Wall Street’s insatiable lust: Data, data, data

Data server


Erik Haines, head of data and analytics at New York-based Guidepoint Global, trawls the globe for meaningful data to sell to hedge fund clients. One of his best strategies is to attend the most seemingly mundane gatherings, such as the Association for Healthcare Resource & Materials Management conference in San Diego last year, and the National Industrial Transportation League event in New Orleans.

“I walk the floor, try to talk to companies and get a sense within an industry of who collects data that could provide a unique insight into that industry,” he said.

Hedge funds and other sophisticated investors are increasingly relying on intermediaries like Haines, 35 years old, as they seek insights into a company’s sales and health that aren’t readily available from conventional sources.

The information ranges from crop yields calculated by satellite images and linguistic analyses of speeches by CEOs to credit-card transactions and monitoring of sentiment about companies on social media.

The accuracy of this data is a subject of debate within the investment world, with some arguing the information is based around samples riddled with biases and errors.

Data hunters scour the business world for companies that have data useful for predicting the stock prices of other companies. For instance, a company that processes transactions at stores could have market-moving information on how certain products or brands are selling or a company that provides software to hospitals could give insights into how specific medical devices are being used.

Gone are the days when a hedge fund would call up a random sampling of Aéropostale stores to ask managers about sales or simply visit big-box retailers to get a feel for the traffic.

In one recent example, Haines discovered a mobile advertising company that also collected data on the type of device someone was using when displaying an ad to them. The data helped estimate iPhone sales ahead of Apple’s announcements in 2011 and 2012, and it was lucrative for Haines’s old company, Quanton Data.

He and the team from Quanton joined Guidepoint, a company that has traditionally provided experts and survey data to customers, earlier this summer.

Some hedge funds have built data-hunting teams internally, especially so-called quants whose strategies rely entirely on finding patterns in large sets of data. Quants typically analyse market data – prices and volume over time – but increasingly are taking those skills to this type of data, which is called “exhaust,” because it is a secondary result of a company’s main business.

WorldQuant, a quantitative hedge fund based in Connecticut, has a team that reviews hundreds of data sets a year and works to bring online as many as possible that provide some value, according to a person familiar with its strategy. Its staff of scientists and mathematicians then go to work on the data to see if it helps predict revenues at companies or other market phenomena.

A host of startups also are trying to make it easier for funds without high-powered data-science staffers to get the same insights. One, called Quandl, based in Toronto, offers a platform that includes traditional market data alongside several “alternative” data.

“The opportunity we are chasing is that in all this huge data there are little nuggets of alpha gold,” said Tammer Kamel, its founder and CEO.

The firm struck a deal with a large insurance company to find out every day what kinds of cars received insurance policies, a possible indicator of how sales are going for automobile manufacturers.

Another deal is with a company that surveys construction permits across county municipal offices, which is a “proxy for construction activity,” he said. While there are indexes that compile official construction numbers from the same data, the company’s goal is to be ahead of these indexes and take advantage of the government’s infrequent updates.

Most data in the world is fairly useless for predicting the prices of stocks and other securities, which makes data hunting all the more difficult, he said. Some cite social media as a poor predictor of company behaviour.

There are also companies set up to create exhaust. In those cases, often a person’s data is the price of a free phone application or service.

For example, app provider Slice Technologies lets users track the arrival of packages to their homes in its signature Slice app or block spam through another service it owns called Unroll.me without charge.

But in exchange for those services, about four million users allow the company to read their emails. Slice, in turn, also analyses receipts and other data in a person’s email which it packages into anonymised data for advertisers and hedge funds. It might show Amazon.com Inc. selling more of a particularly profitable item or an increase in Netflix subscriptions, which investors can use as a factor in their trades.

Slice users agree to let the company use their data for other purposes, provided it is anonymised, when they sign up.

The data can also be useful in private-equity transactions by giving investors information about sales at private companies such as Uber Technologies Inc. and Airbnb, said Jaimee Minney, vice president of communications at Slice. “We can see average fares, number of customers and demographics,” she said.

Uber declined to comment and Airbnb didn’t respond to a request for comment.

Write to Bradley Hope at bradley.hope@wsj.com

This article was published by The Wall Street Journal

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