Ashby Monk on Alternative Data’s Potential
Alternative data has a lot in common with organic food, Ashby Monk, PhD, told the audience at the 2018 Financial Analysts Seminar. “Or, as your grandparents called it, ‘food,’” he joked. “Before it was alternative data, it was just data.”
As the executive and research director of the Global Projects Center at Stanford University, Monk studies the tools and technology that help investors make better decisions. Now that analysts can apply modern technology to better process larger amounts of information, alternative data has seemingly achieved ever greater import, he said.
But people have been using so-called alternative data to make investment decisions for the entire history of finance. “This is incredibly old,” Monk explained.
Monk explained that the split between conventional and alternative data was created when regulators, like the US Securities and Exchange Commission (SEC), decided what had to be reported. That became known as conventional data. “When we say alt data, we really mean innovative data,” Monk said. “We mean sets of innovative data that are not conventionally used for investment decision making.”
The forms alternative data take have varied considerably over the years. At one time, investors made forecasts based on the size of then-US Federal Reserve chair Alan Greenspan’s briefcase. “Hedge funds used to pay people to sit at the border and count shipping cars crossing,” Monk recalled.
But advances in technology have helped create countless new forms of alternative data and new ways to sift through and analyze it. “I think alternative data is far more important to our community than artificial intelligence,” Monk said.
And this revolution in alternative data analysis could lead to the investment industry equivalent of a nuclear arms race.
The question is whether investors will be able to apply this mountain of data constructively. “What we’ve witnessed in our research is that when you have alternative data programs, you begin to hoard data,” he said. “You don’t understand the value of it, you haven’t even run a cost/benefit analysis, but you hoard it.”
Monk continued, “The problem is if you’re in an arms race and you’re spending millions of dollars on access to alternative data sets — satellite imagery is not cheap to get — you end up in a set of very dysfunctional behaviors.” Investors feel compelled to act on their analysis, ending up with a bias toward action that increases market volatility.
Applications of alternative data for short-term returns pose the greatest danger, according to Monk. “You risk getting on a treadmill,” he said, “buying the next data set, trying to use it before its life runs out, before the market realizes what’s going on, and buying the next data set, and partnering to get new data, and spending more and more in order to generate more and more alpha.”
Monk believes that this is likely where most of the fees end up in the hedge fund space. “A lot of it goes to yachts,” he said. “But some of it goes to alternative data.”
And this creates challenges for the hedge funds themselves.
“If you’re selling that data for the purpose of generating alpha, then you have a problem, because you can only sell the data set two, three, four times before the value of the data set goes to zero,” Monk said. “If you’re using it to generate alpha, that’s the nuclear arms race.”
So, which market participants are in the best position to benefit from alternative data? Large institutional investors like public pension plans, sovereign wealth funds, and insurance companies, Monk said. They can use their patience to their advantage, applying alternative data in other ways. “Think about how you use that information to limit capital loss,” he said.
“If you’re using the data to understand your portfolio and get a sense of the risk embedded in your portfolio, or the risk embedded in an asset, that’s like nuclear energy,” Monk said. “Everybody can use it. Everybody can pay for it.”
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