REITs for Everybody Might Now Mean REITs for Nobody

require(quantmod)
require(PerformanceAnalytics)   
#get NAREIT data
#I like NAREIT since I get back to 1971
#much easier though to get Wilshire REIT from FRED
#also it is daily instead of monthly
#getSymbols("WILLREITIND",src="FRED") will do this
require(gdata) 
reitURL <- "http://returns.reit.com/returns/MonthlyHistoricalReturns.xls"
reitExcel <- read.xls(reitURL,sheet="Data",pattern="All REITs",stringsAsFactors=FALSE)
#clean up dates so we can use xts functionality later
datetoformat <- reitExcel[,1]
datetoformat <- paste(substr(datetoformat,1,3),"-01-",substr(datetoformat,5,6),sep="")
datetoformat <- as.Date(datetoformat,format="%b-%d-%y")
reitExcel[,1] <- datetoformat     
#############now start the yield analysis#####################
#get REIT yield
reitYield <- as.xts(as.numeric(reitExcel[4:NROW(reitExcel),7]),
	order.by=reitExcel[4:NROW(reitExcel),1])
######get BAA and 10y from Fed to compare
getSymbols("BAA",src="FRED")
getSymbols("GS10",src="FRED")
######get SP500 yield from some multpl.com
##fantastic site with easily accessible historical information
spYield <- read.csv("http://www.multpl.com/s-p-500-dividend-yield/s-p-500-dividend-yield.csv")
spYield <- as.xts(spYield[,2],order.by=as.Date(spYield[,1]))
yieldCompare <- na.omit(merge(reitYield,spYield,BAA,GS10))
chart.TimeSeries(yieldCompare, legend.loc = "topleft",cex.legend=1.2,lwd=3,
	main="Yield Comparison of REITs with S&P500, BAA Yield, and US 10y Yield",
	colorset = c("cadetblue","darkolivegreen3","goldenrod","gray70"))
#get yield spread information
yieldSpread <- yieldCompare[,1:3]
yieldSpread[,1] <- yieldCompare[,1]-yieldCompare[,2]
yieldSpread[,2] <- yieldCompare[,1]-yieldCompare[,3]
yieldSpread[,3] <- yieldCompare[,1]-yieldCompare[,4]
colnames(yieldSpread) <- c("REIT Yield - S&P500 Yield",
	"REIT Yield - BAA Yield","REIT Yield - US 10y Yield")
chart.TimeSeries(yieldSpread, legend.loc = "topleft",cex.legend=1.2,lwd=3,
	main="Yield Spreads of REITs with S&P500, BAA Yield, and US 10y Yield",
	colorset = c("cadetblue","darkolivegreen3","goldenrod"))     
#############now start the return analysis###################
#shift colnames over 1
colnames(reitExcel) <- colnames(reitExcel)[c(1,1:(NCOL(reitExcel)-1))]
#get dates and return columns
reitData <- reitExcel[,c(3,24,38)]
#name columns
colnames(reitData) <- c(paste(colnames(reitExcel)[c(3,24,38)],".Total.Return",sep=""))
reitData <- reitData[3:NROW(reitData),]
#erase commas
col2cvt <- 1:NCOL(reitData)
reitData[,col2cvt] <- lapply(reitData[,col2cvt],function(x){as.numeric(gsub(",", "", x))}) 
#create xts
reitData <- as.xts(reitData,order.by=reitExcel[3:NROW(reitExcel),1])
#######get sp500 to compare beta and other measures
getSymbols("SP500",src="FRED")
SP500 <- to.monthly(SP500)[,4]
#get 1st of month to align when we merge
index(SP500) <- as.Date(index(SP500))
#merge REIT and S&p
returnCompare <- na.omit(merge(reitData,SP500))
returnCompare <- ROC(returnCompare,n=1,type="discrete")
charts.RollingRegression(returnCompare[, 1:3], returnCompare[,4],
	width=36,lwd = 3,legend.loc = "topleft",cex.legend=1.2,
	main="NAREIT REIT Indexes Compared to the S&P 500
	36 month Rolling",
	colorset=c("cadetblue","darkolivegreen3","goldenrod"))
chart.RollingPerformance(returnCompare,
	FUN="Return.annualized",width=36,lwd = 3,legend.loc = "topleft",cex.legend=1.2,
