Back in 2017 I wrote the first version of package GetDFPData, along with a paper describing the code and providing an empirical application.
However, maintaining the package over the years has been frustrating. The code is becoming increasingly complex, much due to the fact that it handles FRE and DFP data in a single package. Execution speed for large scale importation – many years and many companies – is not reasonable. In top of that, B3’s website is unstable as a source of data and it seems it will stay like that for a long time.
After battling B3’s website for days, I finally managed to gather a master table for all corporate data. I’m happy to report that the 2019’s data is now included in GetDFPData, the CRAN package and shiny interface. This includes new financial statements and company’s FRE data.
I also want to use this update to formally thank everyone that made a donation in the shiny website. Your donation is not only helping paying for the bills of the server but increasing my motivation for working further in this project.
As for R code with then new dataset, let’s give it a try:
The shiny version of GetDFPData is currently hosted in a private server at DigitalOcean. A problem with the basic (5 USD) server I was using is with the low amount of available memory (RAM and HD). With that, I had to limit all xlsx queries for the data, otherwise the shiny app would ran out of memory. After upgrading R in the server, the xlsx option was no longer working.
Today I tried all tricks in the book for keeping the 5 USD server and get the code to work.
I just released a major update to package GetDFPData. Here are the main changes:
Naming conventions for caching system are improved so that it reflects different versions of FRE and DFP files. This means the old caching system no longer works. If you have built yourself your own cache folder with many companies, do clean up the cache by deleting all folders. Run your code again and it will rebuild all files. Unfortinatelly this is a “brute force”, but necessary step. The code and data is now explicit about the version of downloaded files.
Financial statements of companies traded at B3 (formerly Bovespa), the Brazilian stock exchange, are available in its website. Accessing the data for a single company is straightforward. In the website one can find a simple interface for accessing this dataset. An example is given here. However, gathering and organizing the data for a large scale research, with many companies and many dates, is painful. Financial reports must be downloaded or copied individually and later aggregated. Changes in the accounting format thoughout time can make this process slow, unreliable and irreproducible.