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Thought smart beta indices had unlimited capacity? Think again!

Thought smart beta indices had unlimited capacity? Think again!

06-11-2018 | Insight

Following smart beta indices is a popular way to implement factor investing. But as money pours into these strategies, capacity issues cast doubt on their ability to keep their promises.

  • David Blitz
    David
    Blitz
    Head of Quant Research

Speed read:

  • Capacity of smart beta is far lower than some suggest
  • Delaying trades would improve feasibility
  • Active factor investing strategies have the necessary flexibility

When implementing a strategy, investors and asset managers will necessarily be faced with a number practical hurdles. These hurdles include elements such as direct and indirect transaction costs, constraints regarding the number of trades that can be executed with a certain period for a given security, or simply the management of investment in- and outflows.

As assets under management (AuM) grow, these hurdles only tend to get bigger and can end up having a considerable impact on performance. This is what determines the capacity of an investment strategy or an investment style: the point where additional inflows start weighing on the overall performance of a strategy or style.

The growing popularity of smart beta strategies has led many experts to wonder whether capacity issues might arise sooner or later. A recent study1 by one of the largest providers of smart beta products attempted to answer this question. It concluded that smart beta indices have a huge, almost unlimited, capacity. But we challenge this assertion.

Unfeasible smart beta trades

Indeed, we argue that smart beta trades simply become unfeasible at the AuM levels suggested in the study. Most trades would be ten to 100 times the typical daily volume of the stocks in question. Investors placing buy or sell orders of this size would probably trigger circuit breakers put in place by stock exchanges.

Meanwhile, the study does not consider trade feasibility and only looks at the estimated transaction costs of trades. Using a proprietary model, the authors give examples suggesting that the transaction cost estimates for common trade sizes are similar to those found using other transaction cost models, such as some of those used in the academic literature and even our own proprietary model.

But these models are calibrated for conventional trade sizes, which are typically in the range of 0% to 20% of the average daily transaction volume of a stock. Assuming that the predictions of these kinds of models can be extrapolated to trades bigger than ten to 100 times the typical daily volume of a stock clearly seems far-fetched.

Properly designed active factor strategies can provide the high capacity that investors are looking for

Flexible active factor investing

We also argue that properly designed active factor strategies can provide the high capacity that investors are looking for. The intuition behind this result is that while smart beta indices concentrate all their trades on just a handful of rebalancing dates every year, active factor strategies can trade gradually, making full use of the liquidity that is offered by the market.

In a simulation example, we rebalanced MSCI Minimum Volatility indices gradually by delaying trades. We found no loss in performance but a spectacular improvement in trade feasibility. This holds true not just for MSCI Minimum Volatility indices, but also for other smart beta indices such as MSCI Quality and MSCI Value-weighted indices.

1R. Ratcliffe, P. Miranda and A. Ang, 2017, ‘Capacity of Smart Beta Strategies from a Transaction Cost Perspective’, The Journal of Index Investing.

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