**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 R^{2} 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 R^{2} 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 R^{2} is around 0.001. I’ll show results for some additional low correlation portfolios in a later post.

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