Mettle's academic papers
The original research that underpins Mettle Capital's solutions.
Does One Trust Judgement Fit All? Linking Theory and Empirics
Foundational paper on how to model an intangible asset like trust in democracy. Trust, like sustainability, is often treated like a single theoretical concept in empirical studies. Using data from YouGov's weekly omnibus and the British Election Study's Continuous Monitoring Panel, we operationalise multiple forms of trust judgements to examine trust in two British institutions. We find that different forms of trust judgements are of differing significance depending upon the institution under consideration. This is the basis for Mettle's entity-specific dynamic materiality assessments.
Fisher, J., Van Heerde, J., & Tucker, A. (2010). Does One Trust Judgement Fit All? Linking Theory and Empirics. The British Journal of Politics and International Relations, 12(2), 161-188.
Trade Associations as Industry Reputation Agents: A Model of Reputational Trust
Foundational paper on the role of industry benchmarking to understand how to calculate impact materiality. Scholars have started to focus on the ways in which firms manage their reputations through collective action. Informed by a rich set of 43 qualitative interviews with the trade associations representing the UK's 24 largest business sectors, the paper establishes how to determine which attributes should be associated with individual firms and which to the relevant industry. This is the basis for Mettle's calculations of impact materiality, one part of the CSRD's double materiality assessment.
Tucker A. Trade Associations as Industry Reputation Agents: A Model of Reputational Trust. Business and Politics. 2008;10(1):1-26.
Magnitude Algorithm when calculating Financial Materiality for Sustainability Reporting
This paper presents Phase Two of the Charlotte Project, an initiative by Mettle to quantify the financial materiality of ESG factors. Building on Phase One’s foundational framework—which identified limitations in raw ESG sentiment data’s predictive power—this phase introduces Principal Component Analysis (PCA) for feature extraction to mitigate dimensionality challenges and enhance model stability. Results underscore PCA’s effectiveness in noise reduction while highlighting the need for further refinements—such as ensemble methods or class balancing—to advance ESG-driven financial modeling. The study sets the stage for Phase Three, targeting optimized feature engineering and model tuning for scalable applications.