Waiting For Their Big Break

April 23, 2012

Waiting For Their Big Break

The road to long-term viability for alternative ETF (Alt-E) sponsors and for the successful implementation of Alt-Es in portfolios will require many adaptations for our industry. Interesting alternative strategies are being created, many with solid theoretical bases, strong long-term value potential and an ease of use that only a few years ago would have seemed impossible. Unfortunately, the short track records of the existing pool of Alt-Es, the poor returns many had in 2011 and the lack of assets to create marketing scale make for a long road ahead. For investors with the sophistication, patience and ability to find creative uses for Alt-Es, many existing funds offer an interesting array of options. Undoubtedly, more will come to market in the near future. However, to date—and it's early in the development cycle of Alt-Es—these funds have seen weak asset flows amid limited uptake in both the retail and institutional investment communities. I believe the lack of commercial success stems from the complexity of Alt-Es.

Alt-Es have a radically different foundation than traditional ETFs. Rather than mirroring the returns of a traditional index, alternative ETF prices, returns and volatilities are derived from an algorithm or a specific rules-based decision process. Specifically, the ETF price moves based on how the rules (or model) interact with the self-defined investment universe. By definition, an Alt-E will not perform as a vanilla index-based strategy, where the underlying index can be easily measured, understood and monitored. Rather, the decision engine provides the critical inputs for returns and so requires the practitioner to have a deeper understanding of how positions are selected.

These nonindex, rules-based decision processes of Alt-Es have clear and important implications. Generally, we can expect the understanding and sophistication of the buyer to be significantly greater than that of someone buying a simple index ETF. Alt-Es are not made for grandma, at least not unless she has a master's degree from MIT. These are professional-grade tools and will mostly be used by professional investors.

Additionally, creative and thoughtful portfolio construction using Alt-Es will be critical to their successful growth. Potentially complex interactions with other portfolio holdings, model unpredictability and shifting correlations with other portfolio assets heighten the risks of using Alt-Es. Finally, costs, given the added complexity, lack of scale (currently) and additional required education, will be higher than those of simple index-based ETFs.

The first challenge for the Alt-E marketplace is identifying what is and what is not "alternative" and building a categorization system to better compare differing types of Alt-Es. IndexUniverse has broken out a still-small group of 27 ETFs as "alternative." One can go a step further and break down those Alt-Es into four broad categories:

  • Hedge Fund Replication Strategies: These strategies use advanced statistical techniques to attempt to mimic the returns (not the holdings) of a cross section of the hedge fund universe. Much academic ink has been spilled in an effort to gauge the efficacy of statistically replicating hedge fund returns. Generally, these funds work effectively during times of low stress and normalcy in the market. However, they can vary widely from their target (replicating hedge funds) during periods of abnormal market stress and returns. A few HF replication ETFs include the ProShares Hedge Replication (NYSE Arca: HDG), the IndexIQ Hedge Multi-Strategy Tracker (NYSE Arca: QAI) and the iShares Diversified Alternatives Trust (NYSE Arca: ALT). Each fund relies on its own set of algorithms to target the returns of a specific slice of the hedge fund universe.
  • Long/Short Funds: Using predetermined rules, these funds harvest what they believe to be long-term inefficiencies (generally using fundamental factors) by having offsetting long and short positions with differing characteristics. For example, QuantShares has rolled out a number of factor-based ETFs that attempt to profit from tactical as well as strategic factor tilts. Its U.S. Market Neutral Value Fund (NYSE Arca: CHEP) owns shares of the cheapest companies while shorting those companies the model determines are the most expensive.

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