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Inside Goldman’s Active Quant Machine

As the competition for alpha intensifies, GSAM is using more people and data innovations in its race to extract signals from newly disclosed information.

Osman Ali, co-head of Goldman Sachs Asset Management’s active quant equity group, wants just enough data.

If there’s too much data — think large-cap U.S. stocks — it’s harder for his team to make money because the market is too efficient. Too little data, and GSAM’s $60 billion Equity Alpha Strategies group could be data mining — finding patterns that look attractive but which could have fleeting or negative value because they aren’t based on logical economics.

The Equity Alpha strategies are designed to outperform the market, with GSAM using information such as environmental, social, and governance data to help its models deliver excess returns.

Recently, GSAM found signals that could lead to higher investment returns in all the new information available about companies’ greenhouse gas emissions and other climate change mitigation programs. Before the asset manager started its work, Ali brought on experts on environmental and climate issues — intentionally not finance — in part because there is little historical evidence showing how a company’s environmental profile affects its financial performance. The climate experts would also help the group’s quants avoid the dangers of backtesting data and finding what they were hoping to find.

“Quant investors often focus on empirical results, rather than the formulation of an economic hypothesis first,” Ali said. “We wanted to make our hypothesis and the quantification of what we were looking for much richer and more informed.”

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According to Ali, GSAM created a framework to analyze global companies and assign a score to them based on characteristics such as their emissions profile, transition risks, and their harder-to-quantify place in the carbon supply chain. “For example, are they at the extraction phase like an [exploration and production] company?” he said. Or are they a data center REIT, which might be a big user of electricity, but which could more easily transition to green energy. That company’s risk would be lower because it has more flexibility.” 

The framework helped GSAM create analytics to capture information about the companies. It now has empirical evidence that Ali said is helpful in predicting where a stock price will go, particularly in Europe. GSAM has added these scores as a factor in its investment models and will use it in all regions. 

The firm has also recently incorporated into its models data that provides insight into a company’s social footprint. While Ali’s team has long focused on well-governed businesses as potential outperformers, these are more nuanced data sets that include views on management reputation and employee satisfaction at global companies, including those in the U.S. and Japan.

“This wasn’t, ‘let’s find ESG data sets,’” Ali said. “Instead, we have a view that companies that have employees that are happy and productive, likely will have, on the margin, better financial results. This can translate into better returns.”

According to Ali, GSAM constantly updates its models as competition drains the value of certain signals. He said 5 to 10 percent of the models change annually.

Looking forward, the active equity quant group is developing tools to capture and quantify sentiment data and diversity data.

“Just like with climate, we’re spending a lot of time on inclusive growth, including diversity,” Ali said. “We’re dissatisfied with the really easy metrics that are out there. We want to capture inclusive growth across an organization and determine whether a company is making good decisions. This remains an area of open research for us.” 

But GSAM doesn’t want it to be easy.

“There is a lot more data coming out, but you have to do a lot of work to aggregate and analyze it,” he added. “That’s good for us. That’s where the return opportunities exist.”

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