System from Trend Following Factors

As I thought more about Trend Following Factors from Hsieh and Fung, I thought that the trend following factors might indicate a state/regime for the equity markets that could potentially offer momentum-style timing signals for a system on the S&P 500.  Now, THIS ABSOLUTELY SHOULD NOT BE CONSIDERED INVESTMENT ADVICE, especially since the factor data is very lagged and the testing is nowhere near comprehensive enough.  I will however try to replicate the factor methodology to get a more real-time indicator extended to any index in another post. What is most interesting to me is that this is ex-ante intuitive and the signal is just basic statistics.

From TimelyPortfolio

From TimelyPortfolio

From TimelyPortfolio

Comments have been very light. Please let me know your thoughts.

R code from GIST:

	###########NOT INVESTMENT ADVICE######################
	#extend the trend following factors into a system for trading S&P 500
	#Hsieh, David A. and Fung, William,
	#The Risk in Hedge Fund Strategies: Theory and Evidence from Trend Followers.
	#The Review of Financial Studies, Vol. 14, No. 2, Summer 2001 .
	#Available at SSRN: http://ssrn.com/abstract=250542
	#http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-Fac.xls
	require(gdata)
	require(quantmod)
	require(PerformanceAnalytics)
	require(FactorAnalytics)
	URL <- "http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-Fac.xls"
	#get xls sheet TF-Fac starting at the row with yyyymm
	hsieh_factor <- read.xls(URL,sheet="TF-Fac",pattern="yyyymm",stringsAsFactors=FALSE)
	hsieh_factor.clean <- hsieh_factor
	#clean up date to get to yyyy-mm-dd
	hsieh_factor.clean[,1] <- as.Date(paste(substr(hsieh_factor[,1],1,4),
	substr(hsieh_factor[,1],5,6),
	"01",sep="-"))
	#remove percent sign and make numeric
	hsieh_factor.clean[,2:6] <- apply(
	apply(hsieh_factor[,2:6],
	MARGIN=2,
	FUN=function(x) {gsub("%", "", x)}),
	MARGIN=2,
	as.numeric)/100
	#get rid of NAs
	hsieh_factor.clean <- hsieh_factor.clean[,1:6]
	hsieh_factor.xts <- as.xts(hsieh_factor.clean[,2:6],order.by=hsieh_factor.clean[,1])
	chart.CumReturns(hsieh_factor.xts,
	main="Hsieh and Fung Trend Following Factors",
	xlab=NA,
	legend.loc="topleft")
	mtext(text="Source: http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-Fac.xls",
	side=3,adj=0.10,outer=TRUE, col="purple",cex=0.75,line=-4)
	chart.Correlation(hsieh_factor.xts,main="Hsieh and Fung Trend Following Factors")
	mtext(text="Source: http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-Fac.xls",
	side=1,adj=0.10,outer=TRUE, col="purple",cex=0.75,line=-1.5)
	#get edhec data for sample factor analysis
	data(edhec)
	cta <- edhec[,1]
	index(cta)=as.Date(format(index(cta),"%Y-%m-01"))
	cta.factors <- na.omit(merge(cta,hsieh_factor.xts))
	chart.RollingStyle(cta.factors[,1],cta.factors[,2:NCOL(cta.factors)],
	width=36,
	colorset=c("darkseagreen1","darkseagreen3","darkseagreen4","slateblue1","slateblue3","slateblue4"),
	main="Edhec CTA by Trend Following Factors Rolling 36 Months")
	mtext(text="Source: http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-Fac.xls",
	side=1,adj=0.10,outer=TRUE, col="purple",cex=0.75,line=-5)
	#in one line get SP500 data, convert to monthly, and get 1-month rate of change
	GSPC.roc <- ROC(to.monthly(get(getSymbols("^GSPC",from="1900-01-01")))[,4],n=1,type="discrete")
	colnames(GSPC.roc) <- "SP500"
	#convert date to yyyy-mm-01 so we can merge properly
	index(GSPC.roc) <- as.Date(index(GSPC.roc))
	#merge factor data with ROC data
	roc.factors <- na.omit(merge(GSPC.roc,hsieh_factor.xts))
	#graph 6 month rolling correlation
	chart.RollingCorrelation(roc.factors[,2:NCOL(roc.factors)],roc.factors[,1],n=6,
	legend.loc="topleft",main="Correlation (Rolling 6-month)")
	chart.RollingCorrelation(roc.factors[,6],roc.factors[,1],n=6,
	legend.loc="topleft",main="PTFSSTK (Stock) Correlation (Rolling 6-month)")
	abline(h=-0.6,col="red")
	abline(h=0.5,col="green")
	#get rolling 6 month correlation versus all the factors
	correl <- as.xts(
	apply(roc.factors[,2:NCOL(roc.factors)],MARGIN=2,runCor,y=roc.factors[,1],n=6),
	order.by=index(roc.factors))
	#do simple system where long if correlation with stock trend following factor is low
	#as defined by a band of -0.6 and 0.5
	system <- lag(ifelse(correl[,5] > -0.6 & correl[,5] < 0.5,1,0)) * GSPC.roc
	#see how it works
	charts.PerformanceSummary(merge(system,GSPC.roc))

view raw system from trend following factors.r hosted with ❤ by GitHub