We also estimate the costs associated with implementing a range of smart beta strategies; the appendix summarizes the smart beta methodologies we use. These methodologies are intended to simulate the more popular strategies in the marketplace today. We design the methodologies to provide consistent starting universes, regional definitions, and rebalance dates. For calculating costs, we assume AUM equal to $10 billion for US and international strategies, and AUM equal to $1 billion for emerging-market strategies.
Our findings are striking. We determine, for example, that over the period we observe, momentum strategies with as little as $10 billion in aggregate assets have trading costs of 200 bps or more. At the same level of AUM, income strategies’ costs are in the 60–80 bps range, and quality strategies’ costs fall below 40 bps.
The comparisons are even more meaningful if we understand the factors that drive different strategies’ very different costs. Applying Aked and Moroz’s market impact cost model allows us to attribute implementation costs to the smart beta strategies’ key trade-related characteristics, including portfolio volume, tilt, turnover, turnover concentration, and number of trading days. We define and describe these terms as follows:
1. Portfolio volume is the aggregation of median daily trade volume, in dollars, of all the stocks in the portfolio. A strategy’s cost is inversely proportional to its portfolio volume; all else equal, a small-cap portfolio would cost twice as much to implement as a large-cap portfolio if it has half the latter’s aggregate volume.
2. Tilt, in this context, is the degree to which the portfolio holding weights deviate from a volume-weighted portfolio, which is the most liquid combination of a given set of stocks. The volume-weighted portfolio has a tilt of 1. Holding all else equal, a portfolio with a tilt of 2 would experience twice-as-high market impact costs.
3. Annual one-way turnover is another determinant of cost; in general, a strategy that requires a higher rate of trading incurs higher market impact costs.
4. Turnover concentration reflects the degree to which trades are spread across the portfolio. Consider, for example, two rebalances with the same turnover rate. One requires buying $100 million of one large-cap company’s shares, while the other requires buying $10 million worth of the shares of 10 large-cap companies. Highly concentrated trades, such as the former, are more costly to execute. Additionally, strategies that rebalance more frequently (e.g., quarterly versus annually) will tend to have lower turnover concentration.
5. Number of trading days is also an important factor in cost, but note that it describes execution rather than being a characteristic of a smart beta strategy. An implementer less concerned with tracking error can effectively lower the cost by spreading a single rebalance through multiple days of market liquidity.
Solid cost estimates can additionally shed light on relative capacities. Procedurally, we set a fixed cost for all strategies at 50 bps a year and compute the corresponding AUM, effectively defining capacity as the largest amount of assets a strategy can hold without incurring more than 50 bps of market impact cost a year. This approach gives us an even basis for comparing the capacities of different smart beta strategies.
The market impact costs of the simulated US smart beta strategies we study range from a high of 272 bps for a standard momentum strategy to a low of 2 bps for the Fundamental Index™ strategy. Inversely, the capacities of these strategies, at a uniform 50 bp cost level, run from a low of $2 billion for a standard momentum strategy to a high of $291 billion for a Fundamental Index strategy.
The portfolio statistics are telling. For instance, the Sharpe momentum and standard momentum strategies have high turnover rates (108.5% and 155.8%, respectively), high turnover concentrations (88.4% and 90.2%, respectively), and low portfolio volumes ($35.7 billion and $37.9 billion, respectively) relative to the other smart beta strategies. Collectively, these characteristics imply that these two strategies have concentrated or illiquid holdings, completely trade out of and into a few positions, and do so at a fast pace. All of these traits contribute to the strategies’ high cost of implementation. In contrast, Sharpe momentum and standard momentum have the lowest tilt, at 1.3, which suggests their weighting by market capitalization (or a variant) mitigates some of the trading challenges. Overall, momentum may not be a good choice as a stand-alone smart beta strategy, assuming implementers apply passive execution.
The high dividend and dividend growth strategies also have fairly high costs. Their turnover rates are much lower than those of the momentum strategies because they both employ stringent banding rules. The main causes of their high costs are their low portfolio volumes of $13.4 billion and $26.0 billion, respectively, and high tilts of 9.3 and 4.5, respectively, likely the result of investing in a small number of the highest-yielding companies and weighting their positions on the basis of yield. Investors who seek a steady stream of healthy dividends pay a hidden price in the form of market impact costs.
The Fundamental Index is a broad market index, as indicated by its very high portfolio volume of $96.6 billion. Its rebalancing primarily consists of restoring existing constituents to their fundamental weights. Accordingly, both its turnover rate (11.4%) and turnover concentration (21.9%) are the lowest among the smart betas strategies in our sample. Its tilt is also low, on a par with cap-weighted strategies, suggesting that fundamental size is highly correlated with trading volume. In contrast, the concentrated value strategy has significantly lower capacity. A strong and straight bet on a target factor may not necessarily lead to a higher return, and almost certainly creates higher implementation costs as well as more risk.
Although the strategies in the low volatility group have distinctive methodologies and characteristics, they all achieve their primary investment objective—respectable returns with lower risk. They have strikingly different market impact costs, however, ranging from 1.9% for the basic low volatility strategy (almost as high as momentum) to 7 bps for the defensive strategy and 5 bps for the RAFI Low Volatility strategy (almost as low as the Fundamental Index). The extended high–low range underlines the importance of index design. The basic low volatility strategy has the simplest methodology and the lowest simulated volatility. Nevertheless, a 185 bp difference in expected implementation costs seems too great to overlook.
Finally, the multi-factor strategies have moderate costs despite their added complexity. Mixing multiple single-factor portfolios tends to reduce costs because the constituent strategies find liquidity in different subsets of the market, and the trades occurring at the individual factor level may offset one another at the portfolio level.