finRes

The finRes suite provides a collection of packages developed to facilitate data-science and/or research in finance and financial economics. In particular, it provides helper packages for retrieving and storing locally financial data from Bloomberg as well as for processing this data further for financial modeling for example. finRes is organised as a set of packages that work in harmony because they share common data representations and ‘API’ design. This package is designed to make it easy to install and load multiple ‘finRes’ packages in a single step.
The development version can be installed from github using devtools with devtools::install_github("bautheac/finRes").

Datasets

finRes is home to a number of packages that, although self-contained with consumption value on their own, host datasets that play important roles in the finRes suite, mostly in relation to data collection, storage and wrangling but also to analytics and asset pricing in particular. At the time of writing, the set of dataset packages in finRes includes: BBGsymbols, fewISOs, GICS, FFresearch and factors.

Bloomberg

The finRes suite is organised around the data-science pipeline where preprocessing, including data collection and wrangling, plays a major role. finRes addresses the issue in two complementary packages that work in conjunction with most of the dataset packages above.
On the one hand the pullit package provides tools for data collection from Bloomberg. It returns clean and tidy, ready-to-use, data objects for other packages further down the pipeline to work with. On the other hand the storethat package helps storing the data retrieved with pullit for off-Bloomberg consumption in R.
Both pullit and storethat work in tandem with the BBGsymbols package. The latter plays a central role in finRes where it provides the former the semantic required to interact with Bloomberg through the interface provided by the Rblpapi package (Armstrong, Eddelbuettel, and Laing 2021).

Asset pricing

At the time of writing, the analytics part of the pipeline in finRes focuses on asset pricing. On the one hand the FFresearch package abovementioned provides data on classic asset pricing factors and a number of sort portfolios. The data is pulled directly from Kenneth French’s data library and tidied up for seamless consumption in R. The factors package provides complementary datasets for other popular factors in the literature including those developed by Robert F. Stambaugh and collaborators: Lubos Pastor, Yu Yuan, etc. On the other hand the factorem package provides tools for outright factor construction from data retrieved with pullit. The returned objects carry corresponding return & positions time series and a number of methods to help with performance analysis.

Visualization

The bottom-end of the pipeline (communication) is addressed in the plotit package that provides a number of plot methods for finRes objects.

Packages

finRes packages at the time of writing:
- BBGsymbols: popular Bloomberg tickers and field symbols conveniently packaged for R users.
- fewISOs: a collection of financial economics related ISO code datasets conveniently packaged for consumption in R.
- GICS: Global Industry Classification Standard dataset conveniently packaged for consumption in

R.
- FFresearch: Fama/French asset pricing research data conveniently packaged for consumption by R users.
- factors: Clean time series datasets for asset pricing factors popular in the literature.
- pullit: Bloomberg financial data collection in R made easy.
- storethat: store Bloomberg financial data for off-Bloomberg consumption in R.
- factorem: construct bespoke asset pricing factors.
- plotit: plot methods for the finRes suite.

Going further

See package vignettes for details:

library(finRes)

vignette(topic = "datasets", package = "finRes")

vignette(topic = "Bloomberg", package = "finRes")

vignette(topic = "asset pricing", package = "finRes")

Coming next

finRes is still in the alpha stage of development; bugs have to be fixed and design flaws must be addressed in some of the packages. Once these issues are addressed each individual package as well as the suite itself will eventually be submitted to the Comprehensive R Archive Network (CRAN) for larger public dissemination.

References

Armstrong, Whit, Dirk Eddelbuettel, and John Laing. 2021. Rblpapi: R Interface to ’Bloomberg’. https://CRAN.R-project.org/package=Rblpapi.