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Rafael Solis

California State University, USA



Exchange-Traded Funds (ETFs) have become widely held financial instruments among investors. Their popularity has produced an explosion in the number of different ETFs currently available in most financial markets. This revolution started in the early 90s with a single product which consisted of a basket of single securities that where part of (and therefore closely follow) the S&P 500 index (SPDR). The idea was to have a financial instrument that was less volatile than a single stock and yet could be traded as one thus avoiding the shortcomings of traditional mutual funds such as management fees, liquidity and tax disadvantages. The idea worked and currently there are nearly 1000 of such instruments. Our research empirically analyzes the idea that there is no need for so many of these products and therefore an investor could be better off by picking among a handful of them with each of them belonging to a statistically different cluster of such funds. To investigate this idea we randomly sampled 574 Exchange-Traded Funds (ETFs) and analyzed them using three multivariate methods: Cluster Analysis, Factor Analysis and Chernoff-Faces. For each fund, we recorded performance measures that included: Intraday, YTD, 3-Month, 1-Year and 3-Year returns. Utilizing a k-means clustering algorithm we obtained 5 clusters of the ETFs which produced (statistically) similarly behaving funds within each cluster and dissimilar ones between clusters. Factor Analysis and Chernoff-Faces were used to graphically depict the similarities of the ETFs belonging to each cluster. The analyses also showed surprising similarities for many ETFs that are supposed to follow different stock indexes, thus questioning the value of diversification for investors trusting this strategy. On the other hand, the results of this study could provide investors opportunity to further diversify their holdings by choosing funds belonging to different clusters.

Keywords: Financial Markets, Exchange-Traded Funds, ETFs, Diversification, Multivariate Analysis, Cluster Analysis, Factor Analysis, Factor Loadings

JEL Codes: C1, C19, C4, C49, G1, G24, O16, R42