Peer Group
Submission
Peer Group Submission
Submit self-identified peer groups used for executive compensation benchmarking.

Peer Group Submission
Peer submission windows are open twice annually for publicly traded companies in the U.S. and Canada to submit self-identified peer groups used for their executive compensation benchmarking. Self-disclosed peer groups are incorporated into Glass Lewis’ proprietary methodology which drives our executive compensation assessment and the outcome of our executive compensation model.
Relevant companies are notified by email of biannual open window periods. Opt-in to peer submission communications.
Glass Lewis’ Peer Methodology
Glass Lewis has Glass Lewis has a rigorous, state-of-the-art peer methodology that informs our Pay-for-Performance Model, and our Say on Pay recommendations. 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 peers-of-peers. This approach ensures additional screens based on corporate revenue, market capitalization, and assets; weightings also consider the source and frequency of confirmation, and peer rankings are based on a strength-of-connection approach that considers all potential peers, not just those resulting from the network effects of corporate disclosures.
Related Products and Services


Podcast | Activist Investing Today: Glass Lewis' Timmer on AI, Future of Governance
Glass Lewis president Diederik Timmer discusses the role of AI in proxy advisory services, evolving shareholder voting practices and the firm's customized voting policies.


Podcast | Activist Investing Today: Glass Lewis' Timmer on AI, Future of Governance
Glass Lewis president Diederik Timmer discusses the role of AI in proxy advisory services, evolving shareholder voting practices and the firm's customized voting policies.


The Most Advanced AI Model Can Also Be the Most Fragile: A Lesson for Investment Stewardship
This article examines how the sudden withdrawal of a frontier AI model underscores the importance of trusted data, domain expertise, and human oversight in building resilient investment stewardship workflows.


The Most Advanced AI Model Can Also Be the Most Fragile: A Lesson for Investment Stewardship
This article examines how the sudden withdrawal of a frontier AI model underscores the importance of trusted data, domain expertise, and human oversight in building resilient investment stewardship workflows.


On the Agenda: Five Questions Answered on Climate Intelligence Research
This interview with Emil Moldovan, Head of Climate Science, sheds light on the development process and methodology behind Glass Lewis’ Climate Intelligence Research.


On the Agenda: Five Questions Answered on Climate Intelligence Research
This interview with Emil Moldovan, Head of Climate Science, sheds light on the development process and methodology behind Glass Lewis’ Climate Intelligence Research.


The Foundation Matters More Than the Model: Why Trusted Data and Human-Centric AI Will Define the Next Era of Investment Stewardship
This white paper explains why durable value will come from trusted proprietary data, rigorous governance, and human oversight that make AI outputs reliable enough for institutional decision-making.


The Foundation Matters More Than the Model: Why Trusted Data and Human-Centric AI Will Define the Next Era of Investment Stewardship
This white paper explains why durable value will come from trusted proprietary data, rigorous governance, and human oversight that make AI outputs reliable enough for institutional decision-making.