Multi-Smart Beta Allocation: Toward A New Source Of Value Added In Investment Management
While in practice, investors may select among various ways of combining smart-factor indexes in order to account for their investment beliefs, objectives and constraints, the cases of an equal-weighted allocation, and a (relative) equal-risk contribution allocation to four smart-factor indexes seeking exposure to the main consensual factors (notably value, momentum, low volatility and size) provide evidence that the benefits of multifactor allocations are sizable. In particular, exposure to various factors whose premia behave differently over time and across market conditions provides for smoother outperformance. Moreover, natural crossing benefits reduce turnover of multifactor mandates relative to separate single-factor mandates. Investors and asset managers may thus be well advised to further explore the potential of multifactor allocations in a variety of investment contexts.
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- Diversified multistrategy weighting is an equal-weighted combination of the following five weighting schemes: maximum deconcentration, diversified risk weighted, maximum decorrelation, efficient minimum volatility and efficient maximum Sharpe ratio (see Gonzalez and Thabault ).
- To make a popular analogy, one can think of the diversified multistrategy approach as exploiting an effect similar to Surowiecki 's wisdom-of-crowds effect by taking into account the "collective opinion" of a group of strategies rather than relying on a single strategy.
- Maillard et al.  discuss a weighting scheme that equalises each asset's contribution to absolute risk, i.e., portfolio volatility. It is straightforward to extend their approach by applying it to relative returns with respect to a cap-weighted reference index. In this case, the objective is to equalise the contribution of each constituent to the overall relative risk (tracking error) with respect to the chosen reference index.
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- The days to trade (DTT) measure is computed for all stocks at each rebalancing in the last 10 years (40 quarters). Based on the estimated DTT for all constituents of a given index, we can derive an estimate of the required DTT for the index itself, by using, for example, extreme quantiles of the DTT distribution over time and constituents, such as the 95th percentile that we report.
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