There must be a useful insight, concept, or system provided by the new S&P 500 High Beta and Low Volatility Indexes. Now with the announcement by Powershares of etfs for these indicies http://www.invescopowershares.com/volatility/, any of these potential insights, concepts, or systems seem viable. The indexes are available through the S&P website in spreadsheet form
http://www.standardandpoors.com/indices/sp-500-low-volatility/en/us/?indexId=spusa-500-usdw-lop-us-l--
http://www.standardandpoors.com/indices/sp-500-high-beta/en/us/?indexId=spusa-500-usdw-hbp-us-l--
or through Bloomberg with SP5HBIT for High Beta and SP5LVIT Low Volatility.
If we apply some basic techniques of relative strength and momentum introduced in previous posts, we can build a switching strategy between the High Beta and Low Volatility Indexes.
From TimelyPortfolio |
Or with the same signals, we can use the relative strength (RS) signal to generate entry and exit signals for the overall S&P 500 index.
From TimelyPortfolio |
R code:
require(quantmod)
require(PerformanceAnalytics)
#don't think it is possible to download directly from S&P
#but can get a spreadsheet to use from
#http://www.standardandpoors.com/indices/sp-500-low-volatility/en/us/?indexId=spusa-500-usdw-lop-us-l--
#http://www.standardandpoors.com/indices/sp-500-high-beta/en/us/?indexId=spusa-500-usdw-hbp-us-l--
#also these indicies are available through Bloomberg and Reuters
#Bloomberg access with R is possible through RBloomberg
SPHighBetaLowVol<-as.xts(read.csv("sphighbeta-lowvol.csv",row.names=1,stringsAsFactors=FALSE))
SPIndexes<-merge(SPHighBetaLowVol,getSymbols("^GSPC",from="2006-01-03",auto.assign=FALSE)[,4])
SPIndexesReturns<-ROC(SPIndexes,1,type="discrete")
charts.PerformanceSummary(SPIndexesReturns)
#let's try an easy relative strength signal and index filter
#know I can do this better in R but here is my ugly code
#to calculate 125 day or 1/2 year slope of high beta/low vol
width=125
#get relative strength slope of high beta/low vol
for (i in 1:(NROW(SPIndexes)-width)) {
model<-lm(SPIndexes[i:(i+width),1]/SPIndexes[i:(i+width),2]~index(SPIndexes[i:(i+width)]))
ifelse(i==1,indexRS<-model$coefficients[2],indexRS<-rbind(indexRS,model$coefficients[2]))
}
indexRS<-xts(cbind(indexRS),order.by=index(SPIndexes)[(width+1):NROW(SPIndexes)])
#get slope of total S&P on shorter term 75 day
width=75
for (i in 1:(NROW(SPIndexes)-width)) {
model<-lm(SPIndexes[i:(i+width),3]~index(SPIndexes[i:(i+width)]))
ifelse(i==1,indexSlope<-model$coefficients[2],indexSlope<-rbind(indexSlope,model$coefficients[2]))
}
indexSlope<-xts(cbind(indexSlope),order.by=index(SPIndexes)[(width+1):NROW(SPIndexes)])
signals<-na.omit(merge(lag(indexRS),lag(indexSlope),SPIndexesReturns))
ret<-ifelse(signals[,1]>0&signals[,2]>0,signals[,3],ifelse(signals[,1]<0&signals[,2]>0,signals[,4],0))
perf_compare<-merge(ret,SPIndexesReturns)
#name the columns for charting
colnames(perf_compare)<-c("RotationSystem",colnames(perf_compare)[2:4])
charts.PerformanceSummary(perf_compare,main="Rotation System with S&P Indexes)
#now let's use the RS signal to determine entry exit to overall SP index
#when high volatility is outperforming go long S&P 500
ret<-ifelse(signals[,1]>0,signals[,5],0)
perf_compare<-merge(ret,perf_compare)
colnames(perf_compare)<-c("SP500TacticalBasedOnRS",colnames(perf_compare)[2:5])
charts.PerformanceSummary(perf_compare,main="Systems Comparisons with S&P Indexes)
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