# Cross Pollination from Systematic Investor

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After reading the fine article Style Analysis from Systematic Investor and What we can learn from Bill Miller and the Legg Mason Value Trust from Asymmetric Investment Returns, I thought I should combine the two in R with the FactorAnalytics package. Let’s explore the Legg Mason Value Trust run by Bill Miller to get some insight into the source of his returns over many years by using the Ken French momentum by size data set as factors.

From TimelyPortfolio |

From TimelyPortfolio |

From TimelyPortfolio |

R code (click to download from Google Docs):

#use Ken French momentum style indexes for style analysis #http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_Prior_12_2.zip require(PerformanceAnalytics) require(FactorAnalytics) require(quantmod) my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_Prior_12_2.zip" my.tempfile<-paste(tempdir(),"\\frenchmomentum.zip",sep="") my.usefile<-paste(tempdir(),"\\6_Portfolios_ME_Prior_12_2.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_momentum <- read.table(file=my.usefile, header = TRUE, sep = "", as.is = TRUE, skip = 12, nrows=1017) colnames(french_momentum) <- c(paste("Small", colnames(french_momentum)[1:3],sep="."), paste("Large",colnames(french_momentum)[1:3],sep=".")) #get dates ready for xts index datestoformat <- rownames(french_momentum) datestoformat <- paste(substr(datestoformat,1,4), substr(datestoformat,5,7),"01",sep="-") #get xts for analysis french_momentum_xts <- as.xts(french_momentum[,1:6], order.by=as.Date(datestoformat)) french_momentum_xts <- french_momentum_xts/100 #get price series from monthly returns french_price<-as.xts( apply(1+coredata(french_momentum_xts[,1:6]),MARGIN=2,cumprod), index(french_momentum_xts)) #check data for reasonability plot.zoo(french_price,log="y") #for this example let's use Bill Miller's fund getSymbols("LMVTX",from="1896-01-01", to=Sys.Date(), adjust=TRUE) LMVTX <- to.monthly(LMVTX) index(LMVTX) <- as.Date(format(as.Date(index(LMVTX)),"%Y-%m-01")) LMVTX.roc <- ROC(LMVTX[,4],type="discrete",n=1) perfComp <- na.omit(merge(LMVTX.roc,french_momentum_xts)) chart.RollingStyle(perfComp[,1],perfComp[,2:NCOL(perfComp)], width=36, colorset=c("darkseagreen1","darkseagreen3","darkseagreen4","slateblue1","slateblue3","slateblue4"), main="LMVTX Rolling 36mo French Momentum Weights") #could use the packaged chart.Style but does not allow the #flexibility I would like #chart.Style(perfComp[,1],perfComp[,2:NCOL(perfComp)], # colorset=c("darkseagreen1","darkseagreen3","darkseagreen4","slateblue1","slateblue3","slateblue4"), # main="LMVTX French Momentum Weights") #get weights for the cumulative period style.weight <- as.matrix(style.fit(perfComp[,1], perfComp[,2:NCOL(perfComp)])$weights) barplot(style.weight,beside=TRUE,ylim=c(0,max(style.weight)+0.2), names.arg=rownames(style.weight),cex.names=0.7, col=c("darkseagreen1","darkseagreen3","darkseagreen4", "slateblue1","slateblue3","slateblue4"), main=paste("LMVTX French Momentum Weights Since ",format(index(LMVTX)[1],"%b %Y"),sep="")) #look at total R to determine goodness of fit style.R <- style.fit(perfComp[,1], perfComp[,2:NCOL(perfComp)])$R.squared styleR <- function(x) { as.numeric(style.fit(R.fund=x[,1,drop=FALSE],R.style=x[,2:NCOL(x),drop=FALSE],method="constrained",selection="none",leverage=FALSE)$R.squared) } #convert to matrix since I get #error "The data cannot be converted into a time series." #when I use xts as data style.RollingR <- as.xts(rollapply(data=as.matrix(perfComp), width=12,FUN=styleR,by.column=FALSE,by=1), order.by=index(perfComp)[12:NROW(perfComp)]) chart.TimeSeries(style.RollingR,ylab="Rolling 12-mo R", main=paste("LMVTX Rolling R versus French Momentum Since ",format(index(LMVTX)[1],"%b %Y"),sep="")) abline(h=style.R,col="indianred") text(x=1,y=style.R,labels="r for entire series",adj=0,col="indianred")

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