Constraining the maximum asset-portfolio correlation gave bigger returns and smaller volatility.
- exactly 20 names, weights between 1% and 10%
- exactly 20 names, maximum asset-portfolio correlation of 60%
- exactly 200 names, weights between 0.1% and 1%
- exactly 200 names, maximum asset-portfolio correlation of 60%
Here we see how those random portfolios performed during 2011.
Figures 1 and 2 show the distributions of the returns during the year. The correlation constraint added substantial return for the year.
2011 realized volatility
The low correlation portfolios are also — in general — low volatility. Perhaps that is the explanation of the high returns?
2011 information ratio
Adding the correlation constraint at the start of 2011 would have been quite useful. I doubt that it is a magic bullet though.
The commands to generate the random portfolios were given in “Portfolio diversity”.
The returns for the full year can be computed by giving
valuation a two-row price matrix that has the first and last prices for the period.
> require(PortfolioProbe) > divrp.20w.ret11 <- valuation(divrp.20w, + sp5.close11[c(1,253),], returns='simple')
Computing the volatilities is only slightly more complicated. We get the daily returns for the portfolios and then compute the standard deviation on the returns for each portfolio.
> divrp.20w.vol11 <- 100 * sqrt(252) * apply(valuation( + divrp.20w, sp5.close11, returns='simple'), 2, sd)