Key Investment Trends:
ESG investing is gaining increased traction with Vanguard and Blackrock also joining the ‘Net Zero Initiative’ recently. This group includes 43 managers representing $32 trillion of assets, with the goal of the group to cut net greenhouse-gas emissions of their portfolios to zero by 2050.
Other key trends in this space include growth in using Liquid Alternatives, Private Equity, Real-estate debt, distressed debt and, of course, Bitcoin.
On the ETF front in the US, there may be interest in ‘Single Security ETF’ which, according to Bloomberg, is likely to be approved by the SEC. These ETF’s hold not only a single underlying security but also can contain T-Bills and derivatives such as swaps for diversification.
Key challenges in navigating the above trends –
On ESG side – Ability to analyze ESG risk using large non-standard data sets from multiple sources to provide custom solutions to investors based on their preferences
For other areas, there is a need to ingest alternate investment data to combine with existing portfolio to better analyze portfolio, including for risk and performance.
Positioning for the future –
Imagine if firms can support analysis for various asset classes, large ESG data points and multi-asset data together with capabilities for What-If analysis and forward looking analytics.
And have the tools for managers to exclude or under weight companies with high ESG risks or to use ESG data to engage with companies to help improve their ESG ratings.
This, along with the ability to slice/dice data for deeper insights using Business Intelligence (BI) tools, will be a huge plus.
Investors’ benefits include the ability to get tailored solutions for their desired target impact. Thus enabling them to understand what the portfolio is trying to achieve with ESG data sets and other alternate investments.
So what can IM firms do to address the above challenges?
– Focus should be on the data model: A centralized investment data model that can handle not only ESG attributes(from multiple sources) but also store alternate investment data sets.
– Provide ability to perform ‘ad-hoc’ what-if and forward looking analysis with no reviewing of multiple reports and/or excel files.
– ‘Liberate’ the centralized data to users for analysis using their preferred BI tool(s)
– Given the volume and varied data, scalability and performance will be important consideration for the analysis using BI tools to be more effective not only for the short-term but also for the long-term.
From a technology standpoint, firms need to:
1. Invest in building a centralized data model and enable API access
2. Use modern architecture to integrate multiple systems, including legacy systems
3. Invest in cloud warehouse (such as snowflake/redshift) and provide users access to this data using BI tools.
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