Minimum correlation algorithm

L’idée en termes simples

Instead of solving an optimisation, rank the tickers by how unusual their correlations are: the more a ticker zigs when others zag, the bigger its share. Then divide by volatility so that calm tickers are not drowned out. It reaches much of the diversification of the optimisers with no fragile mathematics.

Mode de calcul

1) Standardise each pairwise correlation against the average of all of them and turn it into a score with the normal distribution (low correlation → high score). 2) Rank the tickers by their average score; the ranks are starting weights. 3) Spread those weights through the score matrix, normalise. 4) Divide by each ticker's volatility and normalise again.

aᵢⱼ = 1 − Φ((ρᵢⱼ − ρ̄) ÷ s_ρ)
w₀ ∝ rank(mean_j aᵢⱼ)
w₁ ∝ w₀ᵀA
wᵢ ∝ w₁ᵢ ÷ σᵢ

Comment l’utiliser

• As a quick, stable diversifier for many tickers.
• Compare it with ERC and maximum diversification: when all three agree, the result is robust.

Tirer des conclusions

• The ticker with the lowest average correlation gets the top rank.
• Like risk parity it is long only and keeps every ticker.

Points de vigilance

• A heuristic: it has no single objective you can point to.
• Rank-based weights change in steps when two tickers swap places.

Un peu d’histoire

David Varadi, Michael Kapler and Corey Rittel, "The Minimum Correlation Algorithm: A Practical Diversification Tool" (2012), written for practitioners who found optimisers too unstable.