Let the Lagging Lead

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THIS IS NOT INVESTMENT ADVICE AND WILL PROBABLY WIPE OUT ALL YOUR MONEY IF PURSUED.  While exploring utilities, I discovered a strange phenomenon that I have not quite thoroughly understood, but I attribute to the business cycle.  If I dust off the system proposed in Unrequited lm Love, apply that signal to utilities as my total entry/exit, and then use relative strength to decide utilities or transports (really all cyclicals work with chemicals best), I get some magic. This is much longer than my normal simple process but I think the result might be worth the effort.

From TimelyPortfolio

Although I use the Kenneth French data set (thanks again), the method works very similarly on the Dow Jones series easily obtained through getSymbols with Yahoo! Finance or FRED.

Sloppy R code (Click to Download from Google Docs):

#get very helpful Ken French data
#for this project we will look at Industry Portfolios
#http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/17_Industry_Portfolios.zip   require(PerformanceAnalytics)
require(quantmod)
require(RColorBrewer)   #my.url will be the location of the zip file with the data
my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/17_Industry_Portfolios.zip"
#this will be the temp file set up for the zip file
my.tempfile<-paste(tempdir(),"\\frenchindustry.zip",sep="")
#my.usefile is the name of the txt file with the data
my.usefile<-paste(tempdir(),"\\17_Industry_Portfolios.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_industry <- read.table(file=my.usefile,
header = TRUE, sep = "",
as.is = TRUE,
skip = 11, nrows=1021)   #get dates ready for xts index
datestoformat <- rownames(french_industry)
datestoformat <- paste(substr(datestoformat,1,4),
substr(datestoformat,5,7),"01",sep="-")   #get xts for analysis
french_industry_xts <- as.xts(french_industry[,1:17],
order.by=as.Date(datestoformat))   french_industry_xts <- french_industry_xts/100   #get price series from monthly returns for utilities and transports
Utils <- cumprod(1+french_industry_xts[,14])
Trans <- cumprod(1+french_industry_xts[,13]) #use chemicals #6 for best result
Utilsmean <- runMean(Utils,n=4)   #get relative strength Utils to Transports
UtilsTrans <- Utils/Trans   width = 3
for (i in (width+1):NROW(Utils)) {
linmod <- lm(Utils[((i-width):i),1]~index(Utils[((i-width):i)]))
ifelse(i==width+1,signal <- coredata(linmod$residuals[length(linmod$residuals)]),
signal <- rbind(signal,coredata(linmod$residuals[length(linmod$residuals)])))
ifelse(i==width+1,signal2 <- coredata(linmod$coefficients[2]),
signal2 <- rbind(signal2,coredata(linmod$coefficients[2])))
ifelse(i==width+1,signal3 <- cor(linmod$fitted.values,Utils[((i-width):i),1]),
signal3 <- rbind(signal3,cor(linmod$fitted.values,Utils[((i-width):i),1])))
}   signal <- as.xts(signal,order.by=index(Utils[(width+1):NROW(Utils)]))
signal2 <- as.xts(signal2,order.by=index(Utils[(width+1):NROW(Utils)]))
signal3 <- as.xts(signal3,order.by=index(Utils[(width+1):NROW(Utils)]))
signal4 <- ifelse(Utils > Utilsmean,1,0)   price_ret_signal <- merge(Utils,
lag(signal,k=1),
lag(signal2,k=1),
lag(signal3,k=1),
lag(signal4,k=1),
lag(ROC(Utils,type="discrete",n=4),k=1),
ROC(Utils,type="discrete",n=1))
price_ret_signal[,2] <- price_ret_signal[,2]/price_ret_signal[,1]
price_ret_signal[,3] <- price_ret_signal[,3]/price_ret_signal[,1]
ret <- ifelse((price_ret_signal[,5] == 1) | (price_ret_signal[,5] == 0 &
runMean(price_ret_signal[,3],n=12) > 0 & runMean(price_ret_signal[,2],n=3) < 0 ),
1, 0) * price_ret_signal[,7]
retCompare <- merge(ret, price_ret_signal[,7])
colnames(retCompare) <- c("Linear System", "BuyHoldUtils")
#jpeg(filename="performance summary.jpg",
# quality=100,width=6.25, height = 8, units="in",res=96)
charts.PerformanceSummary(retCompare,ylog=TRUE,cex.legend=1.2,
colorset=c("black","gray70"),main="Utils System Return Comparison")   #eliminate NA at start of return series
retCompare[is.na(retCompare)] <- 0
price_system <- merge(Utils,ifelse((price_ret_signal[,5] == 1) |
(price_ret_signal[,5] == 0 &
runMean(price_ret_signal[,3],n=12) > 0 &
runMean(price_ret_signal[,2],n=3) < 0 ),
NA, 1),coredata(Utils)[width+12]*cumprod(retCompare[,1]+1))
price_system[,2] <- price_system[,1]*price_system[,2]
colnames(price_system) <- c("In","Out","System")   chartSeries(price_system$System,theme="white",log=TRUE,up.col="black",
yrange=c(min(price_system[,c(1,3)]),max(price_system[,c(1,3)])),
TA="addTA(price_system$In,on=1,col=3);
addTA(price_system$Out,on=1,col=2)"
,
name="Utils Linear Model System")   #let's try an easy relative strength signal to choose small cap or large cap
#know I can do this better in R but here is my ugly code
#to calculate 12 month or 1 year slope of utils/trans
width=12
#get relative strength slope of high beta/low vol
for (i in 1:(NROW(UtilsTrans)-width)) {
model<-lm(UtilsTrans[i:(i+width),1]~index(UtilsTrans[i:(i+width)]))
ifelse(i==1,indexRS<-model$coefficients[2],indexRS<-rbind(indexRS,model$coefficients[2]))
}
indexRS<-xts(cbind(indexRS),order.by=index(UtilsTrans)[(width+1):NROW(UtilsTrans)])   price_ret_signal <- na.omit(merge(price_ret_signal, lag(indexRS,k=1), ROC(Trans,type="discrete",n=1)))
#use same linear system signal for in out but add RS to choose Russell 2000 or SP500
retRS <- ifelse((price_ret_signal[,5] == 1) | (price_ret_signal[,5] == 0 &
runMean(price_ret_signal[,3],n=12) > 0 & runMean(price_ret_signal[,2],n=3) < 0 ),
1, 0) * ifelse(price_ret_signal[,8]<0,price_ret_signal[,9],price_ret_signal[,7])
retCompareWithRS <- na.omit(merge(retRS,retCompare,ROC(Trans, n=1, type="discrete")))
colnames(retCompareWithRS) <- c("Linear.With.RS",colnames(retCompareWithRS)[2:3],
"BuyHoldTrans")   #jpeg(filename="performance summary.jpg",
# quality=100,width=6.25, height = 8, units="in",res=96)
charts.PerformanceSummary(retCompareWithRS,ylog=TRUE,cex.legend=1.2,
main="Utility and Transports System Rotator", colorset=brewer.pal(4,"Paired"))
mtext("Source: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html",
side=1,adj=0,cex=0.75)
#dev.off()   corr <- runCor(price_ret_signal[,7],price_ret_signal[,9],n=12)
plot.zoo(corr)

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