Socially responsible returns: including ESG in risk analysis

Tuesday, February 23, 2021 | By Roman Chorneyko, VP, Cloud Solutions, SS&C Algorithmics

Socially responsible returns: including ESG in risk analysis

The concept of Risk Adjusted Return on Capital (RAROC) has been around for many decades. The idea is quite simple—that not all returns are equal. If two investment approaches result in the same level of returns but one incurs much more risk to do so, then obviously any savvy investor would choose the less risky approach. The “risk” term in this equation is completely subjective. There are many ways to define risks, such as Value at Risk (VaR), Volatility and Standard Deviation. But why should analytics only consider financial risk? In today’s investment world there should also be some sort of consideration for the environmental and social attitudes and practices of the companies being invested in.

The different types of data available today can be incorporated into such an analysis. Most market data collectors and vendors, such as Refinitv, Bloomberg, Factset and Morningstar, are producing some type of ESG score. So, what could such an analysis look like? Let’s examine a handful of companies: 

Image shows ESG scores for various companiesThere is a very simple analysis we can do within the SS&C Algorithmics Managed Data and Analytics Service (MDAS) to make our risk reports reflect ESG information. We add the ESG Score (these scores came from; Sustain Analytics is a Morningstar Company) to a table where we also report a 95% Monte Carlo 1d VaR for a $100k position. This provides a way to construct two views of risk and social responsibility. The average ESG score of the equity group in green is significantly better than the red group, and the risk profile and industry allocation are similar.

Just like there is a risk vs reward trade-off, there is going to be a risk vs ESG tradeoff. There is also going to be a reward vs. ESG tradeoff, as the reward is just the other end of the spectrum from risk. Regardless, there are more sophisticated views we can construct, such as how much risk to add for each point of ESG score. Many portfolio construction processes use optimization techniques to construct the ultimate security allocation. With the ESG ranking data settled, it’s simple to add a constraint to the problem. Something like, “cannot invest in any company with higher than a 25 ESG score,” can easily be factored into the problem.

Finally, this type of information and analysis can and should be specific to the values and considerations of the investor. Perhaps the investor wants to factor environmental responsibility more into their investment decisions if, for example, the strategy is based more on resources or agriculture. On the other hand, if investments are primarily in banks and insurance companies, perhaps gender and racial workforce equality are more relevant.

With all of the information regarding the social and environmental “mindedness” of companies available, it’s more important than ever to factor it into reporting, analysis, and portfolio construction. Having the data (ESG scores) and the analytics (generated using SS&C Algorithmics MDAS) in one service makes it simple to provide this additional insight to investments.

Asset Management, Risk Management

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