Capital Group names new Japan president; Robeco replaces Singapore chief; Hillhouse Capital hires ex-Blackstone MD; Vontobel expands EM debt team; HSBC Global AM names Apac ETF sales head; Manulife creates new digital business role; Stanchart names CIO for wealth management; TMF appoints fund services exec in Shanghai; and more.
Institutional investors are demanding more information on companies’ environmental, social and corporate governance (ESG) activities in order to make informed decisions about where to invest and where to use their influence to improve companies’ sustainable management practices. However, the ESG field is crowded with diverse frameworks, ratings providers and greenwashing. Corporates and investors often find more confusion than clarity.
Research on artificial intelligence (AI) technologies to gather and analyse ESG data shows a promising way forward. Natural language processing (NLP) and machine learning techniques can gather and process vast amounts of data about companies’ ESG impacts from diverse sources with speed and a high degree of objectivity to complement to corporate disclosures and existing ESG ratings.
Ping An’s Digital Economic Research Center’s latest research has harnessed AI to assess companies’ climate disclosures and detect potential greenwashing to help investors understand and differentiate firm’ climate risk profiles.
We used NLP techniques to automate the analysis of the climate disclosures report of US and Chinese firms in the S&P 500 and the CSI 300. Researchers developed a set of AI-driven indicators to assess various climate and financial impact-related metrics aligned with the recommendations of the international Task Force on Climate-related Financial Disclosures (TCFD).
The AI-driven indicators extracted more granular information on the climate risk exposure of firms than traditional ESG ratings. The research showed that the AI-driven indicators did better than broad ESG ratings in the market in distinguishing between so-called “brown” firms – those in high emission industries, such as mining, transportation and infrastructure -- and lower emission firms.
This helps investors to select companies more aligned with their investment objectives.
In addition to better informing portfolio tilts, the AI-driven indicators were also used to identify potential evidence of corporate greenwashing, especially among high emission firms. This would help investors screen out companies in their portfolio that do not align with their investment objectives.
The Ping An research shows that:
• Low emission firms have higher compliance around disclosure of climate-related metrics than high emission firms, suggesting under-reporting by high emission firms
• High emission firms under-report the impact of climate risk on capital and financing, such as the impact of stranded assets and liabilities for oil and coal companies
• High emission firms have significantly lower disclosure around Scope 3 emissions (all indirect emissions, except for emissions generated by purchased energy, that occur in the value chain of the company, including upstream and downstream emissions), despite increasing attention from regulators and investors on this dimension
The AI-driven indicators have another potential application: they can help investors identify firms with specific target characteristics to capitalise on investment opportunities. AI-driven indicators of climate disclosure allow us to go beyond carbon emissions to a deeper level of detail to understand how climate change risk disclosure might shape discount rates – and hence valuations.
For example, Ping An research found that climate risk disclosures are associated with higher valuation of large cap firms, after controlling for emissions. Small and medium cap firms that engage in effective climate risk disclosures may also offer superior risk-adjusted returns along the path to efficient climate-risk pricing. As we transition to a new state in which markets reflect climate risk exposures and investors’ preferences, such findings can help savvy investors capitalise on this transition.
By harnessing the potential of AI for ESG investing, we can give investors a richer, holistic view of the relationship between a company’s ESG activities and long-term value, and detect potential greenwashing. AI-driven climate disclosure indicators offer a valuable addition to the investor’s toolkit as a complement to existing ESG ratings.
AI can help investors inform portfolio tilts for meaningful decarbonization strategies, better understand climate risk premiums beyond emissions and how companies’ climate risk disclosures can add value to shareholders and capitalise on the transition to a low-carbon economy.
This article originally appeared in AsianInvestor's Winter 2020 edition.
New European Union regulations being introduced in March will raise the bar for ESG reporting and could well hurt the appeal of energy- and carbon-intensive stocks.
Hurdles such as foreign exchange limitations still exist, but the new rules could help private equity fund managers and investors with ESG integration.
JP Morgan AM hires Asia head of investment stewardship; Hines appoints Asia CIO; JLL names new India CEO; William Blair IM hires Asia sales head; Colliers appoints new Australia chief; Fidelity moves into private credit; Broadridge adds Apac COO; Nomura adds 20 private bankers in Hong Kong and Singapore and more.
By applying the ‘Investment Clock’ framework, investors can link factor behaviour across economic cycles in the US.