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Factor investing debates: Are there any capacity issues?

Factor investing debates: Are there any capacity issues?

30-12-2019 | Insight

Even though factor premiums may be persistent, harvesting them in a consistent and efficient way is no mean feat. Advocates of factor investing argue that it has a much higher capacity than most traditional fundamental strategies. Yet , concerns about crowding have been raised.

  • Yann Morell Y Alcover
    Yann
    Morell Y Alcover
    Investment Writer

Speed read

  • Capacity of factor strategies has become a topical issue
  • Most concerns about capacity relate to public factor indices
  • Proprietary active strategies help avoid this drag

These concerns arise from the idea that because premiums are public knowledge and have been widely documented in the academic literature, a growing number of investors will inevitably focus on a limited number of securities with attractive characteristics. So, while factor premiums may be persistent, this could lead to capacity issues for those looking to benefit from them.

Although some prominent academics argue that factor investing strategies have a very high capacity1, anecdotal evidence of overcrowding has also been reported and red flags raised. In a 2015 editorial published in The Journal of Portfolio Management, Bruce Jacobs warned that smart beta strategies are vulnerable to ‘crowding’, with increasing popularity posing a risk of overpricing and lower future returns2.

A year later, founder and chairman of Research Affiliates Rob Arnott co-authored a research paper provocatively titled ‘The incredible shrinking factor return’3. The study warned about the substantial slippage observed between theoretical and realized factor returns due to the costs of implementation. These included items that can be related to crowding, such as missed trades or bid-ask spreads.

More recently, Robeco’s Joop Huij and Georgi Kyosev showed how strategies based on popular factor indices such as the MSCI USA Minimum Volatility Index have suffered from chronic overcrowding and arbitrage. As trades are announced in advance, many market participants anticipate them, and investors who replicate these indices lose as much as 16.5 basis points per annum4.

Most of the red flags raised have to do with products based on publicly transparent factor indices

Two recent studies on low volatility by Robeco’s David Blitz, for instance, dispelled the crowding concerns associated with this popular factor. They showed that despite decades of research supporting low volatility investing, neither ETFs as a whole nor the more flexible, knowledge and opportunistic hedge funds have been positioned to exploit this factor. On the contrary5.

In fact, empirical studies suggest that when implemented properly, factor investing strategies have a very high capacity6. Some experts even argue that because of its systematic nature, factor investing has much larger capacity than most conventional active stock or bond-picking strategies, which tend to focus on small subsets of the investment universe.

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Public versus proprietary dilemma

In short, most of the discussion around potential capacity issues boils down to the public versus proprietary debate. While the early adopters of factor investing relied on proprietary strategies, most of the recent commercial push has come from generic approaches, so-called “smart beta” products, based on public factor indices.

These products have their merits. They are transparent and usually have very low fees. But they also have important drawbacks, in particular because – as mentioned earlier – they are prone to crowding. Most of that crowding comes from the fact that trades are publicly known in advance and that these trades are, by construction, concentrated on just a few rebalancing dates per year.

With strategies based on the MSCI Minimum Volatility indices, for instance, all trades must be carried out on the last business days of May and November. This severely limits the capacity of the strategy and explains the significant price distortions seen during the period between the announcement and effective rebalancing dates of these indices.

Active factor strategies can trade gradually, making full use of the liquidity offered by the market

Meanwhile, proprietary strategies may be less transparent and come with higher fees, but they have other advantages. One is that they can avoid overcrowding. 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 recent simulation exercise7, Robeco researchers gradually rebalanced the MSCI Minimum Volatility indices by delaying trades. They found no loss in performance and what they characterized as a spectacular improvement in trade feasibility. Robeco researchers believe this holds true not just for the MSCI Minimum Volatility indices, but also for other smart beta indices, such as MSCI Quality and MSCI Value-weighted indices.

What should investors do about this? (The Robeco view)

So, while the capacity of factor investing may be large and factor strategies may not be inherently subject to overcrowding, some products appear to us to be seriously under threat. Investors should therefore always keep in mind crowding risk and focus on strategies with proper capacity management – something public indices typically lack as they are not designed for high capacity.

To illustrate this with a concrete example, Figure 1 shows the losses incurred by investors in the widely used MSCI Minimum Volatility indices, due to price reaction before additions and deletions. As we can see, crowding and other phenomena, such as arbitrage, can be very costly for investors, and quickly wipe out the benefits of factor exposures.

Source: Huij, J. and Kyosev, G., 2018, “Price Response to Factor Index Additions and Deletions”, working paper. The sample period is November 2010 to December 2015 and includes a total of 11 rebalancing moments. The table shows turnover, cumulative abnormal return, and performance drag of MSCI Minimum Volatility Index additions and deletions. Turnover is the sum of the weight of all additions or deletions in the relevant index. CAR (AD:ED-1) is the cumulative abnormal return from the announcement day to one day before the effective day. Performance drag is calculated by multiplying the turnover and CAR of additions and adding the negative of the product of turnover and CAR of deletions. The four indices used are the MSCI USA Minimum Volatility (USD) Index (US), the MSCI World Minimum Volatility (USD) Index (Global), the MSCI Europe Minimum Volatility (USD) Index (Europe) and the MSCI Emerging Markets Minimum Volatility (USD) Index (EM). Abnormal return is calculated as the total USD return of the stocks in excess of the average total USD return of all stocks in the relevant factor index. Performance drag is measured in basis points.

This also means that choosing a product based mainly on the fee charged by the provider, without considering practical implementation aspects, may not necessarily lead to the highest net returns. Fees are important but they should not be considered in isolation. This topic will be discussed in the next article of this series.

1 See, for example, Ratcliffe, R., Miranda, P., and Ang, A., 2017, “Capacity of smart beta strategies: a transaction cost perspective”, The Journal of Indexing.
2 Jacob, B.I., 2015, “Is smart beta state of the art?”, The Journal of Portfolio Management.
3 Arnott, R., Kalesnik, V. and Wu, L, 2017, “The incredible shrinking factor return”, working paper.
4 Huij, J. and Kyosev, G., 2018, “Price Response to Factor Index Additions and Deletions”, working paper.
5 See Blitz, D.C., 2018, “Are Hedge Funds on the Other Side of the Low-Volatility Trade?”, The Journal of Alternative Investments. See also: Blitz, D.C., 2018, “Are Exchange-Traded Funds Harvesting Factor Premiums?”, Journal of Investment Consulting.
6 See for example: Li, F., Chow, T., Pickard, A. and Garg, Y., 2019, “Transaction Costs of Factor Investing Strategies”, Financial Analysts Journal.
7 Blitz, D. and Marchesini, T., 2019, “The Capacity of Factor Strategies”, The Journal of Portfolio Management.

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