A Multifaceted Approach To Smart Beta

February 10, 2015

 

Furthermore, market conditions such as bullish or bearish markets may have a substantial impact on how different portfolio strategies perform. Amenc et al. [2012b] show considerable variation in the performance of some popular smart-beta strategies in different sub-periods, revealing the pitfalls of aggregate performance analysis based on long periods. Separating bull and bear market periods to evaluate performance has been proposed by various authors such as Levy [1974], Turner, Starz and Nelson [1989] and Faber [2007]. Ferson and Qian [2004] note that an unconditional evaluation made, for example, during bearish markets, will not be a meaningful estimation of forward performance if the next period were to be bullish. We thus divide the 40-year period into two regimes: quarters with positive return for the broad CW index comprise bull market periods, and the rest constitute bear markets. Figure 5 shows that the performance of multi-strategy factor indexes depends on market conditions. For example, the U.S. Long-Term Mid Cap Multi-strategy Index posts much higher outperformance in bull markets (+5.37 percent) than in bear markets (+3.02 percent). The opposite is true for the U.S. Long-Term Low Volatility Multi-strategy Index, which underperforms by 0.81 percent in bull markets and outperforms by 7.33 percent in bear markets. If one combines the individual factor tilts, the dependency on the market regime is reduced for the multi-beta allocations compared to the constituent indexes. Indeed, in terms of information ratio, the performance of the multi-beta allocations is roughly the same between bull and bear markets. In terms of returns, both the EW and ERC multi-beta allocations remain defensive diversification strategies, as they outperform by a larger amount in bear regimes than in bull markets. In the end, the multi-beta allocations on the smart-factor indexes allow the premia from multiple sources to be harvested while producing more effective diversification, as they achieve a smoother outperformance across the economic cycles and bull/bear market regimes.

Figure 7
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Aimplementation Benefits Of Allocating Across Factors
The multi-beta indexes analysed above were designed not only to provide efficient management of risk and return but also for genuine investability. Each of the smart-factor indexes has a target of 30 percent annual one-way turnover, which is set through optimal control of rebalancing (with the notable exception of the momentum tilt, which has a minimal target of 60 percent turnover). In addition, the stock selections used to tilt the indexes implement buffer rules in order to reduce unproductive turnover due to small changes in stock characteristics. The component indexes also apply weight and trading constraints relative to market-cap weights so as to ensure high capacity. Finally, these indexes offer an optional high-liquidity feature, which allows investors to reduce the application of the smart-factor index methodology to the most liquid stocks in the reference universe. Amenc et al. [2014a] present a more detailed explanation on how including carefully designed rules at different stages of the index-design process eases implementation of investments in smart-beta indexes.

In addition to these implementation rules, which are applied at the level of each smart-factor index, the multi-beta allocations provide a reduction in turnover (and hence of transaction costs) compared to a separate investment in each of the smart-factor indexes. This reduction in turnover arises from different sources. First, when the renewal of the underlying stock selections takes place, it can happen that a stock being dropped from the universe of one smart-factor index is being simultaneously added to the universe of another smart-factor index. Second, for constituents that are common to several smart-factor indexes, the trades to rebalance the weight of a stock in the different indexes to the respective target weight may partly offset each other.

 

 

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