are individually predictive for future earnings, only chairman-authored content is incrementally informative when considered jointly. Further, management-authored content has lower independent predictive ability when insiders are more optimistic than the board chair. Results support the view that the predictive power of narratives varies with authors’ reporting incentives and that exaggerated optimism in management commentary reflects obfuscation.
Keywords: Annual Reports, Textual Analysis, Text Extraction, Predictive Ability
JEL Classification: M40, M41
Suggested Citation: Suggested Citation
Alves, Paulo and El-Haj, Mahmoud and Rayson, Paul and Walker, Martin and Young, Steven, Heterogeneous Narrative Content in Annual Reports Published as PDF Files: Extraction, Classification and Incremental Predictive Ability (July 1, 2016). Available at SSRN: https://ssrn.com/abstract=2803275 or http://dx.doi.org/10.2139/ssrn.2803275