Amid Big-Data Fads, One Firm Tries a New Approach

With data science and artificial intelligence, Prudential’s QMA is blurring the line between quants and old-fashioned fundamental stock pickers.


Last month BlackRock said it plans to pour resources into data science — finding investment ideas in everything from social media data to satellite images of busy shipping ports — as it shifts to offering more quantitative funds. One quant investment shop is going in a different direction.

QMA, the $116 billion multi-asset manager owned by Prudential Financial that has managed U.S. equities for institutions since 1975, is somewhat skeptical of the big-data revolution, even as it is being touted as the savior of active managers. Many asset managers will only be able to pull short-term signals out of the vast amount of data that has become available in recent years, including on social media platforms, says Joshua Livnat, a managing director who focuses on global accounting research at QMA. Instead, QMA is using its data and computer-based expertise to incorporate insights that have historically been associated with fundamental researchers into its quant process.

To be considered useful to the firm, the data it uncovers has to meet a high bar. Among other things, signals or patterns have to work all over the world, and they have to tie directly back to an underlying economic rationale, executives at the firm say.

Gavin Smith, a vice president and researcher for the firm, says QMA has had success in analyzing earnings call transcripts — once he began to look at them differently.

Smith explains that he started off looking at the sentiment of the call, even assessing the ratio of positive to negative words. “But I was mostly intrigued by changes in tone relative to past calls,” he says. Once he realized that changes in the way that management would talk about results were predictive of stock returns, he dug in to find out why and to tie it to an economic reason. “If you can’t establish that, then you’re falling into the trap of data mining,” he says.


What Smith found was usable: Once the change in sentiment was detected, he could look back four quarters and see a decline in sales, profitability or margins. For four quarters after the change in tone was detected, the company’s business improved.

“The change in tone was giving us insight into the future. And it wasn’t reflected in the hard numbers at the time of the call,” says Smith. “Management was seeing light at the end of the tunnel.”

Using unstructured data also decreases the likelihood that others will stumble across identical insights, reducing the ability to squeeze extra returns out of expensive big data work, the firm says. That’s important given the asset management industry’s obsession with data science.

“Traditional quants looking at financial statement data, say, will all uncover the same signal,” says Smith, who emphasizes that he’s always monitoring models to make sure they aren’t becoming less effective over time.

Next up for QMA is looking at reports from research analysts at brokerage firms to go beyond over-used measures such as earnings forecasts and revisions. Analysts are often slow to change their forecasts, especially when they are negative, because of concerns about hurting corporate relationships. QMA wants to see if it can find clues in these texts that could predict a shift in analyst sentiment on a stock and get ahead of a forecast.

By gleaning useable pearls from unstructured data, quant managers are accessing information once the province of fundamental managers who rely on talking to management, suppliers, and other qualitative methods. Unstructured data is nothing more, in some cases, than a word-by-word account of a conversation with a company’s senior executives or a discussion with investors at a brokerage conference.

Smith says this type of information, including comments on sites where employees can provide opinions about their employers or the fairness of their salaries, can give a much truer picture of a company’s culture and reputation. All of these factors can figure into a stock valuation.

“There’s a blurred line between information that is exploitable by a fundamental manager and what a quant investor can now take advantage of,” says Smith. It’s something QMA hopes will differentiate it in an increasingly competitive market.