Embedding climate data into Financial Algorithms
Why algorithmic climate data is integral to modern portfolio optimization
Responsible investing is increasingly gaining momentum as public, government, and shareholder pressure increases around Environmental, Social, and Governance (ESG) issues. In fact, recent data shows that one out of every three funds is now labelled as an ESG fund. But how do these responsible investment funds compare with their more traditional counterparts?
A recent meta-analysis by McKinsey analyzed 2000 individual studies that examined the financial gains of ESG-driven funds. 63% of these studies found that these funds resulted in positive financial gains. However, 8% of studies found negative results, showing a compromise between responsible investing and financial returns. In fact, a recent OECD (Organisation for Economic Co-operation and Development) report found large variations in the financial returns of sustainable funds. An analysis of the highest rated ESG funds showed 5-year annualized financial returns ranging from -20% to more than 20%.
These variations have huge financial consequences for the financial sector, as institutions are faced with the dual pressures of responsible investment and financial returns. In fact, with ESG assets projected to reach $53 trillion by 2025, a loss of 20% translates to $10.6 trillion globally!
The large variation in the financial returns of sustainable funds can be largely attributed to lack of algorithmic ESG data as financial institutions move from a single-metric regime to one of multi-dimensional portfolio optimization. Environmental data and ratings are still largely qualitative. Even where quantitative data does exist, lack of algorithmic scenario-building and experimentation tools present barriers for simultaneous optimization of portfolios across financial and climate metrics. optimization. EnvAI’s Climate Pathway API solves this problem by placing environmental data on an equal footing with traditional financial data.
As a case study, we sampled 132 companies across a variety of sectors including healthcare, utilities, energy, finance, technology, and others. With a total investment amount of $1 million, we simulated 10,000 portfolios by distributing the fixed total amount of money randomly across the different company stocks. The returns on equity of the portfolios are plotted against the end of century temperature associated with each portfolio below:
As expected, there is large variation in both the economic returns and the climate pathway of the funds. What’s interesting, however, is the relationship between the two. There is a large spread of climate pathways as indicated by end-of-century temperature scenarios across all financial returns. At the same ROE of 1%, portfolios can range in their climate pathway from less than 1 °C to 7+ °C. Similarly, funds that are compliant with a < 1 °C warming pathway can have ROEs ranging from -7% to 2%.
Using the computational engine provided by the EnvAI Climate Pathway API, asset managers can systematically analyze the full space of climate impact vs. financial returns using any number of equities, sectors, and emission assumptions.
But why not optimize funds based on total emissions?
Many financial institutions have pledged to emission-based targets, such as the large number of banks who have signed onto the Net-Zero Banking Alliance. However, not only are these targets based on far-off and hard-to-achieve binary targets (i.e. no target or a zero-emission target), they do not reflect the underlying desired climate pathways such as those of the Paris Agreement. But more importantly, net-zero emission goals do not allow for continuous and algorithmic optimization of investments based on meaningful metrics that relate to the underlying climate mandates.
As an example, take two corporations: Company A and Company B. Company A is a trillion-dollar multinational corporation that produces goods for a sizeable portion of the population. Company B, on the other hand, is a medium-size domestic company. Due to exceptionally poor environmental practices, company B has the same emissions as company A, while producing a fraction of the output. Using total carbon emissions, company A and company B would contribute equally to the environmental footprint of a portfolio. However, it is clear that these two corporations belong to vastly different climate scenarios. Company A represents a world where the demands of billions of people are met with the emissions of a medium-sized domestic company, whereas company B represents a world with a highly unsustainable pathway.
To illustrate the difference between raw emission numbers and the underlying climate scenarios, we plot the end-of-century temperatures of the 132 companies from the example above against the total emissions for each portfolio.
While there is an overall positive correlation between emissions and temperatures, as expected, it is clear that one can mistakenly minimize the total emissions of a fund while remaining on a very warm climate pathway on the upper left corner of the plot. On the other hand, it is also possible to remain on a relatively sustainable climate pathway without having the smallest possible emissions. In other words, simply minimizing total emissions is not always the correct metric in minimizing the environmental footprint of a fund if the goal is to be compliant with climate targets such as those of the Paris Agreement.
By providing the underlying climate pathway of funds through a customizable computational engine provided by the EnvAI Climate Pathway API, investors and asset managers can increase financial gains while keeping their portfolios compliant with the real environmental metrics that correspond to overarching climate goals. Learn more about our Climate Pathway API here. To learn more about the product and request a demo, get in touch with us below!
Contact us to learn more about EnvAI’s Climate Pathway API!