Structure your Unstructured Data!
Investment Research is, by its nature, a curious combination of highly structured information (Macro or Micro financial forecasts, Equity Ratings, financial ratios) with completely unstructured information (investment theses, background information, company descriptions).
Some of the structured information can be used to ensure that people are reading the right content and that certain important connections have been picked up. When an Oil analyst publishes a piece updating their Brent Crude forecast, for example, if that forecast is stored in a database, it can be flagged to colleagues (perhaps Airline analysts and Economists) who rely on it for inputs to their own models.
However, there are plenty of situations where that sort of useful automated cascading of information is impossible because the information is not structured. And this is where paragraph-level topic correlations comes in.
When an Economist writes about the relationship between consumer spending and jobless claims, you would hope that the Economist’s Consumer Stock analyst colleagues would read the research to understand hard or soft impacts on their forecasts. Do they need to update the growth rates plugged into their DCF valuation models?
Sell Side Analysts are Resource-Rich but Time-Poor
But even though they theoretically have this information available to them, they are unlikely to read it. Analysts are resource-rich but time-poor. And, given that the information they need is unstructured text, there is no way to set up a system to flag it to the right people.
However, if that document has been processed and atomised by Limeglass, the content tags for each paragraph will be readily available and an automated system could be built off that information. ‘Consumer Spending’ and ‘Jobless Claims’ would be separate tags that would both occur in this paragraph.
If the Consumer Stock analysts were set up to receive alerts for the ‘Consumer Spending’ tag, they would be made aware that there is a potentially interesting co-occurrence with the ‘Jobless Claims’ tag. This may pique their interest enough to read the relevant paragraph, thus improving the information flow between different subject matter experts on the Sell Side.