We are living in an age of information overload where businesses are swamped with information from all angles. What happens when companies don’t use information in the best way possible? Rowland Park and Simon Gregory, co-founders of next-generation financial research technology company, Limeglass, argue that the true cost of not optimising financial research is a loss of opportunity.
Information is everywhere, from the moment we wake up to the minute we go to sleep. We are bombarded with imagery and sounds from multiple sources all competing for our attention and overwhelming the senses.
As individuals, our activities are regularly recorded in detail at many points so that our needs are identified and our wishes can be predicted. Streaming services suggest films and TV series we might want to watch based on our previous consumption. Successful online retailers provide options based not only on what we have already bought, but also on the purchases of other people with similar demographic profiles and preferences.
Our lives are being empowered through technology and the data we inherently provide through our actions, leads to new options being presented back to us. We organise our personal lives through our access to technology and information, but we are not always using information in a way that is best for our business. At work we are inundated with documents and emails from numerous sources; how do we plough through it all to find the answers we want and make the right decisions in a timely manner?
Volume of information, yet lack of direction
In the arena of financial research, the huge mass of available information means that it is very difficult for market participants to effectively assess and use the analysis they receive, which can lead to suboptimal outcomes.
The problem is compounded by the fact that research publications have a rich structure and cover a wide variety of topics where context shifts from paragraph to paragraph. Up until now, there has been a lack of technical innovation in the field of research.
Many analysts and traders use their email inbox for storing research. The problem is that they may entirely miss useful insights as there is no effective way to quickly locate the information. The best they can do is search for a phrase within their inbox and look at the subjects of the emails.
Aggregators have tried to solve this issue but are still relying upon a document-centric approach which is not particularly suitable, as indexing this type of content at document level has its limitations. Full-text search struggles with nuances in meaning and any shifting context. Reliance on other techniques, such as the prevalent method of document tagging tends to lead to documents being either under or over-tagged, regardless of whether they were classified by an analyst or an automated system.
Ultimately, whether using an inbox or a document aggregator, results are typically presented as a list of documents or emails requiring users to click into each one and read through the contents for relevant information. This is hugely inefficient and it is easy to miss key insights.
A bond trader wanting to purchase UK Gilts might see information in a report on UK 10-year Gilts, but would miss sections talking about UK 10-year bonds in other articles on say the UK’s GDP or on the Bank of England. Where one piece of information is not spotted, the overall analysis may be skewed. It is a bit like trying to build a house but not reading the research on the risk of flooding in the area.
The challenge for research publishers is that potentially hours of work and valuable insights may not find their way to the right audiences as they are not easily discoverable. The problem is compounded by the fact that the research producer does not have a way to identify which parts of their reports were most valuable to their audiences, even when the audience is an internal team.
So, how can we ensure that more research is used, and less key information is missed?
In the sphere of financial research, this can be done by ‘Research Atomisation’. Limeglass automatically atomises research documents with its rich natural language processing solution. This allows for the smart tagging of each paragraph whilst taking into account the actual context of the article. Atomising and tagging these unstructured publications unlocks the power to surface any number of relevant market topics from a multitude of documents, from ESG to M&A activity.
The result offers a smart, relevant, detailed and convenient manner of consuming data. In providing such intelligence, these actionable insights add real value and a competitive edge to market participants. In the world of financial services, having a competitive advantage is, of course, a key factor to success.