Dividend Quartiles with Kenneth French Data

August 1, 2011
By

[This article was first published on Timely Portfolio, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Based on my perception of the last 3 years, I would have expected high dividend stocks to have substantially underperformed low and zero dividend stocks.  Fortunately, just like with size and momentum in Beating Kenneth French Small – High, we can explore the datasets from 1927 with dividend quartiles to see how it looks over the long term.  I thank Kenneth French and R, since I can now disprove my perception.  Strangely the biggest discrepancy from the basic linear model is the high stocks relative to the mid dividend stocks.

For a lot of additional information on high yield stocks, see CSFB Global 2011 Yearbook written by Elroy Dimson, Paul Marsh, and Mike Staunton.

From TimelyPortfolio
From TimelyPortfolio
From TimelyPortfolio

R code (click to download):

#explore dividend data from the
#very helpful Ken French
#for this project we will look at Dividend Portfolios
#http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_D-P.zip   require(PerformanceAnalytics)
require(quantmod)
require(ggplot2)   my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_D-P.zip"
my.tempfile<-paste(tempdir(),"\\frenchmomentum.zip",sep="")
my.usefile<-paste(tempdir(),"\\Portfolios_Formed_on_D-P.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_dividend <- read.table(file=my.usefile,
header = FALSE, sep = "",
as.is = TRUE,
skip = 20, nrows=1002)[,1:5]
colnames(french_dividend) <- c("date","Zero","Low","Mid","High")   #get dates ready for xts index
datestoformat <- french_dividend[,1]
datestoformat <- paste(substr(datestoformat,1,4),
substr(datestoformat,5,7),"01",sep="-")   #get xts for analysis
french_dividend_xts <- as.xts(french_dividend[,2:5],
order.by=as.Date(datestoformat))   french_dividend_xts <- french_dividend_xts/100   #jpeg(filename="performance summary of dividend portfolios.jpg",
# quality=100,width=6.25, height = 8, units="in",res=96)
charts.PerformanceSummary(french_dividend_xts,ylog=TRUE,cex.legend=1.25,
main="Performance by Kenneth French Dividend
Monthly Since 1927"
,
colorset=c("cadetblue3","cadetblue","cadetblue4",
"darkolivegreen3","darkolivegreen","darkolivegreen4"))
mtext("Source: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html",
side=1,adj=0,cex=0.75)
#dev.off()   #get price series of small cap high momentum for system building
#by applying the cumprod function on each column
french_dividend_price <- as.xts(apply(french_dividend_xts[,1:4]+1,FUN="cumprod",MARGIN=2))
#if we wanted to compare to the average of all we could use this
#french_all_price <- cumprod(1+apply(coredata(french_dividend_xts[,c(1:4)]),MARGIN=1,FUN=mean))   #jpeg(filename="linear models of relative strength dividend.jpg",
# quality=100,width=6.25, height = 8, units="in",res=96)
par(mfrow=c(3,1))
for (i in 1:3) {
french_rs <- log(french_dividend_price[,4]/french_dividend_price[,i])
model <- lm(french_rs[,1]~index(french_rs))
plot(french_rs,type="l",
main=paste("French High Dividend to ",colnames(french_dividend_xts)[i]," Dividend",sep=""))
abline(lm(french_rs~index(french_rs)))
}
mtext("Source: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html",
side=1,adj=0,cex=0.75)
#dev.off()   #jpeg(filename="linear residuals of relative strength dividend.jpg",
# quality=100,width=6.25, height = 8, units="in",res=96)
par(mfrow=c(3,1))
for (i in 1:3) {
french_rs <- log(french_dividend_price[,4]/french_dividend_price[,i])
model <- lm(french_rs[,1]~index(french_rs))
plot(runSum(as.xts(model$residuals[,1]),n=24),type="l",
main=paste("Residuals French High Dividend to ",colnames(french_dividend_xts)[i]," Dividend",sep=""))
}
mtext("Source: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html",
side=1,adj=0,cex=0.75)
#dev.off()
#another way to chart
# chartSeries(french_rs,theme="white") ,
# TA="addTA(as.xts(model$fitted.values[,1]),on=1)");addTA(runSum(as.xts(model$residuals[,1]),n=24))")             #######################################################
#for later possibly
#apply earlier charts and systems from momentum to the dividend data   #speedy solution for ranking from Charles Berry
