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.
2020-07-22 Update: The final version of the paper is now published at RAC.
Back in May 2020, I started to work on a new paper regarding the use of Garch models in R. Today we finished the peer review process and finally got a final version of the article and code. I’m glad to report that the content improved significantly.
In a nutshell, the paper motivates GARCH models and presents an empirical application using R: given the recent COVID-19 crisis, we investigate the likelihood of Ibovespa index reach its peak value once again in the upcoming years.
Myself and Henrique Martins (PUC Rio) organized a call for papers on data reuse, for publication in RAC – Revista de Administração Contemporanea. The deadline for submission is 10th october 2020, with expected publication date in july 2021.
We will select quality papers that use data already published and shared in other scientific outlet to test new theories or present a tutorial article, helping students see how an econometric result was achieved in practice.
You can find more details about this call for papers in this pdf file. I encourage everyone to submit their work.
I’ve been researching financial data for over 10 years and gathered a great deal of compiled tables. Most of these comes from my R packages and have been used for creating class material, doing research and even writing a book. These files were mostly found in many copies across different projects.
Last week I started to organize and centralize all my data files and noticed how valuable these tables could be for other researchers and teachers.
As of today, I’ll be hosting all public compiled data in my website. Most of them are the product of using my R packages in a large scale data grabbing process.
2020-07-18: Package GetCVMData is now renamed GetDFPData2. See this post for details..
Package GetCVMData is an alternative to GetDFPData. Both have the same objective: fetch corporate data of Brazilian companies trading at B3, but diverge in their source. While GetDFPData imports data directly from the DFP and FRE systems, GetCVMData uses the CVM ftp site for grabbing compiled .csv files.
When doing large scale importations, GetDFPData fells sluggish due to the parsing of large xml files. As an example, building the dataset available in my data page takes around six hours of execution using 10 cores of my home computer.
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:
Myself, Mauro Mastella, Daniel Vancin and Henrique Ramos, just finished a tutorial paper about GARCH models in R and I believe it is a good content for those learning financial econometrics. You can find the full paper in this link.
In a nutshell, the paper introduces motivation behind the GARCH type of models and presents an empirical application: given the recent COVID-19 crisis, we investigate how much time it would take for the Ibovespa index to reach its peak value once again. The results indicate that it would take, on average, about two and half years for the index to recover.
The slides for my newly released book Analyzing Financial and Economic Data with R are finally ready! I apologize for keep you guys waiting.
The slides are available as independent .Rmd files for all book chapters including:
##  "afedR-Slides_Chapter-01_Introduction.Rmd" ##  "afedR-Slides_Chapter-02_BasicOperations.Rmd" ##  "afedR-Slides_Chapter-03_ResearchScripts.Rmd" ##  "afedR-Slides_Chapter-04_ImportingLocal.Rmd" ##  "afedR-Slides_Chapter-05_ImportingInternet.Rmd" ##  "afedR-Slides_Chapter-06_DataStructureObjects.Rmd" ##  "afedR-Slides_Chapter-07_BasicClasses.Rmd" ##  "afedR-Slides_Chapter-08_Programming.Rmd" ##  "afedR-Slides_Chapter-09_CleaningData.Rmd" ##  "afedR-Slides_Chapter-10_Figures.Rmd" ##  "afedR-Slides_Chapter-11_Models.Rmd" ##  "afedR-Slides_Chapter-12_ReportingResults.Rmd" ##  "afedR-Slides_Chapter-13_OptimizingCode.Rmd" All content is released with a generous MIT license, so fell free to use and edit the files as you wish.
After a couple of unexpected delays, I am very pleased to announce the publication of the second edition of my book, Analyzing Financial and Economic Data with R. You can find it in Amazon as an ebook or paperback. An online version is available here. More details, including supplementary material, are available in the book webpage.
The first edition was released back in 2017 and it was a great journey working once again in this material. Many sections and chapters have been improved, along with new content. Here are the main changes:
I’m just about to leave for my vacation and, as usual, I’ll write about the highlights of 2019 and my plans for the year to come. First, let’s talk about my work in 2019.
Highlights of 2019 The year of 2019 was not particularly fruitful in journal publications. I only had two: Accessing Financial Reports and Corporate Events with GetDFPData, published in RBfin and A consumer credit risk structural model based on affordability: balance at risk published in JCR. Both are papers I wrote back in 2017 and 2018 and not new articles.