#use monthly factor data from Betting Against Beta paper
#This file contains monthly return of the Betting against Beta factors used in Frazzini A and L.H. Pedersen (2013), “Betting Against Beta"
#Copyright ©2013 Andrea Frazzini and Lasse Heje Pedersen
#combine factor data from Fama/French
#See Fama and French, 1993
#"Common Risk Factors in the Returns on Stocks and Bonds"
#Journal of Financial Economics
require(gdata)
require(PerformanceAnalytics)
require(factorAnalytics)
require(quantmod)
require(rCharts)
#read spreadsheet
babFactors <- read.xls(
"//www.econ.yale.edu/~af227/data/BAB%20factors%20-%20Frazzini%20and%20Pedersen.xlsx"
,pattern = "caldt"
,blank.lines.skip = T
,stringsAsFactors = F
)
#convert spreadsheet data to R xts
#remove % with gsub, make numeric, and divide by 100
babFactors.xts <- as.xts(
do.call(cbind,
lapply(
babFactors[,-1]
,function(x){
df<-data.frame(as.numeric(
gsub(
x=x
,pattern="%"
,replacement=""
)
)/100)
colnames(df) <- colnames(x)
return(df)
}
)
) #date is first column; will use in order.by
,order.by = as.Date(paste0(babFactors[,1],"-01"),format="%Y%m-%d")
)
#now read Fama/French Factor data
my.url="//mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors.zip"
my.tempfile<-paste(tempdir(),"\\F-F_Research_Data_Factors.zip",sep="")
my.usefile<-paste(tempdir(),"\\F-F_Research_Data_Factors.txt",sep="")
download.file(my.url, my.tempfile, method="auto",
quiet = FALSE, mode = "wb",cacheOK = TRUE)
unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)
#read space delimited text file extracted from zip
french_factors <- read.table(file=my.usefile,
header = TRUE, sep = "",
as.is = TRUE,
skip = 3, nrows=1052)
#get dates ready for xts index
datestoformat <- rownames(french_factors)
datestoformat <- paste(substr(datestoformat,1,4),
substr(datestoformat,5,7),"01",sep="-")
#get xts for analysis
french_factors.xts <- as.xts(french_factors,
order.by=as.Date(datestoformat))
french_factors.xts <- french_factors.xts/100
factorsUS <- na.omit(merge(french_factors.xts,babFactors.xts[,1]))
colnames(factorsUS)[5] <- "BAB"
#not necessary but grab DJIA from FRED for sanity check
djia <- to.monthly(getSymbols("DJIA",src="FRED",auto.assign=F))[,4]
colnames(djia) <- "DowJonesIndu"
index(djia) <- as.Date(index(djia))
returnsFactors <- na.omit(
merge(
ROC(djia,n=1,type="discrete")
,factorsUS
)
)
betasRolling <- rollapply(
returnsFactors[,-1]
, width = 36 #3 year or 36 month rolling
, by.column=FALSE
, by=1
, FUN = function(x){
fit.time <- fitTimeSeriesFactorModel(
assets.names=colnames(x[,1]),
factors.names=colnames(x[,-c(1,4)]),
data=x,
fit.method="OLS"
)
return(fit.time$beta)
}
)
colnames(betasRolling) <- colnames(returnsFactors)[-c(1,2,5)]
require(reshape2)
betasRolling.melt <- melt(data.frame(index(betasRolling),betasRolling),id.vars=1)
colnames(betasRolling.melt) <- c("date", "factor", "beta")
nBeta <- nPlot(
beta ~ date,
group = "factor",
data = na.omit(betasRolling.melt),
type = "multiBarChart", #lineChart #stackedAreaChart, #bar, area don't work with negative
height = 400,
width = 700
)
#nBeta$chart(stacked = TRUE, useInteractiveGuideline=TRUE)
nBeta$xAxis(tickFormat =
"#!function(d) {return d3.time.format('%Y')(new Date(d * 24 * 60 * 60 * 1000));}!#"
)
nBeta$yAxis(tickFormat =
"#!function(d) {return d3.format('0.2f')(d);}!#"
)
#nBeta
nBeta$params$type = "lineChart"
nBeta
#chart.Correlation(returnsFactors)