Proxy advisor Glass Lewis’s internally constructed peer groups for issuers will see substantially less overlap with company selected peer groups as compared to previous years due to a newly-implemented peer group construction methodology. The new methodology “will drive the scores of the model going forward and the quantitative input for [their] recommendations on Say on Pay proposals.” As such, Glass Lewis say-on-pay evaluations may show marked differences in relative performance comparisons versus what companies are discussing with shareholders in the proxy statement.
- Will Glass Lewis’ new peers be different to those used in 2019?
Yes. Peers in 2020 will be significantly different to peers used under the old 2019 methodology. The new methodology increases the number of tests applied to a company’s self-disclosed peers, resulting in a reduction in the overlap between a company’s self-disclosed peers the Glass Lewis P4P evaluation peers. Specifically, Glass Lews indicates that "Beginning with a company’s self-disclosed peers, Glass Lewis then includes investor views on both industry-based and country-based peers, in addition to the company’s peer-of-peers."
- Will these new peers change Glass Lewis’ recommendations on Say on Pay proposals?
The changes to the peer methodology will change individual company outcomes, which will influence recommendations in positive or negative directions. Glass Lewis expects there to be no material change to the distribution of grades awarded or the number of against recommendations as a result of the new peer methodology because the "pay for performance model distributes grades evenly". It has also said that there will be other changes to the P4P model for the 2020 proxy season.
The change in peer group construction methodology is being driven by Glass Lewis’s transition to exclusively use CGLytics compensation data for its proxy research. Previously, the company had utilized Equilar’s compensation data. A more general Q&A page also provides information on how to access and test the modeling and peer group methodology through CGLytics, submitting peers to Glass Lewis, and engaging with them about concerns over the data and accuracy of reports.