A long time ago, when I was studying “Management and Business Administration” in Switzerland, I thought it would be cool to be able to calculate Financial KPIs in order to compare different companies within one sector or to be able to identify sector specific differences.
Well, in Switzerland we still don’t have any requirement to file the Financial Reports electronically and unfortunately this will not change anytime soon. Fortunately there are countries which are more progressive, like e.g. the US which makes the information available via EDGAR or the UK with CompaniesHouse. A complete overview of countries which support XBRL can be found here.
This information might be useful to drive investment decisions. So it was my goal to create a comprehensive set of KPIs across different dimensions which can be used by machine learning. In this document we will use EDGAR to calculate KPIs to measure the following dimensions of a reporting company
The solution has been implemented with JupyterLab using the BeakerX Scala kernel with the following additional libraries:
– Smart EDGAR (to access the EDGAR data)
– jupyter-jdk-extensions (to display our custom tables in BeakerX)
The result can be find in the following Gist
It is the expectation that the stock price of companies with better KPIs will grow faster than their competitors. So in my next blog we will evaluate the KPIs against the stock prices.