ETF Model, momentum and correlations rules?

[yuvaltaylor]

" (Minimum Correlation Portfolio - Breaking Down Finance)
Is it possible to put the Viradis Correlation Algorithm to the test in this strategy in P123?

This method calculates weights for max return, use inverse of weights for min return. The objective of the minimum correlation algorithm is to choose the portfolio weights such that the assets are weighted proportionally to their average correlation with the other assets in the portfolio. That way, we obtain a portfolio where assets that have lower correlation to all the other assets in the portfolio get a higher weight.

In the above there is a spreadsheet link for three ETFs which calculates the optimum weights for a combo of the three.
P123 could incorporate this into the Book simulation module, or an expanded version for more ETFs or strategies.

Inputs are all available from P123:
1.Correlation matrix from Simulated Book.
2. Standard Deviations for each ETF or strategy from Risk Measurements .

I just tested this and it actually does produce the highest returns if the weights are calculated accordingly.

I understand how this works but it seems to be missing one essential component: the expected return of each ETF. As I see it, the three essential variables in portfolio weighting are expected return, correlation, and volatility. If the expected returns are all equal, then this formula, which takes into account the other two variables, should work well. But if they’re even slightly unequal, does correlation make a lot of difference? I’m still trying to figure out the answer to that. If you have any insights along those lines or recommended reading, I’d love to hear.