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How AI Technologies Can Put Purpose and Profit into ESG Investments

Many institutional investors today are seeking more than simple profit. They are looking to make a social and environmental difference, stewarding their financial resources toward investments that fulfill their purpose and mission while increasing financial returns.

The field of environment, social, and governance (ESG) investing has grown into a full-blown industry as a result, with wealth managers adding ESG advisors to their ranks and companies reporting on their ESG policies. Institutional investors, hedge funds, and family offices that take ESG considerations into account are seeking new ratings and research methods to guide their decision making.

Yet screening companies and portfolios for ESG considerations without sound models and robust information is like walking in place and calling it a workout. The ESG box may be ticked on the list but the exercise is hollow.

Artificial intelligence systems based on knowledge-graph technologies can make the difference. The intrinsic nature of dynamic Knowledge Graphs—projecting within their structure billions of relationships across hundreds of millions of data points—augments the capability of investment analysts and researchers by screening more information and finding unintuitive inferences that result in faster and better quality of insights.

The Rise of ESG

ESG themes can include considerations like climate change, pollution, health and safety, labor standards, human rights, anti-corruption, etc. To adequately incorporate these considerations into a portfolio, smart investors determine their own ESG thesis and analyze a wide variety of information and data sources for investment fit—and profit opportunities.

ESG-based investment theses are gaining adherents because the ESG lens can provide a more complete picture of the risk profile and growth potential of the companies. It gives another view of the quality of company management, culture, environmental record, and operating risk.

“Firms are coming to grips with the limitations of a traditional approach to finance—one that defines value too narrowly by looking only at financial considerations,” says Jed Emerson, in a William Blair whitepaper. “Sustainable and impact investors realize that a company’s entire story can’t be told just by the numbers that appear on financial statements.” Emerson is the author of The Purpose of Capital: Elements of Impact, Financial Flows and Personal Being and advises wealth manager and investment bank William Blair and Company on social and impact investing.

Taking ESG into consideration when looking at large investments in individual companies or throughout a portfolio is becoming increasingly important to institutional investors, hedge funds, and some family offices. The Forum for Sustainable and Responsible Investment estimates the overall 2018 sustainable investment market, which includes public companies following an ESG ethos, at about $12 trillion.

Assets in “global impact investment” increased to $502 billion by the end of 2018, the Global Impact Investing Network (GIIN) reports. GINN surveyed 1,340 organizations identified as “impact investors.” GIIN’s latest estimate is more than twice the $228 billion it calculated in May after looking at data from 229 organizations.

The number of hedge funds adopting ESG policies is also increasing, according to research by J.P. Morgan. Last year, only four of the hedge firms working with the bank had ESG policies. This year, it’s more than 30. It expects the number to grow to 50, half of the funds it works with, to have ESG policies by year end.

The head of the alternatives solutions group at J.P. Morgan, puts it like this: “We are seeing managers who are willing and understand and say, ‘A-ha, this makes sense not only from a business perspective but from an alpha perspective.”

ESG Research and Screening Considerations

Incorporating ESG considerations into a portfolio goes far beyond adding a new product to a portfolio mix. It requires a thoughtful strategy. Investors that are serious about ESG will develop their own ESG thesis, rooted in the value of the institutions and its stakeholders.

It’s difficult work to construct an ESG strategy, use it to select investments and balance portfolios, and keep it relevant over time. As one asset manager put it, “There is a lot of judgment to this. It’s not something where you can pull a number off a Bloomberg terminal and be done.”ESG scores and screens are coming online from a variety of analytics, data, and software vendors, either as stand-alone services or additions to existing systems.

They can provide guidance for screening but often lack the nuance a serious ESG investor seeks. The usefulness of a score or screen depends on:

  • The quality and quantity of the data sets used to generate the score or screen
  • The relationship of the underlying score and screen data to your fund’s ESG themes or thesis
  • The ability of the scoring system to adapt to and extend your ESG criteria over time

Portfolios, assets classes, and stakeholder concerns change constantly. At the same time, companies work in ever-changing social and environmental conditions, all of which determine risk and, as a result, both long-term value and short-term price movements. Taking that into consideration requires massive qualitative research against individually determined criteria.And that’s where new technologies can help.

Integrating New Insights

New analytics based on augmented and artificial intelligence are expanding the depth and breadth of knowledge for investors targeting specific industries. With the proliferation of AI-based platforms, robotics, analytics, voice-activated interfaces, and even blockchain technologies in finance, comes better insights and more robust knowledge about strategies and outcomes.

Robotic process automation, for instance, can improve efficiencies in gathering data and managing risk. PwC estimates that, once processes are sound, financial professionals can save 46% of their time in gathering research and information. At the same time, automated analytics solutions can increase the accuracy of transaction data and better manage system controls, thereby reducing risks

Technology is making information access and financial decision making more personal and accessible for both institutional and retail investors. Mobile apps combined with voice-activated user interfaces are making financial decisions more personal, immediate, and empowering. Technology-driven recommendations are becoming the norm in investments, reports TD Ameritrade, as they are in shopping and entertainment.

The knowledge graph framework is another example of an emerging technology advancing the finance industry. A knowledge graph is a mathematical framework for dynamically gathering and semantically processing a large network of structured and unstructured data and information representing multiple subject areas. By mining the data of the source information, the platform creates an efficient algorithm-based representation of the overall knowledge they provide, allowing for capturing significant relationships—often hidden—semantically and quantitatively across complex data sets.

Instead of basing results on individual elements, like a typical database query, it uses algorithms to construct relationships—the graph or network—among concepts that are not evident. It shows the relationships between concepts and their connections, and derives inferences, and suggests promising areas of intelligence.

As a result, a knowledge graph constructed from a large number of diverse information sources and data sets can be used to make predictions, support decision-making efforts, and identify strategies to find meaningful trends hidden within the information.

The insights of a good investment analyst are still critical, however. Knowledge graphs are the perfect tool for providing augmented intelligence. In finance, machine learning robo-advisor platforms provide black-box trading advice, but fail to display the rationale of the output of the algorithm. Knowledge graph-based solutions synthesize a constant flux of company, industry, financial, economic, political information, to name a few, to aid investment professionals in making purposeful and profitable decisions.

That’s the kind of edge that institutional investors need now, especially for ESG investing. Using a multi-domain knowledge graph, a fund or portfolio manager can extract investment securities and screen portfolios that not only fit specific ESG criteria, but also create new thematic investment insights tailored to an investor’s mission, risk, and return profiles.

Comprehensive models and ratings for ESG are dependent on an institution’s preferences and strategies. Solutions like these can make the difference between pursuing a trendy investment technique and building a strategy that can generate profit (alpha) while supporting a social mission and point of view.

About the Author

Ruggero Gramatica is founder and CEO of Yewno, innovator of the Knowledge graph that generates actionable knowledge from today’s vast informationYewno has created an extensive multi-domain knowledge graph using proprietary AI algorithms, combined with a multi-disciplinary technology platform that extracts insights and delivers products and services tailored to specific industries. Yewno generates actionable knowledge from the ever-increasing amount of information available today. As a pioneer in the Knowledge Economy and the innovator of the proprietary Yewno Knowledge Graph, an artificial intelligence-based framework powered by billions of disparate data sources, Yewno provides continuously evolving inferences that uncover unexpected insights for financial services, education, life sciences, government and beyond.

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