In msperlin/GetITRData: Reading Financial Reports from Bovespa's ITR System

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.

Package GetITRData provides a R interface to all financial statements available in the website. 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 quarterly financial statements in large scale research, facilitating the reproducibility of corporate finance studies with B3 data.

Installation

The package is 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('GetITRData') # not in CRAN yet

# Development version in Github
devtools::install_github('msperlin/GetITRData')

How to use `GetITRData`

The starting point of GetITRData is to find the official names of companies in Bovespa. Function gitrd.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(GetITRData)
library(tibble)

gitrd.search.company('petrobras')

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 quarterly statements can be accessed with function gitrd.get.info.companies. It will read and parse a .csv file from my github repository. This will be periodically updated for new quarterly statements. Let's try it out:

df.info <- gitrd.get.info.companies(type.data = 'companies')

glimpse(df.info)

This file includes several information that are gathered from Bovespa: names of companies, sectors, dates quarterly statements and, most importantly, the links to download the files. The resulting dataframe can be used to filter and gather information for large scale research such as downloading financial data for a specific sector.

Downloading financial information for ONE company

All you need to download financial data with GetITRData are the official names of companies, which can be found with gitrd.search.company, the desired starting and ending dates and the type of financial information (individual or consolidated). Let's try it for PETROBRAS:

name.companies <- 'PETRÓLEO BRASILEIRO  S.A.  - PETROBRAS'
first.date <- '2004-01-01'
last.date  <- '2006-01-01'
type.statements <- 'individual'
periodicy.fin.report <- 'annual'

df.reports <- gitrd.GetITRData(name.companies = name.companies, 
                               periodicy.fin.report = periodicy.fin.report, 
                               first.date = first.date,
                               last.date = last.date,
                               type.info = type.statements)

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:

glimpse(df.reports)

Object 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 income statements for all desired years of PETROBRAS are:

df.income.long <- df.reports$fr.income[[1]]

glimpse(df.income.long)

The resulting dataframe is in the long format, ready for processing. In the long format, financial statements of different quarters are stacked. In the wide format, we have the quarters as dates. If you want the wide format, which I believe is most common in financial analysis, you can use function gitrd.convert.to.wide. See an example next:

df.income.wide <- gitrd.convert.to.wide(df.income.long)

knitr::kable(df.income.wide )

Downloading financial information for SEVERAL companies

If you are doing serious research, it is likely that you need financial statements for more than one company. Package GetITRData is specially designed for handling large scale download of data. Let's build a case with 3 randomly selected companies:

set.seed(2)
my.companies <- sample(unique(df.info$name.company), 5)

first.date <- '2008-01-01'
last.date  <- '2010-01-01'
type.statements <- 'individual'
periodicy.fin.report <- 'annual'

df.reports <- gitrd.GetITRData(name.companies = my.companies, 
                               periodicy.fin.report = periodicy.fin.report,
                               first.date = first.date,
                               last.date = last.date,
                               type.info = type.statements)

And now we can check the resulting tibble:

glimpse(df.reports)

Every row of df.reports will provide information for one company. Metadata about the corresponding dataframes such as min/max dates is available in the first columns. Keeping a tabular structure facilitates the organization and future processing of all financial data. We can use tibble df.reports for creating other dataframes in the long format containing data for all companies. See next, where we create dataframes with the assets and liabilities of all companies:

df.assets <- do.call(what = rbind, args = df.reports$fr.assets)
df.liabilities <- do.call(what = rbind, args = df.reports$fr.liabilities)

df.assets.liabilities <- rbind(df.assets, df.liabilities)

As an example, let's use the resulting dataframe for calculating and analyzing a simple liquidity index of a company, the total of current (liquid) assets (Ativo circulante) divided by the total of current short term liabilities (Passivo Circulante), over time.

library(dplyr)

my.tab <- df.assets.liabilities %>%
  group_by(company.name, ref.date) %>%
  summarise(Liq.Index = acc.value[acc.number == '1.01']/ acc.value[acc.number == '2.01'])

my.tab

Now we can visualize the information using ggplot2:

library(ggplot2)

p <- ggplot(my.tab, aes(x = ref.date, y = Liq.Index, fill = company.name)) +
  geom_col(position = 'dodge' )
print(p)

As we can see, we could not find available data for all companies. Nonetheless, JPSP is the company with highest liquidity, being able to pay its short term debt with the current assets in all years. We can certainly do a lot more interesting studies based on this data set.

Exporting financial data

The package includes function gitrd.export.ITR.data for exporting the financial data to an Excel file. Users can choose between the long and wide format. See next:

my.basename <- 'MyExcelData'
my.format <- 'xlsx' # only supported so far
gitrd.export.ITR.data(data.in = df.reports, 
                      base.file.name = my.basename,
                      type.export = my.format,
                      format.data = 'long')