An Elegant Measure of Diversification: The Cluster Gini Score (CGS)
In the paper we wrote on The Minimum Correlation Algorithm we introduced the Composite Diversification Score (CDI). The purpose of this measure was to demonstrate how well a set of portfolio weights has minimized the average portfolio correlations and also balanced the risk contributions from each asset as measured using the Gini coefficient of inequality. The CDI can also be extended in a similar manner to CGS when applied to clusters- this is beyond the scope of this post.
An alternative and potentially more intuitive measure of portfolio diversification uses the concepts embedded in Cluster Risk Parity (CRP). The logic is very simple: if CRP using ERC (equal risk contributions) both within and across clusters represents the “optimally diversified portfolio”, then any portfolio that deviates from that is considered to be imbalanced from a risk/diversification standpoint to varying degrees. Essentially a portfolio from a given universe is considered to have “factor” exposures to each cluster, and also “tracking error” (risk of under-performing a benchmark) to those factors. In practice, we would like to have both a balance of risk across factors/clusters and a balance the risk attributed to having exposure to a factor/cluster. The Gini coefficient was used in the CDI to measure inequality in risk contribution exposure. A measure where (1-Gini) of asset risk contributions would mean that a value of 1 would represent perfect equality and 0 would be perfect inequality. The same concept can be extended to across (inter) factor/cluster risk contributions and within (intra) factor/cluster risk contributions. The formula for CGS would be as follows:
CGS= 100x (sqrt(NC)x(inter-cluster RC (1-Gini))+(avg intra-cluster RC (1-Gini)))/(sqrt(NC)+1)
Essentially this is a weighting of inter-factor risk balance versus the average intra-factor risk balance, where the inter-factor (across clusters) is assigned a greater weight as a function of the square root of the number of clusters available. The resulting equation shows a result between 0 and 100, with 100 being a Cluster Risk Parity allocation, and very low scores representing high concentration and poor distribution of risk. This score can be used to determine the degree of diversification/concentration of any set of portfolio weights across a given universe. In other words, if you have for example 5 assets and a set of weights that you believe to be optimal, the CGS can help to determine how imbalanced you are from a diversification standpoint. This can also be extended to optimization where CGS can help to moderate the objective function to ensure more stable results and generate portfolios that are more intuitive. In machine learning algorithms, and data-mining, CGS can help to produce more stable predictions and enhance the capability of handling large datasets when using clustering.