NEW YORK (July 24): When it comes to gaining that elusive trading edge, data is fast becoming the new frontier whether it comes from Fitbits, Rokus and Teslas or employment websites like Glassdoor.com.
That’s why some of the world’s biggest hedge funds, from Steve Cohen’s Point72 Asset Management to Ken Griffin’s Citadel, have been snapping up large swaths of alternative data. Many are paying big money for it.
“There is not one major hedge fund or asset manager that doesn’t have data initiatives underway or that are not using alternative data in some way,” said Michael Marrale, chief executive officer of M Science, a firm that provides data and analytics to hedge funds.
Spotting trends and patterns in consumer habits is big business, part of a global market for big data, that a JPMorgan Chase & Co. report said could reach more than $200 billion by next year. Still, there’s no guarantee all that information will lead to riches. It needs to be scrubbed, organized and aggregated to be of any use.
Here’s some data that funds are watching.
WiFi and Bluetooth-Enabled Devices
WiFi and Bluetooth connections have become so ubiquitous they’re often taken for granted. But hedge funds have become keenly interested in tracking devices that connect to the internet.
Capturing signals they emit can show “when and where new things appear in the world,” said Hugh O’Connor, director of data sourcing and partnerships at Eagle Alpha, which gathers alternative data for the finance industry.
Firms can keep tabs on the number of Roku video-streaming devices or Fitbit fitness trackers being used, the length of time consumers spend on them and their approximate locations. Similarly, if you buy a Tesla Model 3 car and use its Bluetooth-enabled media, a data provider can capture when your new ride is hitting the road.
There’s been “incredible demand” from some of the world’s largest asset managers for this type of information, Marrale said.
Data culled from mobile-phone use can reveal, in real time, the number of people carrying devices at a particular location. This can shed light on how many -- or few -- people are frequenting a retailer, supermarket or fast-food joint.
Firms can also monitor app downloads: how popular they are, where they’re occurring and when they’re being used to make purchases.
Hedge funds have set up internal units to scrape the biggest warehouse of information there is: the internet. They’re sifting sites to create bespoke collections of public data. Some examples include pricing trends on airline flights or hotels, inventory figures for products offered on coupon website Groupon, or sales posted for merchandise on Amazon.com.
How often are people tweeting about Apple’s newest iPhone? Is the latest Nike sneaker a hit with teens? Firms have started tracking key words or phrases on social-media sites including Facebook and Instagram to gauge what consumers are thinking. That information can be mapped to various companies, providing clues about the popularity of a product or service.
Credit Card Data
Consumer transaction data has been widely used for years, whether it’s tracking what and how much people charge to their credit cards, receipts sent to email inboxes, or which sites accept online payment services like Venmo. Given its potential, this type of data can be more expensive than other sets.
“Credit card data can range from $150,000 to over a million dollars a year, depending on certain characteristics such as the level of granularity,” said Daniel Goldberg, founder of Alternative Data Analytics.
Firms can collect reams of information on job postings, changes in compensation and employee reviews of companies. They can scrape websites like Glassdoor or dig through the IRS notices employers file on their benefit plans.
All kinds of intelligence can be gleaned from this material. If a tech giant suddenly starts seeking talent from the health-care industry, for example, that could suggest it has a new product or service in the works. A spike in the removal of job postings from a company’s website could signal corporate distress.