# http://r.789695.n4.nabble.com/efficient-rolling-rank-td2013535.html
#rolling rank of price over last 12 months (12 means max price for last 12)
#pad first 11 with NA
nper <- 10
x.rank <- c(rep(NA,nper-1),rowSums(coredata(french_high_price)[ -(1:(nper-1)) ] >= embed(french_high_price,nper)))
x.rank <- as.xts(x.rank,order.by=index(french_high_price))
signalRank <- ifelse(x.rank[,1] > 3 |
french_high_price >= runMax(french_high_price,2),1,0)
retRank <- lag(signalRank,k=1)*french_dividend_xts[,4]   #try rolling 10 month moving average popularized by Mebane Faber
signalAvg <- ifelse(french_high_price > runMean(french_high_price,n=10),1,0)
retAvg <- lag(signalAvg,k=1)*french_dividend_xts[,4]   #try RSI
signalRSI <- ifelse(RSI(french_high_price,n=4) > 45,1,0)
retRSI <- lag(signalRSI,k=1)*french_dividend_xts[,4]   retCompare <- merge(retRank,retAvg,retRSI,french_dividend_xts[,4])
colnames(retCompare) <- c("Small.High.Rank",
"Small.High.Avg","Small.High.RSI","Small.High.Benchmark")
#jpeg(filename="performance of small-high with systems.jpg",quality=100,width=6.25, height = 8, units="in",res=96)
charts.PerformanceSummary(retCompare,ylog=TRUE,cex.legend=1.2,
colorset = c("cadetblue","darkolivegreen3","purple","gray70"),
main="Kenneth French Small Size and High Momentum Stocks
Compared To Various Price Systems"
)
#dev.off()   #jpeg(filename="capture of small-high with systems.jpg",quality=100,width=6.25, height = 8, units="in",res=96)
chart.CaptureRatios(retCompare[,1:3],retCompare[,4],
main="Kenneth French Small Size and High Momentum Stocks
Compared To Various Price Systems"
)
#dev.off()   #get average of small size for additional system testing
french_dividend_avg <- cumprod(1+apply(coredata(french_dividend_xts[,c(1:4)]),MARGIN=1,FUN=mean))
french_dividend_avg <- as.xts(french_dividend_avg,order.by=index(french_high_price))
nper <- 10
x.rank <- c(rep(NA,nper-1),rowSums(coredata(french_dividend_avg)[ -(1:(nper-1)) ] >= embed(french_dividend_avg,nper)))
x.rank <- as.xts(x.rank,order.by=index(french_dividend_avg))
signalRankAvg <- ifelse(x.rank[,1] > 3 |
french_dividend_avg >= runMax(french_dividend_avg,2),1,0)
retRankAvg <- lag(signalRankAvg,k=1)*french_dividend_xts[,4]   retCompare <- merge(retRank,retRankAvg,french_dividend_xts[,3])
colnames(retCompare) <- c("Small.High.Rank",
"Small.High.RankOnAvg","Small.High.Benchmark")
#jpeg(filename="performance of small-high with 2 rank systems.jpg",quality=100,width=6.25, height = 8, units="in",res=96)
charts.PerformanceSummary(retCompare,ylog=TRUE,cex.legend=1.2,
colorset = c("cadetblue","darkolivegreen3","gray70"),
main="Kenneth French Small Size and High Momentum Stocks
Compared To Rank-based Price Systems"
)
#dev.off()       chart.QQPlot(french_dividend_xts[,4],
main = "Normal Distribution", distribution = 'norm', envelope=0.95)
library(MASS)
fit = fitdistr(1+french_dividend_xts[,3], 'lognormal')
chart.QQPlot(1+french_dividend_xts[,3], main = "Log-Normal Distribution", envelope=0.95, distribution='lnorm', meanlog = fit$estimate[[1]], sdlog = fit$estimate[[2]])
library(sn)
fit = st.mle(y=french_dividend_xts[,3])
chart.QQPlot(french_dividend_xts[,3], main = "Skew T Distribution", envelope=0.95, distribution = 'st', location = fit$dp[[1]], scale = fit$dp[[2]], shape = fit$dp[[3]], df=fit$dp[[4]])       chart.Histogram(french_dividend_xts[,4], methods = c( "add.density", "add.normal") )
chart.Histogram(french_dividend_xts[,1], methods = c( "add.centered", "add.density", "add.rug") )
chart.Histogram(french_dividend_xts[,3], methods = c( "add.centered", "add.density", "add.rug", "add.qqplot") )
chart.Histogram(french_dividend_xts[,3], methods = c("add.density", "add.centered", "add.rug", "add.risk") )     #interesting linearity between momentum
chart.Regression(french_dividend_xts[,4], apply(coredata(french_dividend_xts[,c(1:4)]),MARGIN=1,FUN=mean),
fit="conditional", main="Conditional Beta")

To leave a comment for the author, please follow the link and comment on their blog: Timely Portfolio.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Tags: , ,

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)