	main="NAREIT REIT Indexes Compared to the S&P 500
	36 month Rolling Return",
	colorset=c("cadetblue","darkolivegreen3","goldenrod","gray70"))
chart.RiskReturnScatter(returnCompare["1971::2003"],
	lwd = 3,legend.loc = "topleft",cex.legend=1.2,
	main="NAREIT REIT Indexes Compared to the S&P 500 1971-2003",
	colorset=c("cadetblue","darkolivegreen3","goldenrod","gray70"))
chart.RiskReturnScatter(returnCompare["2004::"],
	lwd = 3,legend.loc = "topleft",cex.legend=1.2,
	main="NAREIT REIT Indexes Compared to the S&P 500 Since 2004",
	colorset=c("cadetblue","darkolivegreen3","goldenrod","gray70"))
charts.PerformanceSummary(returnCompare,ylog=TRUE,
	lwd = 3,legend.loc = "topleft",cex.legend=1.2,
	main="NAREIT REIT Indexes Compared to the S&P 500",
	colorset=c("cadetblue","darkolivegreen3","goldenrod","gray70"))         
#############now start the bucket analysis###################
#bucket momentum as described by Aleph Blog
#get 10 month moving average
#set up avg with same as reitData
avg <- reitData[,1:3]
avg <- as.data.frame(avg)
avg[,1:3] <- lapply(reitData[,1:3],runMean,n=10)
avg <- as.xts(avg)
#get % above 10 month moving average
momscore <- reitData/avg-1
#break into 5 evenly distributed by frequency quintiles
#get signal into 3 column xts
signal <- momscore
for(i in 1:3) {
	breaks <- quantile(momscore[,i], probs = seq(0, 1, 0.20),na.rm=TRUE)
	#use default labels=TRUE to see how this works
	buckets <- cut(momscore[,i], include.lowest=TRUE, breaks=breaks)
	#store so we can see later
	ifelse(i==1,bucket_ranges <- names(table(buckets)),
		bucket_ranges <- rbind(bucket_ranges,names(table(buckets))))
	#now use labels=FALSE to return 1-5 based on quintile
	buckets <- cut(momscore[,i], breaks=breaks, labels=FALSE)
	signal[,i] <- as.xts(buckets,order.by=index(signal))
	#move forward by 1
}
#name bucket_ranges with reit column names
rownames(bucket_ranges)<-colnames(reitData)
signal <- lag(signal,k=1)
ret <- signal
#showing my R weakness here and had to go back to for..next
for(i in 1:3) {
	ret[,i] <- ifelse(signal[,i] >= 3,1,0) * ROC(reitData[,1],1,type="discrete")
}
charts.PerformanceSummary(ret,ylog=TRUE,legend.loc = "topleft",cex.legend=1.2,
	main="NAREIT REIT Index Data with Aleph Blog Momentum",
	colorset=c("cadetblue","darkolivegreen3","goldenrod"))     
getSymbols("DJIA",src="FRED")
#examine DJIA quantiles prior to 1973 to see if we could
#know in advance what possible REIT quantiles would work
DJIA <- to.monthly(DJIA)["1896::1971",4]
momDJIA <- DJIA/runMean(DJIA,n=10)-1
breaks <- quantile(momDJIA, probs = seq(0, 1, 0.20),na.rm=TRUE)
buckets <- cut(momDJIA, breaks=breaks)
table(buckets)   
#what happens if we apply the DJIA prior to 1973 buckets to the REITs
ret <- merge(ret,ret)
for(i in 1:3) {
	#if REITs > 3.95% above 10 month moving average then long
	#3.95% is the lower end of the DJIA 1896-1971 3 momentum quantile
	ret[,i+3] <- lag(ifelse(momscore[,i] >= 0.0395,1,0),1) * ROC(reitData[,1],1,type="discrete")
}
colnames(ret)[4:6]<-paste(colnames(reitData[,1:3])," with DJIA buckets",sep="")
#much much better than I expected
charts.PerformanceSummary(ret,ylog=TRUE,legend.loc = "topleft",cex.legend=1.2,
	main="NAREIT REIT Index Data with Aleph Blog Momentum but DJIA Momentum Buckets",
	colorset=c("cadetblue","darkolivegreen3","goldenrod",
	"coral","darkorchid","darkolivegreen"))