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. (2025). On the Usefulness of Guidance Reports. Working Paper. Available at SSRN: link
Abstract:
We examine whether LSEG Guidance Report (GR) data are useful for scholars studying management guidance. We extract the contents of over 23,000 Guidance Reports for S&P 1500 firms. We find 1.735 million guidance instances across 192 items, which far exceeds the 261 thousand instances across 13 items included in the LSEG I/B/E/S Guidance (IG) database that scholars commonly rely on. IG is a processed subset of GR containing only quantitative guidance that is standardized to be comparable to analyst forecasts. Qualitative (quantitative) GR guidance explains 76% (24%) of the coverage gap. Quantitative GR guidance is less likely to be processed into IG when standardization costs are high and the importance of the guided item to managers and analysts is low. Qualitative and quantitative GR guidance data absent from IG are associated with analyst forecast revisions and market reactions, consistent with GR capturing guidance of economic importance. GR also uniquely identifies disclosure channels and speakers, revealing that nearly one-quarter of guidance occurs outside traditional earnings announcements and that a variety of top management team members deliver guidance. These findings suggest that GR data can enhance academics’ understanding of management guidance beyond what can be learned from IG data alone.
If you have any questions about GR data in our paper, please feel free to contact Xiaoxi Wu (xiaoxi.wu@unibocconi.it).
Happy to help!