Traditional money managers are scouring the world for information to gain an edge as indexing takes over
“Alternative data” is going mainstream as fund managers are projected to spend more than $1 billion this year to beat the market averages and stave off the rise of low-cost passive investing.
Investors have been embracing the first option, if spending is anything to go by. While sources offer varying figures on the size of the alternative-data industry, there’s widespread agreement that business is growing rapidly with no sign of an imminent slowdown. Schroders, Fidelity Investments, Capital Group, Neuberger Berman, T.
The growth in alternative data has a number of drivers, most of which offer few surprises. After all, the advent of machine-learning technology and the drop in the cost of computing power have made it much less expensive to crunch ever-larger sets of data — a phenomenon that’s led a number of traditional, active asset managers to increasingly incorporate quantitative investing techniques into fundamental-oriented models.
Some of the industry’s best-known players include alternative-data aggregator Quandl, acquired in December by Nasdaq Inc. NDAQ, +2.
“I think of alternative data as very much an additional input into our process,” said Katherine Glass-Hardenbergh, associate portfolio manager at Acadian Asset Management, a quantitative investment-management firm with around $96 billion in assets under management that’s been using alternative data sets for years.
Alternative data sets should generally be expected to deliver performance similar to traditional data sets, he said. So while the data aren’t necessarily “better” when it comes to providing performance-enhancing insight, they are “different,” Luo explained. But those choices can lead to other dilemmas. Consider a vendor touting an off-the-shelf data set as a back-tested, highly reliable signal. Even if taken at face value, there’s the concern the vendor will be selling that data set to a wide range of investors, which means its advantage could soon be arbitraged away, Glass-Hardenbergh said. Or there may be little “visibility” into what makes up the data.
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