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Location data provides new insight into investments

Michael Recce said location data can help differentiate brand loyalty vs. new customers.

Location-based data are being used by fundamental active money managers in a variety of ways to help decide whether to move on a stock or bond — often in ways that aren't otherwise obvious, sources said.

"Basic data would be one guy at a McDonald's," said Michael Recce, chief data scientist at Neuberger Berman Group LLC, New York. "But with more data, you could find out that guy is at McDonald's three times a day. That would show brand loyalty. If McDonald's has 10 times growth in sales year over year, in financials, you don't know if that sales growth is from new customers or loyalty. With location data, this provides one to two degrees more information, so you can determine how much of the business is from new customers and how much from existing customers."

Greg Skibiski, founder and CEO of Thasos Group, a New York-based provider of analyzed real-time location data sold to money managers, gave three hypothetical examples of location-based data use by investment professionals:

  • Monitoring employment at manufacturing plants to forecast whether the stock of the companies could go up if production increases or down if the number of workers declines;
  • Gauging the number of people at mining facilities and quarries to determine if production is increasing, and if that is reflected in the current stock price; or
  • Comparing the activity in retail stocks with the number of shoppers at retail outlets. If shares are down but store traffic is up, portfolio managers might use that as a reason to increase their holdings, hoping to cash in if the price goes up when same-store sales figures are released.

At Schroder Investment Management, Mark Ainsworth, London-based head of data insights, provided examples of how the firm's portfolio managers acted on location-based data. In one, Schroder decided against investing in a retailer that had intended to do an initial public offering for its Brazilian operations after geospatial analysis of location data showed that many of the company's stores were located in close proximity to its competitors. "We laid out precisely how many stores there were near the competitors', and it showed the level of competition was much higher than thought, so the team avoided the IPO."

In fixed income, Mr. Ainsworth said, the data were used to see if the firm would buy debt being issued by a retail pizza chain to pay for store expansion in the U.K. The data allowed Schroder to determine whether the targeted growth areas would put the chain in direct competition with other casual dining restaurants and if so, whether there was enough foot traffic in the area that it could attract new business. Schroder eventually purchased the debt.