Historical Country Returns

#*****************************************************************
# Read historical data from AQR library
# [Betting Against Beta: Equity Factors, Monthly](https://www.aqr.com/library/data-sets/betting-against-beta-equity-factors-monthly)
#******************************************************************
library(readxl)
library(SIT)
library(quantmod)

data = env()
data.set = 'betting-against-beta-equity-factors'

# monthly market returns in excess of t-bills
data$market.excess = load.aqr.data(data.set, 'monthly', 'MKT')

#monthly U.S. Treasury bill rates.
data$risk.free = load.aqr.data(data.set, 'monthly', 'RF', last.col2extract = 2)

# total market return
aqr.market = data$market.excess + as.vector(data$risk.free)

# check data starting dates
start.dates = function(data) {
	sapply(colnames(data), function(n) 
		format( index(na.omit(data[,n])[1]), '%Y %b %d')
	)
}

print( sort(start.dates(aqr.market)) )

#*****************************************************************
# Read historical data from Kenneth R. French - Data Library
# [U.S. Research Returns Data](http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors.zip)
# [International Research Returns Data](http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_International_Countries.zip)
#******************************************************************
data.set = 'F-F_Research_Data_Factors'
data = get.fama.french.data(data.set, 'months')
	US.Mkt.RF = data[[1]]$Mkt.RF
	US.RF = data[[1]]$RF

data.set = 'F-F_International_Countries'
data = get.fama.french.data(data.set, 'months', file.suffix='')

temp = env()
for(n in ls(data))
	temp[[n]] = make.stock.xts(data[[n]][[1]]$.Mkt)
temp$US = make.stock.xts(US.Mkt.RF + US.RF)

bt.prep(temp, align='keep.all', fill.gaps = F)
	ff.market = temp$prices

print( sort(start.dates(ff.market))	)

#*****************************************************************
# Compare data: different universe => different returns
#******************************************************************
# convert data into same monthly format	
yyyymm = function(x) make.xts( coredata(x), as.Date(ISOdate(date.year(index(x)),date.month(index(x)),1)))	
	aqr.market = 100*yyyymm(aqr.market)
	ff.market = yyyymm(ff.market)	
	
# map country codes between AQR and FF	
codes = country.code()
aqr.map = codes[,'name']
	names(aqr.map) = codes[,'code3']
	
ff.map = names(ff.market)	
	names(ff.map) = ff.map
	# add custom mapping
	ff.map['Austrlia'] = 'Australia'
	ff.map['HongKong'] = "Hong Kong, Special Administrative Region of China"
	ff.map['Swtzrlnd'] = 'Switzerland'
	ff.map['Singapor'] = 'Singapore'
	ff.map['NewZland'] = "New Zealand"
	ff.map['UK'] = "United Kingdom"
	ff.map['US'] = "United States of America"
	ff.map['Nethrlnd'] = "Netherlands"
	ff.map['Nethrlnd'] = "Netherlands"

# print differences
setdiff( aqr.map[names(aqr.market)] , ff.map[names(ff.market)] )

setdiff( ff.map[names(ff.market)] , aqr.map[names(aqr.market)] )

# plot side by side
layout(matrix(1:4,2,2))
common = intersect( aqr.map[names(aqr.market)] , ff.map[names(ff.market)] )
for(n in common) {
	aqr.index = which(aqr.map[names(aqr.market)]==n)
	ff.index = which(ff.map[names(ff.market)]==n)

	temp = merge(na.omit(aqr.market[,aqr.index]), na.omit(ff.market[,ff.index]))
		temp = na.omit(temp)	
	temp = bt.apply.matrix(1+temp/100,cumprod)
	
	plota(temp[,1], col='black', type='l', main=n, ylim = range(temp))
		plota.lines(temp[,2], col='blue', type='l')
		
	plota.legend(paste(spl('AQR,FF'), n),'black,blue',as.list(temp))
}