Part two in a series
A few months ago, I presented a novel method for balancing a widely diversified portfolio. By “widely diversified,” I mean all asset classes, foreign and domestic. This is to reduce inter item correlation. Here are the balancing rules:
- Look at last month’s return for each of the nine EFTs
- Drop the bottom two of ten (including cash)
- Add a constant to the remaining eight, so that the lowest score is set to 0.0%
- Allocate funds to these issues, pro rata by their adjusted score
- Repeat monthly
These rules produced pretty good results in backtesting over a ten year period. The method, and the portfolio, are described in the earlier post.
You are probably thinking that last month’s return is a poor predictor for next month, and that’s certainly true for individual stocks. Here, though, we have a diverse group for which relative rank is predictive. For each month, I ranked the ten issues (including cash) from 1 to 10, with 10 being the lowest return. The table below shows the following month’s return for each rank.
For this group, rank does indeed predict next month’s return, with an R2 of 0.35. I also tested the hypothesis that the top five’s average trailing return of 0.7% is greater than the bottom five’s 0.2%, and it is (with 96% confidence). You can check this yourself by downloading monthly data series for the given ETFs.
Rank based on a three month lookback is an even stronger predictor, with an R2 of 0.81 (chart below). Running model #3 with the longer lookback increases its CAGR to 9.9% and its Sharpe to 0.76.
I keep saying “for this group,” because the method depends on low inter-item correlation. The theory is that the portfolio will cover the gamut of asset classes, with institutional flows to the leading classes persisting for several months. I selected the portfolio based on this theory, which is quantified by an average inter-item correlation of 35%.
It’s not really a rotation model. If you are rotating, say, sector funds, then you are still concentrated in U.S. equities.
The nine sector funds, shown above, are a strongly correlated group with no negatively correlated pairs. The minimum correlation coefficient is 20% (between XLK and XLU) and the inter-item mean is 55%. For this group, rank has no predictive value. The R2 is around 0.001. I’ll show results for some additional low correlation portfolios in a later post.