braverock/factorAnalytics: Factor Analytics

Linear factor model fitting for asset returns (three major types- time series, fundamental and statistical factor models); related risk (volatility, VaR and ES) and performance attribution (factor-contributed vs idiosyncratic returns); tabular displays of risk and performance reports; factor model Monte Carlo, single and multiple imputation methods for simulating returns and backfilling unequal histories. 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 dataframe 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.

Getting started

Package details

AuthorEric Zivot, Doug Martin, Sangeetha Srinivasan, Avinash Acharya, Yi-An Chen, and Lingjie Yi
MaintainerSangeetha Srinivasan <[email protected]>
LicenseGPL-2
Version2.0.33
URL http://r-forge.r-project.org/projects/returnanalytics/
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("braverock/factorAnalytics")
braverock/factorAnalytics documentation built on Nov. 11, 2018, 8:28 p.m.