A Multifaceted Approach To Smart Beta

February 10, 2015


Figure 6 displays statistics relative to the investability of the multi-beta equal-weight and relative ERC allocations along with the average of the midcap, momentum, low-volatility and value smart-factor indexes. For comparison, we also show the same analytics for their highly liquid counterparts. We see that the turnover of multi-beta indexes is very reasonable. In fact, managing a mandate on each smart-factor index separately would yield a turnover that is higher than the average turnover across the smart-factor indexes. This is due to the fact that rebalancing each component index to the allocation target would induce extra turnover. However, implementing the multi-beta index in a single mandate exploits the benefits of natural crossing arising across the different component indexes and actually reduces the turnover below the average level observed for component indexes. We provide in the table for each multi-beta allocation the amount of turnover that is internally crossed in multi-beta indexes as compared to managing the same allocations separately. We see that about 6 percent turnover is internally crossed by the EW allocation and that the ERC allocation that tends to generate more turnover also exploits natural crossing effects more than the EW allocation (around 7.8 percent is crossed internally). These cancelling trades result in an average one-way annual turnover that can be even lower than for the EW allocation, as is the case in the developed universe.

In addition to turnover, Figure 6 shows the average capacity of the indexes in terms of the weighted average market cap of stocks in the portfolio. This index-capacity measure indicates very decent levels, with an average market cap of around $10 billion for the multi-beta index, while the highly liquid version further increases capacity to levels exceeding $15 billion in the case of the U.S. long-term track records. In the case of the developed universe, the weighted average market caps are higher, as the period under scrutiny is more recent (last 10 years)—around $16.3 billion for the standard indexes and $23 billion for the highly liquid ones. In both regions, we provide an estimate of the time that would be necessary to set up an initial investment (i.e., full weights) of $1 billion AUM in the indexes, assuming the average daily dollar traded volume can be traded (100 percent participation rate) and that the number of days required grows linearly with the fund size.6 Overall, this does highlight the ease of implementation of the multi-beta indexes and the effectiveness of the highly liquid option. Indeed, the DTT required for the initial investment on U.S. indexes are very manageable (about 0.12 days for the standard multi-beta indexes, and 0.07 days with the highly liquid feature). Even in the developed universe, the highly liquid multi-beta indexes would require about 0.09 days of trading. In addition, one should keep in mind that the number of days needed to rebalance the indexes (i.e., trade the weight change rather than the full weight on each stock) would be much lower. Even though the excess return is reduced by a few basis points, which can be explained by a potential illiquidity premium, it should be noted that the highly liquid multi-beta indexes do maintain the level of relative risk-adjusted performance (information ratio) of the standard multi-beta indexes in the U.S. case, and it provides even stronger information ratios in the developed universe. Finally, even when assuming unrealistically high levels of transaction costs, all the smart-factor indexes deliver strong outperformance (from 2 to 3.69 percent) net of costs in both regions. Compared with the average stand-alone investment in a smart-factor index, the multi-beta indexes almost always result in higher average returns net of costs due to the turnover reduction through natural crossing effects across its component smart-factor indexes.

Figure 6
For a larger view, please click on the image above.



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