Discrete hierarchy of sizes and performances in the exchange-traded fund universe

Using detailed statistical analyses of the size distribution of a universe of equity exchangetraded funds (ETFs), we discover a discrete hierarchy of sizes, which imprints a log-periodic structure on the probability distribution of ETF sizes that dominates the details of the asymptotic tail. This allows us to propose a classification of the studied universe of ETFs into seven size layers approximately organized according to a multiplicative ratio of 3.5 in their total market capitalization. Introducing a similarity metric generalizing the Herfindhal index, we find that the largest ETFs exhibit a significantly stronger intra-layer and interlayer similarity compared with the smaller ETFs. Comparing the performance across the seven discerned ETF size layers, we find an inverse size effect, namely large ETFs perform significantly better than the small ones both in 2014 and 2015.

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