Modeling Couch Potato strategy

I first read about the Couch Potato strategy in the MoneySense magazine. I liked this simple strategy because it was easy to understand and easy to manage. The Couch Potato strategy is similar to the Permanent Portfolio strategy that I have analyzed previously.

The Couch Potato strategy invests money in the given proportions among different types of assets to ensure diversification and rebalances the holdings once a year. For example the Classic Couch Potato strategy is:

• 1) Canadian equity (33.3%)
• 2) U.S. equity (33.3%)
• 3) Canadian bond (33.3%)

I highly recommend reading following online resources to get more information about the Couch Potato strategy:

• MoneySense
• Couch Potato Portfolio: Introduction
• Couch Potato Portfolio: Meet the potato family
• Canadian Couch Potato
• Couch Potato FAQ
• Model Portfolios
• AssetBuilder
• Introducing: The Couch Potato Building Blocks
• The Couch Potato Portfolios

Today, I want to show how you can model and monitor the Couch Potato strategy with the Systematic Investor Toolbox.

###############################################################################
# Load Systematic Investor Toolbox (SIT)
# https://systematicinvestor.wordpress.com/systematic-investor-toolbox/
###############################################################################
setInternet2(TRUE)
con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb'))
    source(con)
close(con)


	# helper function to model Couch Potato strategy - a fixed allocation strategy
	couch.potato.strategy <- function
	(
		data.all,
		tickers = 'XIC.TO,XSP.TO,XBB.TO',
		weights = c( 1/3, 1/3, 1/3 ), 		
		periodicity = 'years',
		dates = '1900::',
		commission = 0.1
	) 
	{ 
		#*****************************************************************
		# Load historical data 
		#****************************************************************** 
		tickers = spl(tickers)
		names(weights) = tickers
		
		data <- new.env()
		for(s in tickers) data[[ s ]] = data.all[[ s ]]
		
		bt.prep(data, align='remove.na', dates=dates)
	
		#*****************************************************************
		# Code Strategies
		#******************************************************************
		prices = data$prices   
			n = ncol(prices)
			nperiods = nrow(prices)
	
		# find period ends
		period.ends = endpoints(data$prices, periodicity)
			period.ends = c(1, period.ends[period.ends > 0])
	
		#*****************************************************************
		# Code Strategies
		#******************************************************************
		data$weight[] = NA
			for(s in tickers) data$weight[period.ends, s] = weights[s]
		model = bt.run.share(data, clean.signal=F, commission=commission)
		
		return(model)
	} 	

The couch.potato.strategy() function creates a periodically rebalanced portfolio for given static allocation.

Next, let’s back-test some Canadian Couch Potato portfolios:

	#*****************************************************************
	# Load historical data
	#****************************************************************** 
	load.packages('quantmod')	
	map = list()
		map$can.eq = 'XIC.TO'
		map$can.div = 'XDV.TO'		
		map$us.eq = 'XSP.TO'
		map$us.div = 'DVY'			
		map$int.eq = 'XIN.TO'		
		map$can.bond = 'XBB.TO'
		map$can.real.bond = 'XRB.TO'
		map$can.re = 'XRE.TO'		
		map$can.it = 'XTR.TO'
		map$can.gold = 'XGD.TO'
			
	data <- new.env()
	for(s in names(map)) {
		data[[ s ]] = getSymbols(map[[ s ]], src = 'yahoo', from = '1995-01-01', env = data, auto.assign = F)
		data[[ s ]] = adjustOHLC(data[[ s ]], use.Adjusted=T)	
	}
		
	#*****************************************************************
	# Code Strategies
	#****************************************************************** 
	models = list()
		periodicity = 'years'
		dates = '2006::'
	
	models$classic = couch.potato.strategy(data, 'can.eq,us.eq,can.bond', rep(1/3,3), periodicity, dates)
	models$global = couch.potato.strategy(data, 'can.eq,us.eq,int.eq,can.bond', c(0.2, 0.2, 0.2, 0.4), periodicity, dates)
	models$yield = couch.potato.strategy(data, 'can.div,can.it,us.div,can.bond', c(0.25, 0.25, 0.25, 0.25), periodicity, dates)
	models$growth = couch.potato.strategy(data, 'can.eq,us.eq,int.eq,can.bond', c(0.25, 0.25, 0.25, 0.25), periodicity, dates)
	
	models$complete = couch.potato.strategy(data, 'can.eq,us.eq,int.eq,can.re,can.real.bond,can.bond', c(0.2, 0.15, 0.15, 0.1, 0.1, 0.3), periodicity, dates)	
	
	models$permanent = couch.potato.strategy(data, 'can.eq,can.gold,can.bond', c(0.25,0.25,0.5), periodicity, dates)	
		
	#*****************************************************************
	# Create Report
	#****************************************************************** 
	plotbt.custom.report.part1(models)

I have included a few classic Couch Potato portfolios and the Canadian version of the Permanent portfolio. The equity curves speak for themselves: you can call them by the fancy names, but in the end all variations of the Couch Potato portfolios performed similar and suffered a huge draw-down during 2008. The Permanent portfolio did a little better during 2008 bear market.

Next, let’s back-test some US Couch Potato portfolios:

	#*****************************************************************
	# Load historical data
	#****************************************************************** 
	tickers = spl('VIPSX,VTSMX,VGTSX,SPY,TLT,GLD,SHY')
	
	data <- new.env()
	getSymbols(tickers, src = 'yahoo', from = '1995-01-01', env = data, auto.assign = T)
		for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)	
		
		# extend GLD with Gold.PM - London Gold afternoon fixing prices
		data$GLD = extend.GLD(data$GLD)

	#*****************************************************************
	# Code Strategies
	#****************************************************************** 
	models = list()
		periodicity = 'years'
		dates = '2003::'
	
	models$classic = couch.potato.strategy(data, 'VIPSX,VTSMX', rep(1/2,2), periodicity, dates)
	models$margarita = couch.potato.strategy(data, 'VIPSX,VTSMX,VGTSX', rep(1/3,3), periodicity, dates)
	models$permanent = couch.potato.strategy(data, 'SPY,TLT,GLD,SHY', rep(1/4,4), periodicity, dates)
		
	#*****************************************************************
	# Create Report
	#****************************************************************** 
	plotbt.custom.report.part1(models)

The US Couch Potato portfolios also suffered huge draw-downs during 2008. The Permanent portfolio hold it ground much better.

It has been written quite a lot about Couch Potato strategy, but looking at different variations I cannot really see much difference in terms of perfromance or draw-downs. Probably that is why in the last few years, I have seen the creation of many new ETFs to address that in one way or another. For example, now we have tactical asset allocation ETFs, minimum volatility ETFs, income ETFs with covered calls overlays.

To view the complete source code for this example, please have a look at the bt.couch.potato.test() function in bt.test.r at github.

Some additional references from the Canadian Couch Potato blog that are worth reading:

• An Interview with the Original Couch Potato
• Scott Burns Interview: Part 2
• How Often Should You Rebalance?
• How to Lower Your Rebalancing Costs
• Does Rebalancing Boost Returns?