The increasing pace of the digitalisation of information has resulted in a vast array of new possibilities for systematic investors. While some opportunities are unable to match the hype and hubbub surrounding “alternative” and “big” data, one area in which we have had tangible success is textual analysis.
The Jupiter systematic Team

The Jupiter Systematic team, headed by Amadeo Alentorn, and including Tarun Inani, James Murray, Yuangao Liu, Sean Storey and Matus Mrazik (from left to right)

There has recently been a confluence of advances which, in combination, have made the analysis of text an interesting proposition for systematic investors. These advances include the availability of greater computing resources, the evolution of mathematical methods, and the dramatic increase in both publication and collection of machine-readable text. Alongside the technological advances is a long-standing acknowledgement that valuable information resides within textual data. Non-systematic investors spend significant amounts of time reading news stories, conversing with other market participants and engaging with company management (which are activities of a different kind from analysing numerical data). While it is not clear that all these activities are value accretive, the breadth of possibility coming from being able to elicit information from text is a tantalising prospect. Textual analysis offers the ability to rigorously assess the informational content of a document in much the same way we do with numerical data. However, with vast swathes of data to examine, how do we find valuable information amidst the noise?

The technical challenges associated with textual analysis ar