"Each new economic data release is like the start of a new ball game,” says Paul Smilgius, founder and head trader at Sargis, a Chicago-based futures trading firm. “Because it’s potentially the biggest market-moving event of the day, there’s a lot of anticipation.” To help ensure that Sargis makes the right call early in the game, Smilgius relies on a machine-readable economic news feed.

The low-latency feed – supplied by a specialist provider, Need To Know News – is programmed into Sargis’ trading application which automatically fires off the appropriate trade(s) in response to the data contained in the specially-formatted economic data news story. “If US non-farm payrolls came out significantly below estimates, you might want to be a buyer of Eurodollars or short-term interest rates or you might want to be a seller of the S&P,” Smilgius explains. “You need to be able to incorporate as much of the data into your trading strategy as possible because although payrolls may be down this month, the revision (to last month’s figures) may go in the opposite direction.”

Smilgius, a 15-year veteran in the Chicago Mercantile Exchange’s trading pit, has been using machine-readable news to fuel Sargis’ automated trading strategies since making the switch to screen-based trading four years ago. Starting out as a market-maker at CME, LIFFE and Eurex US, Sargis is now largely focused on event-driven and momentum-based trades. “We’ve developed ideas on the screen that had worked in the pit, such as spread-trading and momentum trades, across a range of CME contracts,” says Smilgius.

Because speed of execution is key to the success of Sargis’ trading strategies, it was a logical step for Smilgius to feed machine-readable – also commonly referred to as digitised or elementised – news into the firm’s trading applications with minimal latency. “We started out with another financial news provider, but soon realised we only wanted a specific piece of their coverage, i.e. the economic data releases,” he says. “As well as needing the content being tailored to our needs, we wanted the fastest possible feed.”

Turning news into data

An increasing number of hedge funds, market-makers and prop shops looking for an edge over the competition are exploring the scope of machine-readable news to add an extra dimension to their trading strategies. “Everyone intuitively understands that news moves markets,” says Alan Slomowitz, Director, Institutional Product Development, Dow Jones Content Technology Solutions. “Now traders have turned to more automated means of trading, they want as many different inputs as possible. They started by feeding raw market data into their trading models and algorithms, and now firms like ours have turned news into data that can also be very reliably integrated,” he says.

Dow Jones is one of a growing number of firms – which now range from specialist US-based providers such as Need To Know News and Acquire Media to global information providers including Reuters and Thomson Financial – offering a range of products that enable financial markets clients to respond quickly to news events. The core products are news feeds for trading and archives for model-building, but new ‘sentiment analysis’ tools are supported increasingly sophisticated use of machine-readable news. Low-latency real-time news feeds contain textual information that has been formatted, tagged and identified so that an automated trading model can use the content to take particular trading actions, either on its own or in combination with other data. Archives contain many millions of machine-readable news items from up to 20 years ago that can be used in conjunction with tick data to identify and exploit historical trading patterns.

Providers make news stories machine readable first by putting key elements in a consistent XML format and second by adding meta-data tagging to identify companies, industries, subjects, geographies and other facts. For Thomson Financial, whose archive draws from third-party and acquired news sources, this has meant applying a consistent format and clean ontology across 50 million news stories going back ten years. “Both automated trading strategies and execution algorithms have traditionally relied on only real-time pricing information, lacking the ability to recognise market-moving events in the news,” says Ryan Terpstra, Senior Strategist, Corporate Strategy Group, Thomson Financial. “As a result, people are trying to build news-intelligent models and algorithms to incorporate textual data.”

When bad news is good news

Use of real-time machine-readable news feeds that enable automated trading models to respond quickly to scheduled news items such as economic data releases has rapidly expanded to encompass a wider range of predicted and unpredicted stories. While it is a small step from trading off breaking economic data to responding automatically to scheduled corporate earnings news, many firms are using machine-readable news feeds for risk management rather than alpha-generation purposes at present.

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