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why trend is not your friend on french industries
#get very helpful Ken French data
#for this project we will look at Industry Portfolios
#my.url will be the location of the zip file with the data
#this will be the temp file set up for the zip file
#my.usefile is the name of the txt file with the data
download.file(my.url, my.tempfile, method="auto",
quiet = FALSE, mode = "wb",cacheOK = TRUE)
#read space delimited text file extracted from zip
french_industry <- read.table(file=my.usefile,
header = TRUE, sep = "", = TRUE,
skip = 9, nrows=12211)
#get dates ready for xts index
datestoformat <- rownames(french_industry)
datestoformat <- paste(substr(datestoformat,1,4),
#get xts for analysis
french_industry_xts <- as.xts(french_industry[,1:NCOL(french_industry)],
#divide by 100 to get percent
french_industry_xts <- french_industry_xts/100
#delete missing data which is denoted by -0.9999
french_industry_xts[which(french_industry_xts < -0.99,arr.ind=TRUE)[,1],
unique(which(french_industry_xts < -0.99,arr.ind=TRUE)[,2])] <- 0
#get price series or cumulative growth of 1
french_industry_price <- cumprod(french_industry_xts+1)
#do 200 day moving average for initial testing
#just change n to test on other widths or rolling period
ma <- as.xts(apply(french_industry_price, MARGIN = 2, FUN = runMean, n=200), = index(french_industry_price))
#do plot to test reasonableness
#set up system to enter when price moves above moving average
#exit when below
ma.system <- lag(as.xts(
apply(french_industry_price > ma, MARGIN = 2, as.numeric), = index(french_industry_price)),
k=1) * french_industry_xts
#get returns cumulative and annualized for the entire period
ret.comp.cumul <- Return.cumulative(ma.system) - Return.cumulative(french_industry_xts) <- Return.annualized(french_industry_xts)
ret.comp.ann <- Return.annualized(ma.system) -
rownames(ret.comp.ann) <- "Out(under)performance"
#get colors to use for heat map style coloring by out/under performance
brew <- brewer.pal(name="RdBu",n=5)
#get color ramp
cc.brew <- colorRampPalette(brew)
#apply color ramp
cc <- cc.brew(length(ret.comp.ann))
#do colors based on out/under performance but with gray so visible when labelling
cc.palette <- colorRampPalette(c(cc[1],"gray60",cc[length(cc)]))
cc.levpalette <- cc.palette(length(ret.comp.ann))
cc.levels <- level.colors(ret.comp.ann, at = do.breaks(c(-max(abs(ret.comp.ann)),max(abs(ret.comp.ann))),length(ret.comp.ann)),
col.regions = cc.levpalette)
#plot(ret.comp.cumul ~ Return.cumulative(french_industry_xts),pch=19)
#plot out/underperformance of ma system vs annualized compounded return of buyhold
pch=19, col=cc.levels,bty="l",
xlab = "Ann. Return of BuyHold", ylab = "Out(under)Performance of MA")
text(,y=ret.comp.ann, labels=colnames(ret.comp.ann), cex=0.7, pos=2, col=cc.levels)
#plot out/underperformance of ma system vs std. dev. of buyhold
plot(y=ret.comp.ann, x=apply(na.omit(french_industry_xts),MARGIN=2,sd),
xlab = "StdDev of BuyHold", ylab = "Out(under)Performance of MA")
text(y=ret.comp.ann, x=apply(na.omit(french_industry_xts),MARGIN=2,sd), labels=colnames(ret.comp.ann), cex=0.7, pos=2, col=cc.levels)
#plot out/underperformance of ma system vs Omega of buyhold
plot(y=ret.comp.ann, x=Omega(french_industry_xts),
xlab = "Omega of BuyHold", ylab = "Out(under)Performance of MA")
text(y=ret.comp.ann, x=Omega(french_industry_xts), labels=colnames(ret.comp.ann), cex=0.7, pos=2, col=cc.levels)
#using rugarch get garch stats similar to those explored in the research
spec = ugarchspec(
mean.model=list(armaOrder=c(1,1), include.mean=T))
#set up function to get garch stats through apply function
gfNa <- function(data, spec) {
x <- na.omit(coredata(data))
gf <- suppressWarnings(ugarchfit(spec=spec, data=x))
stats <- coef(gf)
#do apply to get garch stats across all industries
gf.stats <- apply(french_industry_xts[,1:NCOL(french_industry_xts)],MARGIN=2,FUN=gfNa,spec=spec)
#plot return comparison by each garch stat
for (i in 1:NROW(gf.stats)) {
plot(y=ret.comp.ann, x=gf.stats[i,],
pch=19, col=cc.levels, bty="l",
xlab=rownames(gf.stats)[i], ylab="Out(under)Performance of MA")
text(y=ret.comp.ann, x=gf.stats[i,], labels=colnames(ret.comp.ann), cex=0.7, pos=2, col=cc.levels)
#thinking through linear model
#linmod = lm(as.vector(ret.comp.ann)~as.vector(gf.stats[1,]))
#do parallel coordinates chart with color
col = cc.levels,lwd = 2,
main = "Out(under)Performance by GARCH Stat")
col = cc.levels,lwd = 2,
main = "Out(under)Performance by GARCH Stat")
#get rolling out(under) performance for horizon chart
#change na to 0 in ma.system returns
unique(which(,arr.ind=TRUE)[,2])] <- 0
ma_system_price <- cumprod(1+ma.system)
roc <- french_industry_price
#split into groups so do not run out of memory
for (i in seq(12,48,by=12)) {
#get difference in rolling performance
roc[,((i-11):(i))] <- ROC(ma_system_price[,((i-11):(i))],n=250,type="discrete") -
roc[1:250,] <- 0
#do a horizon plot of all 48 industries with horizonscale of 0.25
horizonscale=0.25, #feel free to change to whatever you would like
scales = list(tck = c(1,0), y = list(draw = FALSE,relation = "same")),
origin = 0,
colorkey = FALSE,
#since so many industries, we will comment out grid
# panel = function(x, ...) {
# panel.horizonplot(x, ...)
# panel.grid(h=3, v=0,col = "white", lwd=1,lty = 3)
# },
ylab = list(rev(colnames(roc)), rot = 0, cex = 0.7, pos = 3),
xlab = NULL,
par.settings=theEconomist.theme(box = "gray70"),
#use ylab above for labelling so we can specify FALSE for strip and strip.left
strip = FALSE,
strip.left = FALSE,
main = "Moving Averages System Performance on French Daily 48 Industry 1963-2011\n source:")
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