Optimizing Ivy Portfolio

Early on I posted a simple live version of GTAA strategy. It demonstrated the effectiveness of the Ivy Portfolio (M.Faber, 2009) rationale in recent market with a small sample. Again, the rationale is very simple and powerful: screen a wide range of asset classes each week/month, then invest in those that have shown the strongest momentum. Last time I tracked 39 ETFs’ 9-month SMA and equally allocated portfolio assets to the top 8. Although I got pretty good results, the sample was relatively small and those ETFs are quite different in terms of time of inception, liquidity, tracking error, etc.

And above all, equal allocation seems a bit, for lack of a better word, boring. This time I want to use a more general sample to see how we can improve this by implementing some optimization strategies I’ve shown in my previous post Backtesting Portfolio Optimization Strategies.

Some equipment check before we launch the test.

Asset Classes:
1. SPX Index: S&P 500 LargeCap Index
2. MID Index: S&P 400 MidCap Index
3. SML Index: S&P 600 SmallCap Index
4. MXEA Index: MSCI EAFE Index
5. MXEF Index: MSCI Emerging Markets Index
6. LBUSTRUU Index: Barclays US Agg Total Return Value Unhedged USD (U.S. investment grade bond)
7. XAU Curncy: Gold/USD Spot
8. SPGSCI Index: Goldman Sachs Commodity Index
9. DJUSRE Index: Dow Jones U.S. Real Estate Index
10. GBP Curncy: GBP/USD Spot
11. EUR Curncy: EUR/USD Spot
12. JPY Curncy: JPY/USD Spot
13. HKD Curncy: HKD/USD Spot

1. Rebalance monthly
2. Rank 12-month SMA; invest in the top 3
3. For each asset, minimum weight = 5%; maximum weight = 95%
4. Use CVaR optimization to construct the portfolio each month; confidence level = 1%

Fortunately, our test didn’t fall apart and crash into the Pacific Ocean. The CVaR model seems did a good job improving the original strategy. However, it has to be pointed out that not all optimization models are better than an equal-weighted one. As demonstrated below, the minimum-variance and maximum-sharpe ratio models didn’t make much difference.



Backtesting Portfolio Optimization Strategies

Recently I’m trying to develop some handy tools to help backtest and analyse user-specified portfolio strategies. Now I’d like to do a quick demonstration through testing four portfolio strategies (minimum variance, maximum Sharpe Ratio, minimum CVaR, and equal-weighted) that rebalance weekly from 1990 to 2012.

Instead of specific stocks or ETFs, 10 S&P sector indices by GICS will be used as hypothetical assets. As aggregated equity trackers, they are similar in terms of market efficiency, liquidity, macro environment etc. And by using them we eliminated factors that jeopardize the quality of data such as IPO or survivorship bias. Moreover, most ETFs only emerged several years ago, with equity indices one can go back much further for a bigger sample.

Firstly, I got the data from Bloomberg and load it from a csv. file. Please note in order to transfer the raw data into a time-series object (zoo), we need to convert the dates in the csv. file into integers.

### load packages

raw_data <- read.csv(file.choose(), header = TRUE) ## open the file directly
raw_data[, 1] <- as.Date(raw_data[, 1], origin = '1899-12-30') ## convert numbers into dates
raw_data <- zoo(raw_data[, -1], order.by = raw_data[, 1]) ## create a zoo object

With the data properly formatted, we can perform backtests based on our strategies.

minvar_test <- backtest(raw_data, period = 'weeks', hist = 52, secs = 10, model = 'minvar',
	reslow = .1 ^ 10, reshigh = 1)
sharpe_test <- backtest(raw_data, period = 'weeks', hist = 52, secs = 10, model = 'sharpe')
	cvar_test <- backtest(raw_data, period = 'weeks', hist = 52, secs = 10, model = 'cvar', alpha = .01)
equal_test <- backtest(raw_data, period = 'weeks', hist = 52, secs = 10, model = 'eql')

I’m not posting function backtest here because it’s quite bulky and has other optimization functions nested inside. But as you can see what it does is just take a zoo object, ask what strategy the user wants to test, and perform a backtest accordingly. By setting argument daily.track = TRUE, You can also track the portfolio’s position shift on daily basis. But due to time and space constrain I won’t show it this time neither.

Here’s the PnL curves of the tests.

And to see their position transitions, we need another function.

## returns a transitional map of a backtest strategy
transition <- function(allo, main = NA) {
	cols = rainbow(ncol(allo))
	x <- rep(1, nrow(allo))

	plot(x, col = 'white', main = main, ylab = 'weight', ylim = c(0, 1),
	xlim = c(-nrow(allo) * .2, nrow(allo)))

	polygon(c(0, 0, 1:nrow(allo), nrow(allo)), c(0, 1, x, 0),
	col = cols[1], border = FALSE)

	for (i in 2:ncol(allo)) {
		polygon(c(0, 0, 1:nrow(allo), nrow(allo)), c(0, 1 - sum(allo[1, 1:i]),
		x - apply(allo[, 1:i], 1, sum), 0), col = cols[i], border = FALSE)

	legend('topleft', colnames(allo), col = cols[1:ncol(allo)], pch = 15,
	text.col = cols[1:ncol(allo)], cex = 0.7, bty = 'n')

## visualize the transitions
par(mfrow = c(4, 1))
transition(cvar_allo, main = 'CVaR Portfolio Transition')
transition(minvar_allo, main = 'Min-Variance Portfolio Transition')
transition(sharpe_allo, main = 'Sharpe Portfolio Transition')
transition(equal_allo, main = 'Equally Weighted Portfolio Transition')

Scalability was taken into consideration when these functions were built. I look forward to nesting more strategies into the existing functions.