Data on climate risk and social issues can be spotty, but algorithms try to fill in the gaps.
On Jan. 14, the chief executive officer of the world’s largest asset manager warned that investors had to pay attention to global environmental risks. “Climate change has become a defining factor in companies’ long-term prospects,” wrote BlackRock Inc.’s Larry Fink in a letter to CEOs. “I believe we are on the edge of a fundamental reshaping of finance.” In fact, many big investors now incorporate environmental and other social factors into their stockpicking, for at least some of their funds. But they face a real obstacle: The data is a mess.
Some companies disclose a lot of information about sustainability, labour practices, or gender equity. Others say almost nothing. A fund manager buying a few dozen large-cap U.S. stocks may be able to have analysts dig up enough corporate info to make a decision. But things get harder if you want to be able to choose from thousands of stocks, or evaluate small companies or emerging-market equities.
Quantitative investors say they have a solution. These traders use computers to sort through reams of data, and they say they’re better than anyone else when it comes to making investment decisions based on messy or incomplete information. Quants are “used to filling in the gaps,” says Andrew Dyson, CEO of QMA, a quantitative investment firm that’s part of asset manager PGIM. It launched a socially conscious investment strategy in 2018.
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