How to address concentration risk in portfolio construction? Today we will explore different ways to design an index that tilts so heavily to the largest constituents while keeping an eye on tradability and risk management.
Historically, index providers have used fixed weight capping to mitigate large concentration in one or more securities, where the extra weight is redistributed to smaller non capped constituents. Recall from our first blog in the series the weights as follows:
Market capitalization:
Source: CF Benchmarks, as of March 1, 2022
By applying a fixed cap of 25% to these weights, bitcoin and ether would exceed the cap at inception, with no difference in weighting based on market capitalization or liquidity. Different capping weights would result in similar results and lead to the index trading more like a fixed weight index than a market capitalization weighted index.
The point on liquidity is probably the most impactful for a small burgeoning asset category like cryptocurrency. For an index to be representative of the market it needs to be tradable. Smaller constituents generally have a significantly lower liquidity profile, thus making it difficult for institutional managers to replicate the market beta when the smaller constituents receive a higher weighting that results from capping the very largest constituents. Therefore an index that significantly re-weights to much smaller and illiquid names creates a benchmark that may not be representative or tradable.
So how to solve this problem? Instead of applying a fixed cap, the index will benefit from a smoothed capitalization methodology where each additional percentage weight is discounted sequentially more than the previous one by a defined amount. This progressive redistribution from large to small caps benefits from keeping the final index weights relatively close to free float market capitalization while still achieving diversification.
In order to achieve the smooth capitalization redistribution, the discounting will increase exponentially using the inverse step function applied to each additional 4% bucket. The inverse stepped function basically reduces each tranche by 1/ nth tranche. To illustrate, if we were to apply the inverse step function with a 4% tranche to the 60.31% market capitalization weight to bitcoin, you would have a process that looks like the following chart and we have a new bitcoin index weight of 37.11%.
Bitcoin diversification step example:
Source: CF Benchmarks, as of March 1, 2022
By applying this process to the whole index you achieve a redistribution that looks like the following.
Diversified weights:
Source: CF Benchmarks, as of March 1, 2022
The smoothed capitalization redistribution allows investors to achieve the primary goals of a market capitalization weighted index, to be representative of the market, to reduce concentration to the largest weights and to remain tradeable.
Our blog series continues tomorrow with a discussion on how the use of index inclusion and exclusion screens can be used to help reduce risk.
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