This website provides information on LSEG Guidance Reports (GR) data and research.
What are LSEG Guidance Reports (GR)?
Guidance Reports (GR) are produced by LSEG and contain raw as-reported corporate issued guidance. GR content is sourced from company public disclosures, such as conference calls, press releases, analyst days, and industry conferences. Academic scholars can obtain GR data via LSEG Workspace with LSEG license.
The paper below provides an overview of the GR data and discusses directions for future research:
Mayew, W. J., Pinto, J., & Wu, X. (2024). On the Usefulness of Guidance Reports. Working Paper. Available at SSRN: link
Abstract:
Scholars commonly measure corporate guidance in large samples using either the I/B/E/S Guidance (IG) database or by deriving guidance from forward-looking statements (FLS) in corporate disclosures. Prior research notes that IG often fails to capture some firm guidance, whereas the FLS methodology often struggles to correctly identify specific guidance instances at the sentence level. We introduce LSEG Guidance Reports (GR) as a way to better operationalize firm guidance. We analyze more than 23,000 GR that contain 1.7 million guidance instances across 192 topics. We find that this measure of guidance far surpasses IG's coverage, of 261 thousand guidance instances across 13 topics, for the same firm-years. We then contrast GR with guidance using the FLS methodology. We identify guidance topics that impact analyst earnings forecast accuracy but that prior research using the FLS methodology was unable to identify. Finally, we study analyst perceptions of firm guidance by documenting associations between analyst perceptions of investor relations quality and forms of guidance. We find that analysts value quantitative financial guidance, which assists in valuation modeling, and qualitative nonfinancial guidance, which elucidates their investment theses. These insights into analyst perceptions are not obtainable using IG or the FLS methodology.
If you have any questions about GR data in our paper, please feel free to contact Xiaoxi Wu (xiaoxi.wu@unibocconi.it).Â
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