ITA Wealth Management

If you are building a portfolio from scratch, are you better served using Vanguard or Barclay (iShares) ETFs?  That is the question I posed to myself now that I have access to the Quantext Portfolio Planner. What I did was to populate a portfolio using the “Big Six” ETFs plus developed international, emerging markets, and REITs.  For Barclay or iShares I used the following ETFs.

Barclay

• IVE
• IJJ
• IJS
• IVW
• IJK
• IJT
• EFA
• EEM
• ICF

I invested 11% in each asset class with exception of international.  For that asset class, I invested 12%.  The time of examination was from 10/8/03 through 10/8/2008 or a five-year period.  Here are the results.

• Return = 10.90%
• Standard Deviation = 20.18%
• Diversification Metric = 12%  (See definition below)
• Portfolio Autocorrelation = 26.51%  (See definition below)

Vanguard

From the Vanguard ETF garden I plucked identical asset classes.  These are the ETFs I selected.

• VTV
• VOE
• VBR
• VUG
• VOT
• VBK
• VEU
• VWO
• VNQ

Again, I invested 11% in each asset class with exception of VEU, the international asset class.  I invested 12% in VEU just as I invested 12% in the international iShare, EFA.  Here are the Vanguard results.

• Return = 12.32%
• Standard Deviation = 9.11%
• Diversification Metric = 61%
• Portfolio Autocorrelation = 7.91%

It is obvious Vanguard is the better choice based on this analysis.  The return is greater with lower SD.  The Diversification Metric is much higher and the Portfolio Autocorrelation is closer to zero.

What is portfolio autocorrelation?

QRP and QPP both calculate an historical statistic called portfolio autocorrelation.  This is a recent feature and is not yet included in the user manual / textbook.  Portfolio autocorrelation is the correlation in portfolio returns from one month to the next.  If it is positive then high returns tend to be followed by high returns and vice versa.  If portfolio autocorrelation is negative, then the portfolio returns tend to be ‘mean reverting’ which means that very high return months tend to be followed by returns closer to the mean–the portfolio tends to damp out periods of very high or very low returns.  Portfolio theory generally assumes that autocorrelation is zero–the random walk.  QPP and QRP model the market as though autocorrelation is zero, and the metric shown is for historical performance.  If you have a portfolio that shows a lot of positive autocorrelation, this is a flag–this means that big swings get amplified.  These effects are widely debated, but there is evidence that they can be meaningful:

What does the Diversification Metric (DM) mean?

In our latest release of the software, we have added a new analytical function that accounts for non-market correlation between portfolio components.  This is important because many asset classes have correlation to one another beyond what can be captured by Beta.  This is a major challenge for many portfolios, but especially those with concentrations in a sector.  This problem is described in a recent article. (Go to QPP site to find active link.) Our software generates a statistic that measures how effectively the non-market component of returns actually diversify one another.  In the best possible case, the non-market component of returns would be totally uncorrelated with one another.  In the worst case, they would be highly correlated.  The diversification metric (DM) measures how un-correlated the non-market returns are across the portfolio.  Higher values of DM mean that the non-market component of returns shows low correlation across the portfolio.  Higher DM means that your are getting more real diversification out of your portfolio.