Getting the edge on ESG Big Data with a data-driven, science-based approach
Sustainable investing has become common practice, however there is not one route to the final portfolio. Asset owners need to navigate not only regulatory requirements and internal KPIs*, but the breadth and depth of data, data providers and diverse approaches by asset managers. Institutional investors must identify those asset managers who master the data and can transparently implement their regulatory requirements and stakeholder objectives. Quantitative or systematic asset managers typically apply a data-driven approach to investing which includes broad universe coverage, fundamental analysis of securities and portfolio diversification across factors, sectors, and countries. Quantitative managers are well positioned to provide transparency and guidance when navigating ESG big data. Let’s look at the route ahead using climate data as an example.
Data and science as the foundation for investment decisions
As systematic asset managers, we try to identify trends and patterns based on relevant data. Our expertise lies primarily in the development of signals, but also in the sophisticated combination of individual metrics and their integration into existing investment models.
At Quoniam, our investment approach is data-driven and science-based. By data-driven we mean using large amounts of data to power our decisions. Science-based implies that our choice of data is based on the use of rigorous, systematic, and objective methodologies to obtain reliable and valid knowledge. This means that when adding ESG data to our investment process, we do not proceed superficially, but apply the same data-driven and science-based approach we apply to financial investing.
Navigating ESG big data
As a quantitative asset manager, we draw on existing infrastructure for the analysis and exploitation of sustainability data. This way, we efficiently extract the relevant information for sustainable portfolios that are aligned with financial investment objectives and clients’ KPIs.
When we approached ESG as a new data project, our first goal was to understand the data and the relationship between the many metrics that exist in the ESG field. Important keywords here are distribution of data, coverage and correlation.
With regard to ESG data, our research showed that some of the data is often only relevant for a few industries. For example, does a company have a risk of stranded assets, such as large coal or oil reserves that have not been extracted? We also found that historical data is often not available or only insufficiently available, especially for climate data.
Exciting findings beneath the climate data
When we analysed the data in the ESG field, we noticed that green patents are partly positively correlated with CO2 emissions, i.e. companies with more green patents tend to have higher CO2 emissions. We found this interesting, which is why we deepened our analyses on green patents.
We also noticed that companies’ climate assessments mainly look at emissions from the past. We believe, however, that such an assessment should also consider how companies plan to reduce emissions in the future. A pure concentration of the portfolio on past emissions data excludes high-emission industries, but also potentially strong transformation candidates.
Focus on future opportunities
In turn we focussed our climate data research on metrics that have a connection to the potential future emissions of the company, which included further exploration of, for example, green patents. The field of forward-looking data is not yet widely researched, and we chose to publish our findings in the “Back to the Future: The Role of Forward-looking Climate Metrics in Decarbonization Portfolios” working paper.1
The strength of quant managers lies in integrating climate risks and opportunities into the existing investment process. To create our climate equity strategy, we looked at how climate metrics can be reconciled with return forecasts and how they correlate with classic style factors. The avoidance of sector and country bias was also relevant. Finally, we addressed the question of how to implement a climate strategy with a certain return potential that manages climate risks and climate opportunities at the same time, and what the impact of climate integration is on the performance of the investment strategy.
At the moment we use classic or structured ESG data, but there are efforts on the part of research, data providers, but also on our part to improve the data quality, availability and understanding to use and filter unstructured data such as news articles, earnings calls and company reports for additional information. We expect ESG big data to remain a dynamic topic.
Dr. Jieyan Fang-Klingler, Co-Head of Research Forecasts, Quoniam Asset Manager GmbH
*Key Performance Indicators
1The paper can be found at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4135443