Analysis of Funds – A Systemic Approach



Funds are composed of ensembles of securities. A schematic of a Complexity Map of a system of funds is indicated below.


The map reveals that, for example, fund 10 is the hub of the system and of the 8 securities composing it, number 6 and 8 have the biggest footprint in terms of its complexity and resilience.

Analysis of fund performance data (weekly, monthly, quarterly, etc.) allows us to quantify the following new KPIs:

•    Complexity – a measure of how intricate the system of funds is. In general, high complexity may lead to low performance, low efficiency low governability and a reduced degree of performance predictability.

•    Critical complexity – maximum complexity a fund (system) can attain without becoming chaotic.

•    Entropy – a measure of disorder. High-entropy systems are difficult to comprehend and govern. In such cases it is difficult to formulate realistic performance goals.

•    Resilience – a measure of the capacity to resist shocks and destabilizing events present in the markets and in the global economy (bubbles, crises, contagion, natural disasters, scandals, etc.).

•    Complexity Maps – a graphical representation of the structure of the data which reflects the functioning of the system of funds.


An additional and crucial piece of information we provide is the so-called intrinsic volatility, which differs from classical volatility measures in that it takes into account the effective structure of each fund and, most importantly, it does so based on a new measure of correlation, the generalized correlation, see our earlier blog. Intrinsic volatility does not depend on weights and differs significantly from conventional volatility in periods of pronounced market turbulence.


An interesting way to analyze the complexity and resilience of funds is to combine the corresponding performance data with any external factors which are relevant to the said funds, such as commodity prices, exchange rates, unemployment, interest rates, etc.

The combination of the above factors provides the basis of a holistic analysis, in which exogenous and endogenous business KPIs are integrated. The objective is to provide broader insight into the structure of a business and a more relevant reflection of its resilience and potential criticalities.

A system of 22 funds has been analyzed. The funds are based on commodities (coal, oil, tin, lead, corn, sugar, rubber, etc.). The funds have been analyzed as an ‘isolated’ system and also in conjunction with the monthly costs of the various commodities as well as exchange rates of certain currencies.

It is interesting to note how the resilience of the system changes once external factors are accounted for:

Resilience based on fund performance data only:           71%

Resilience of combined system:                                          61%

This confirms our experience – a restricted view of a business provides, in general, a more optimistic value of resilience with respect to a holistic analysis.In this case, we’re talking of a full 10% less.

The funds Complexity Map, depicted below, indicates how it is the external factors that have the highest footprint in term of complexity and resilience and how fund 14 – a hub of the system of funds – would be the one to potentially offer the most intense reaction in case of trouble.




Complexity profiling of the system of funds adds additional information: over 60% of the dynamics, complexity and resilience of the system of funds is governed by external factors, the remainder by the Asset Management company.




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