Trade the news when most only read them — benefit from advanced ultra fast text analytics
by: Markus Schicho
While the interrelation between news and stock quotes is commonly acknowledged, it is the immediate and vigorous impact that often leaves us stunned.
Market regulations demand recurring publication of business figures at predefined dates per company. With prior warnings and expectations from the market, common patterns can be observed in the market — but to participate in the first reaction (and profit from it) you need to understand the given news very quickly, compare the numbers with any expectation and then trade accordingly. Experienced traders say that this time to react is decreasing and down to fractions of a second by now.
This is, of course, limited by the complexity of the news, the different options depending on the published numbers or events, and of course, by human reaction time. How to enable the trader to react faster than others?
First of all, an ultra-fast, automatic “understanding” of the news is necessary — which is, by now, possible with elaborated and high-performance linguistic analytics, a technology implemented at econob: Figures, companies and events can be abstracted from the natural text. Second, allow the trader to predefine different trading actions depending on different figures or events. With prior research, these can be parameterized elegantly. Then, automatic trading tailored to the trader’s expertise and focus is possible — within that fraction of a second, allowing to profit from news that all the others only started to read.
Unexpected news hit the market even more fiercely, often not only affecting stock prices but also resulting in a change in market opinions or customer behavior as they, obviously, can trigger moods and emotions. It is Atrap main goal to provide financial market members with both insight into these mechanisms and the technology to control or react profitably.
Editors Notes: This entry has been submitted to the FINTECH Book, the world’s 1st globally crowd-sourced book on FINTECH. Readers that enjoyed this innitial abstract are invited to share and like it so that it may be featured in a longer version that will published in the FINTECH Book due to be released November 2015.