When JCPenney reported results for the second quarter, the news came as a surprise — for most investors.
To a small group though, word that the retailer was seeing better than expected traffic was no shock. These investors, mostly hedge funds, pay a firm called RS Metrics for intelligence on JCPenney's parking lots across America.
Using satellite images, RS Metrics could tell that traffic into the stores was rising in April and May, and its clients were able to get alerts on those increases in near real-time.
And, if they chose, they could trade on those: JCPenney's shares jumped more than 10% after the report, over two days in mid-August.
Welcome to the world of "alternative data," where obscure data sets can be turned into tradable information. It's a cottage industry of tech firms that have sprung up in recent years, processing information on everything from the weather to web searches and selling it for thousands of dollars to hedge funds looking for any advantage they can get.
It is legal (with some parameters), and, in part, it's just the application of sophisticated technology to tasks that research analysts have done for years the hard way. In the past, the same hunch about JCPenney might've been gathered by sending an analyst into several of the company's stores to do a "channel check."
For the providers of this data, it's still early days, says Michael Gantcher, head of sales at RS Metrics.
"It is the Wild West, and I mean that in a positive sense," he said. "It isn't bureaucratic, systematized or over-regulated. The buy side can ask a question it needs answered."
Alternative data refers to anything that is raw or unstructured, and is distinct from things like company filings, historic market prices, or investor presentations.
The industry has sprung up amid an explosion in obtainable data over the past decade, taking in everything from mobile-phone data to job postings to traffic data.
"There is a whole class of emerging data, and that comes from the deployment of million of sensors around the world by governments, companies or consumers," said Adam Broun, chief operating officer at Kensho, a startup in the field that's backed by Goldman Sachs.
"There is better weather data, and you can get much more detail on microclimates. There is traffic data, agricultural data, and every smart phone is a data collection tool. There is web data. The list goes on," said Broun.
In its simplest for, the approach is pretty straightforward. If a company reports revenues on a quarterly basis, and an investor is able to see the same revenue trends in an alternative data set — think website hits, road traffic, or construction permits — then they can get a read on quarterly performance ahead of the competition, explained Erik Haines, chief executive of Quanton Data.
"If you are able to do that to allow you to beat consensus, and you have a $100 million position in that company, that has enormous value," he said.
The first adopters of the data-driven trading were hedge funds that focus on company fundamentals and use the additional information to test out their hypothesis, according to Gene Ekster, who has worked with alternative data at companies including 1010data and Point72 Asset Management.
Quantitative funds have also zoned in on this kind of data, along with so-called quantamental funds, which mix quant approaches with bottom-up analysis.
They typically either license data and crunch it using their own internals teams, pay for analysis crunched by third parties, or gather the data themselves. The slide below, from a white paper on alternative data published by Integrity Research Associates and written by Ekster, sets out the process.
Traditional mutual-fund investors are also now moving in to the space too.
Vanguard Group, one of the world's largest asset managers, is using the data to understand market moves better, said John Ameriks, the firm's head of quantitative equity. He cited sources of data ranging from social media to government filings to text analysis.
"We really want to see, well, what’s the causality chain?" he said. "When we see something occurring in the marketplace in conjunction with this type of news, with this type of information, or this type of search activity, why do we think that’s leading to a signal ... and what’s the theory behind why it’s gonna persist? And that’s the hard part."
The sources of the data are springing up too, including companies that are generating "exhaust data" as a byproduct of their own internal functions.
One example is Twitter, which is first and foremost a communications platform, but has found value in the social-sentiment data it is able to pull together.
Elaine Ellis, a marketing manager at Gnip, told Business Insider in September that Twitter sells data to banks and hedge funds directly.
"A lot of companies are looking at data exhaust to see if there is value there," Kensho's Broun said. They tend to be reluctant to do a lot with it, sometimes for confidentiality and regulatory reasons, or because there isn't a lot they want to do it."
To be sure, there are impediments to the use of this data becoming widespread. Privacy is clearly a key issue. A story in August by Bradley Hope at The Wall Street Journal on the tracking of bank cards caused a storm, for example. According to that story, Yodlee, an online personal-finance firm, sold some bank-card transaction data to firms that were then able to accurately gauge spending at specific retailers.
Regulation in the sector is still in its infancy too. There are no well-known legal cases that address the use of harvesting web data for investment purposes, for example, according to Ekster.
"Alternative data research compliance is an increasingly important topic and subject of intense discussion in part due to its regulatory ambiguity," he said in the white paper.
Regardless, it seems that mining raw and unstructured data for investment insights is going mainstream. Investors are hiring data specialists and putting projects in place to make sure they aren't left behind.
"At some point this isn't going to be alternative data anymore," Ekster said.
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