Products
We start by generating normalized Aggregate Rankings of securities using consensus ESG data from Owl Analytics. Next, we identify the key ESG metrics that affect performance and then rank securities accordingly. In addition, we produce forecasts for each of our Aggregate Rankings, while also predicting a security’s Alpha (excess return) twelve months into the future.
A Costly and Complex Challenge for Asset Managers
Our performance-driven ESG rankings, along with rankings and alpha forecasts, are derived from models built using widely-sourced, global data. We provide reliable signals and tools that allow investors to find an edge.
Results-Driven
We provide superior ways of assessing and managing current ESG-based exposures, and then take the critical step of relating those exposures to ESG-driven performance. Historical results of our signals over the last year and since inception.
U.S. Large Caps
(Confluence Signals, 1-Year Performance)
Equally weighted, updated quarterly. 1 yr performance is 2022.
U.S. Large Caps
(Confluence Signals Since Inception)
Equally weighted, updated quarterly. Since Inception starts 7/31/2019.
Use Cases
PORTFOLIO MANAGEMENT
• Manage portfolio ESG and Carbon risk exposure at the aggregate and company level
• Gain stock-level insights about how individual pillars (E, S, G) and underlying themes (Controversy, Diversity, Carbon, etc.) contribute to ESG-driven performance and risk
• Determine compliance with ESG-related investment policy guidelines
• Preemptively position portfolios against adverse trends in ESG and Carbon risk exposure
• Maximize portfolio alpha generation/minimize deterioration via Alpha Rankings
EQUITY RESEARCH
• Create more accurate financial models/forecasts by assessing a company’s ESG and Carbon risks
• Inform stock-level investment recommendations based on primary pillars (E, S, or G), and underlying themes (Controversy, Diversity, Carbon, etc.)
• Assign premiums/discounts to cash flow forecasts/ DCF models based on a company’s E, S, and G and Carbon risks
QUANTITATIVE RESEARCH
• Develop proprietary quantitative signals from historical Aggregate and Performance ESG Rankings, and forecasts of future Aggregate Rankings.
• Incorporate ESG and Carbon metrics into models to complement fundamentals and other factors
RISK MANAGEMENT
• Identify positions that pose significant ESG and Carbon risk along with other sources of risk
• Use of forecasting metrics to identify stock-specific ESG risk