A Quantamental Leap Forward
A step-by-step summary of our methodology and an overview of its benefits.
Using multi-sourced, aggregated ESG data*, we generate computer models to identify the key metrics that explain ESG-driven excess returns historically, and that are predictive of future ESG rankings. We then use factor analysis and model validation techniques and apply them in innovative ways to the ESG data. Next, we identify stocks predicted to have attractive ESG-driven returns going forward and rank them.
*Primarily supplied by OWL Analytics
Our Methodology In 5 Steps
Step 1
Compute residual returns for individual stocks, controlling for market and sector.
Step 2
Use feature selection to identify Key Performance Indicators (KPIs) that explain residual returns (alphas) from all possible ESG metrics.
Step 3
Identify the historical values of, and 12-month changes in ESG metrics, that best predict the KPIs identified in Step 2.
Step 4
Using results from Step 3, forecast future values of the KPIs for each stock.
Step 5
Rank stocks based on predicted KPIs, and on forecasted alphas driven by KPIs.
Our Methodology’s Benefits
Confluence’s methodology is superior because it produces better, more comprehensive assessments of existing ESG-based exposures, clear foresight into where those ESG exposures are headed, and actionable forecasts of ESG-driven residual returns.
While there are many approaches to sustainable investing, most (if not all) rely on overly narrow, qualitative, and subjective measures with mixed or poor results. Hence, investors need our quantitatively based, data-driven, fully validated methodology to stay focused upon and drive performance.
Our Complete Methodology
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