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It may have been a long time coming, but it appears that machine-readable news’ time has come. With today’s emphasis on fast, automated and event-driven markets, the ability to tap events and apply them to computer-based models seems obvious. But until relatively recently, the idea of electronically capturing news–or text-based information–for use in automated processes has been the stuff of science fiction.

Today, machine-readable news is used in a range of supporting roles in trading. Aside from generating trading signals for high frequency traders and other alpha- seekers, it can be used to build sentiment measurement applications, stock screening applications and back-testing systems for trading algorithms. It can be used in support of market surveillance systems. And with more of the major newswire organizations getting in on the act, its use seems set to increase.

Key to the successful deployment of any application relying on machine-readable news is a robust metadata methodology and sufficient data to allow back-testing of the business rules and models underlying the programme. Once those two requirements are satisfied, the developer can have confidence in the base-level algorithms, and the focus can switch to the value-added characteristics.

At the low end of the scale, this may translate into simple stock-screening applications, for individual securities or lists of stocks. It can mean the analysis of macroeconomic data to identify trends, correlations and other relationship. It can involve scanning key parameters to measure market sentiment. Increasingly, machine-readable news can be co-opted for algorithmic trading. Recent examples include efforts to predict potentially volatile trading days, indicating which stocks or types of stock may be most affected. It can also be used to quickly derive directional signals from the marketplace, and set in play appropriate trading algorithms.

Yes, a trader can do all of the above. But with increasingly fast markets, low-latency market connections and complex, multi-legged trading models, automation of such processes is emerging as a Holy Grail. Machine-readable news will only play a greater role as underlying data sets become more reliable and market practitioners build on early successes.

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  Machine Readable News and Algorithmic Trading (1.6 MiB)