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Contribution Details

Type Master's Thesis
Scope Discipline-based scholarship
Title Analysis of Smart Beta ETFs
Organization Unit
  • Christoph Buxtorf
  • Thorsten Hens
  • Nilufer Caliskan
  • English
Institution University of Zurich
Faculty Faculty of Economics, Business Administration and Information Technology
Number of Pages 177
Date January 2016
Abstract Text “Smart Beta” has become a buzzword for an emerging type of investment which constitutes an evolution of traditional market-cap-weighted index funds by combining active and passive elements of asset management but otherwise lacks a standard industry-definition so far. This concept seems to be marketed as a revolutionary investment method, though its cornerstones are known to academics and used by mainly institutional investors for decades. Moreover, a number of prominent scholars take issue with some of its mathematical foundations. Hence, the current hype about Smart Beta seems exaggerated and gives rise to the questions why an increasing number of investors are buying products (mostly ETFs) that track alternatively-weighted indexes and if their immense growth is likely to continue. In order to shed light on this subject, this thesis aims to compare and disentangle opposing Smart Beta definitions, to show the growth of alternatively-weighted ETFs, to investigate potential benefits and risks of Alternative Indexing (AI), and to empirically test if the popularity of Smart Beta may be attributed to superior performance figures or utility. In particular, the thesis contains four parts which analyze (1) how ETFs/AI evolved, how different Smart Beta definitions diverge, what Smart Beta ETP strategies exist, which strategies have most total net assets (TNA) and highest growth rates, and who drives their demand, (2) how Smart Beta investing may bring benefits or pose risks as compared to active mutual funds or traditional ETFs and (3) if Smart Beta ETF indexes exhibit superior risk and return characteristics or (4) higher Cumulative Prospect Theory (CPT) values or CRRA/Mean-Variance utility than the IWV and SPY ETF, which track the Russell 3000 and the S&P 500 index. Finally, a comparative statics analysis documents the required levels of loss/risk aversion and probability weighting so that investors with different utility functions prefer various Smart Beta indexes over the IWV/SPY ETF, a riskfree asset (defined as the US monthly treasury bill) and each other in head-to-head comparisons. In this way, this work expands the discussion of academics and industry representatives whether AI is more efficient than cap-weighting by additionally evaluating its utility and employing an actual data set which consists of ETFs that can be directly invested in and account for the total cost of owning a Smart Beta index (i.e., TER, rebalancing costs, etc.).
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