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. 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.
GetDFPData provides a R interface to all annual financial statements available in the website and more. It not only downloads the data but also organizes it in a tabular format and allows the use of inflation indexes. Users can select companies and a time period to download all available data. Several information about current companies, such as sector and available quarters are also at reach. The main purpose of the package is to make it easy to access financial statements in large scale research, facilitating the reproducibility of corporate finance studies with B3 data.
The positive aspects of
A white paper about the package is available at SSRN.
The package is (not yet) available in CRAN (release version) and in Github (development version). You can install any of those with the following code:
# Release version in CRAN install.packages('GetDFPData') # Development version in Github ::install_github('msperlin/GetDFPData')devtools
The web interface of
GetDFPData is available at https://www.msperlin.com/shiny/GetDFPData/.
The starting point of
GetDFPData is to find the official names of companies in B3. Function
gdfpd.search.company serves this purpose. Given a string (text), it will search for a partial matches in companies names. As an example, let’s find the official name of Petrobras, one of the largest companies in Brazil:
library(GetDFPData) library(tibble) gdfpd.search.company('petrobras', cache.folder = tempdir())
Its official name in Bovespa records is
PETRÓLEO BRASILEIRO S.A. - PETROBRAS. Data for quarterly and annual statements are available from 1998 to 2017. The situation of the company, active or canceled, is also given. This helps verifying the availability of data.
The content of all available financial statements can be accessed with function
gdfpd.get.info.companies. It will read and parse a .csv file from my github repository. This will be periodically updated for new information. Let’s try it out:
<- gdfpd.get.info.companies(type.data = 'companies', cache.folder = tempdir()) df.info glimpse(df.info)
This file includes several information that are gathered from Bovespa: names of companies, official numeric ids, listing segment, sectors, traded tickers and, most importantly, the available dates. The resulting dataframe can be used to filter and gather information for large scale research such as downloading financial data for a specific sector.
All you need to download financial data with
GetDFPData are the official names of companies, which can be found with
gdfpd.search.company, the desired starting and ending dates and the type of financial information (individual or consolidated). Let’s try it for PETROBRAS:
<- 'PETRÓLEO BRASILEIRO S.A. - PETROBRAS' name.companies <- '2004-01-01' first.date <- '2006-01-01' last.date <- gdfpd.GetDFPData(name.companies = name.companies, df.reports first.date = first.date, last.date = last.date, cache.folder = tempdir())
The resulting object is a
tibble, a data.frame type of object that allows for list columns. Let’s have a look in its content:
df.reports only has one row since we only asked for data of one company. The number of rows increases with the number of companies, as we will soon learn with the next example. All financial statements for the different years are available within
df.reports. For example, the assets statements for all desired years of PETROBRAS are:
<- df.reports$fr.income[] df.income.long glimpse(df.income.long)
The resulting dataframe is in the long format, ready for processing. In the long format, financial statements of different years are stacked. In the wide format, we have the year as columns of the table.
If you want the wide format, which is the most common way that financial reports are presented, you can use function
gdfpd.convert.to.wide. See an example next:
<- gdfpd.convert.to.wide(df.income.long) df.income.wide ::kable(df.income.wide )knitr
The package includes function
gdfpd.export.DFP.data for exporting the financial data to an Excel or zipped csv files. See next:
<- 'MyExcelData' my.basename <- 'csv' # only supported so far my.format gdfpd.export.DFP.data(df.reports = df.reports, base.file.name = my.basename, type.export = my.format)
The resulting Excel file contains all data available in