The FactorAnalytics package contains fitting and analysis methods for the three main types of factor models used in conjunction with portfolio construction, optimization and risk management, namely fundamental factor models, time series factor models and statistical factor models. The purpose of this project is to add key improvements to the package that will make it its basic features and capabilities close to those of commercial portfolio optimization and risk management products.
S&P Global Market Intelligence has kindly provided firm fundamentals data referred to as “scores” or “alpha factors” for educational use in the open source FactorAnalytics R package. The data is contained in the R data frame object “factorDataSPGMI” consisting of the following cross-section of scores for approximately 300 stocks from 1990 to 2015: AccrualRatioCF, AnnVol12M, Beta60M, BP, Chg1YEPS, DivP, EBITDAEV, EP, EQ-style, LogMktCap, PM12M1M, ROE. This data greatly facilitates the educational value to users of the fundamental factor model in FactorAnalytics. The package developers wish to thank S&P Global Market Intelligence for contributing this data to the FactorAnalytics package.
To get started, you can install the package from github using devtools.
library(devtools)
install_github("braverock/FactorAnalytics")Because of the large size of the factor data files, if you are going to load the repository from source and want access to the large binary data files, your git installation needs to support git large file storage (LFS). Instructions are here:
R Script and slides used in Prof. Douglas Martin's "Fundamental Factor Models in FactorAnalytics" Pre-Conference Seminar.
Click here for the background slide deck for the Boston useR group talk by Prof. Doug Martin.