Daniel Gerard, State Street Associates
“There are issues with the fundamental analyst driven nature of ESG factors in the investment process,” said Daniel Gerard, head of investment and risk advisory, State Street Associates at roundtable discussion in Hong Kong today. “Data can be too infrequent, measuring events are sometimes too extreme, and capturing events can be too late.
“By using artificial intelligence (AI) and machine learning (ML), new data providers and analyses are popping up to address some of these concerns,” he added, although acknowledging that “they are a long way away from becoming mainstream”.
ESG is becoming increasingly important in the portfolio investment process, with asset owners rather than asset managers in the vanguard for its promotion, Gerard said.
Many investment managers now routinely advertise the inclusion of ESG analysis into their investment processes, across a widely-recognised range of five styles, namely: exclusionary screening, positive screening, ESG integration, impact investing and active stewardship.
Some managers are also making efforts to disaggregate the specific contribution of ESG factors to a portfolio’s total return.
However, it remains difficult to evaluate the environmental and social impact of companies’ operations, and the quality of governance metrics. Basically, are the tools – that is, the ESG rating agencies – sufficient?
“Most especially, there are ‘intangible risks’ that can’t always be anticipated and so can’t been measured,” said Gerard.
Currently, agencies such as MSCI and Sustainalytics, assign ESG ratings to firms.
However, academics have shown that these ratings diverge substantially between different rating agencies, according to Gerard.
“There is extraordinary discrepancy in the ratings of firms from one company to the next, making the evaluation of social and environmental impact very difficult,” he said.
The consequences are that ESG performance is unlikely to be properly reflected in stock and bond prices, and that empirical research to improve a fund’s ESG performance becomes tougher because the selection of a particular ESG rater may skew the research’s results.
Measurement inadequacies
These discrepancies can be assigned to differences in scope (whether they are looking at the same things), aggregation (their respective weightings to each factor), and, most significantly measurement.
“We need a better way to measure the quality of the data to reach accurate assessments,” said Gerard.
In practice, that means analysing data more recently, more frequently, more systematically, and being able to tap into a larger data base.
Artificial intelligence and machine learning can help improve the processes on all these issues, according to Gerard.
State Street said it has developed an ESG portfolio management tool that incorporates the ESG scores assigned by conventional analytic firms such as MSCI with the scores generated by AI vendors, such as Truvalue Labs.
But, Gerard acknowledged that that AI and ML applications are at a nascent stage. They rely on “web scraping” and natural language processing to access and interpret vast quantities of data, but there can be difficulties distinguishing between valuable and poor sources, and understanding the nuances and emphases of prose and its variety of formats.
“Establishing polarity, that is, determining a positive or negative assessment from a particular piece of content, can be hard,” he said.
Nevertheless, AI and ML techniques should eventually help resolve the divergence between conventional ESG rating agencies.
“Investors want a platform that can incorporate many perspectives, both traditional analyst-driven views and the emerging technology-driven views of the future. They want to cross-check biases, decrease information disadvantages, and expand their dialogue with their clients and partners,” said Gerard.
Source: State Street
