The quantitative finance world has recently been transfixed by its version of the East Coast/West Coast feud. But instead of Brooklyn versus Compton rappers, it’s their suburban cousins Greenwich versus Newport Beach arguing over factor timing.
Average household income
East Coast (New York Area)
Notorious B.I.G. (RIP), Sean Combs (Bad Boy)
Cliff Asness (AQR)
West Coast (Los Angeles Area)
N.W.A., Suge Knight (Death Row)
Rob Arnott (Research Affiliates)
Source: City Data, retrieved on May 24, 2017
It is a complicated topic but a quick summary is that Mr. Arnott believes that a) factors can be timed, and b) popular factors are
currently crowded. Conversely Mr. Asness thinks a) it’s difficult and expensive to time factors, and b) current factor valuations are not
extreme. Institutional Investor magazine has more details at: Factor Fight Rages on Between Asness and Arnott.
Given that our hubs are London and Denver, we’ve stayed clear of this bi-coastal debate, but clients have asked us for our view. With no
axe to grind, we end up somewhere in the middle. Like all investing, if you can time your trades perfectly (or at least better than
average) you can beat a mechanical portfolio. There are occasional bubbles (and busts) when a particular factor gets crowded and
expensive, and vice versa. Good timing can, by definition, add to returns.
But it’s hard to get this right – and available evidence from ETF-land suggests that the average investor is terrible at factor timing.
To show this, we analyse the largest 20 “smart beta” ETFs listed in the US – each have at least $3 billion in assets.
There are three themes that have been popular in smart beta ETFs for several years already – dividends (~$65 billion), low volatility
(~$35 billion), and equal weighted (~$16 billion) – so we have a decent history for these styles. Although there are now ETFs
available for other factors e.g. value and momentum, those are mostly newer, smaller products.
1. Dividends – the largest ETF for the dividend factor manages ~$17 billion. The ETF has very active
creation/redemption (a proxy for net buying/selling respectively) with the number of outstanding units varying considerably over the last
few years. The chart below has two lines – the grey line (left axis) shows the number of ETF units outstanding, while the red line (right
axis) shows the relative performance of the dividend factor (ratio of ETF total return to S&P 500 total return) over the last three
Chart 1: Dividend ETF Units Outstanding and Relative Performance
Source: Janus Henderson, Bloomberg, as at 15 May 2017. Chart shows the price and units outstanding for the iShares Select Dividend ETF.
Notably, the total return on the ETF has matched the S&P 500 total return (the relative factor is exactly 1.00 currently) e.g. an
investor should have achieved the same return whether picking the ETF or the S&P 500. But the chart above shows that the number of
units outstanding typically went up when the dividend factor was outperforming, and went down when the dividend factor was
Looking at only the days that the ETF units were created (net buying) – relative performance averaged 1.011 – i.e. dividend stocks had
outperformed the S&P by 1.1%. Similarly for days that ETF units were redeemed (net selling) – relative performance averaged 0.987 –
ie dividend stocks had underperformed the S&P 500 by 1.3%. So by actively trading the dividend factor, investors on average lost 2.4%
relative to the S&P 500.
This is likely return chasing – when dividend stocks are outperforming, money comes into the factor. Or perhaps money comes in, and
dividend stocks outperform – it’s hard to decisively determine the direction of causality. But either way, this example shows that timing
the factor is costly to the average smart beta ETF investor.
Source: XKCD (Randall Munroe) https://xkcd.com/552/
2. Low Volatility – the largest ETF for the minimum volatility factor has ~$13 billion in assets. Charting the ETF
versus relative performance shows a similar result. Although the low volatility factor has done quite well by outperforming the S&P
500 by ~4% over the last three years, the average investor in this ETF has underperformed the S&P 500. On average, creations (net
buying) took place at a 1.069 relative performance, and redemption (net selling) took place at a 1.049 relative performance. So investors
on average lost 2% relative to the S&P 500 by transacting, and the factor is currently at 1.039 or about 3% below the average entry
level of 1.069.
Thus, even in a factor that has clearly outperformed the S&P 500, the average investor is worse off than a passive S&P 500
Chart 2: Low Volatility ETF Units Outstanding and Relative Performance
Source: Janus Henderson, Bloomberg, as at 15 May 2017. Chart shows the price and units outstanding for the iShares MSCI Min Vol USA
We see this effect even in low volatility products targeting international markets. A minimum volatility emerging markets ETF was
extremely popular in recent years as investors wanted EM exposure but also low volatility. Consequently this ETF significantly
outperformed the broader MSCI EM index and inflows poured in. However, this relative performance has reversed with the low volatility ETF
now trailing the basic EM index significantly. Over the past year, it has underperformed by ~16%, missing much of the emerging markets
Chart 3: Low Volatility Emerging Markets ETF Units Outstanding and Relative Performance
Source: Janus Henderson, Bloomberg, as at 15 May 2017. Chart shows the price and units outstanding for the iShares Min Vol Emerging
3. Equal-Weighted – this is the simplest and oldest smart beta factor – equal-weighting an index provides a modest
size and value tilt relative to market capitalization weights. Given the strong market rally, equal-weighted has underperformed
market-cap by about 3.5% over the past 3 years. The largest ETF in this space manages ~$13 billion. Notably, this has been a stickier
product with assets relatively stable. Nevertheless, even in this ETF, the number of units outstanding has often correlated with relative
performance as shown in the chart below. The average buy level is about 1% above the average sell level (in relative performance terms).
Chart 4: Equal Weighted ETF Units Outstanding and Relative Performance
Source: Janus Henderson, Bloomberg, as at 15 May 2017. Chart shows the price and units outstanding for the Guggenheim S&P 500 Equal
All the examples above show a consistent result – factor timing is hard and the average investor, at least those using ETFs, tend to get
it wrong by piling into factors that are working and vice-versa. Instead, this investor behaviour is often a signal to do the opposite
trade – buy after large outflows, and sell after large inflows to a factor ETF. We’ve previously written about the same effect in country
ETFs – see our February 2017 note: The Curse of the ETF Tourist.
In the alternative funds that we manage, we typically prefer strategies that instead provide liquidity e.g. buy when others need to sell
and vice-versa. Doing this has historically generated alpha over the long-term, avoids crowding, and minimizes transaction cost impact.
We look for these flow-driven price pressure trades in both equity and fixed income markets.
Importantly, we are not a factor shop like our East Coast/West Coast brethren – we typically avoid running well-known quant factors.
Assets in smart beta ETFs are likely just the tip of the iceberg. There is certainly much more money running similar factor strategies
in managed- and internally-run accounts in addition to what is publicly disclosed by ETFs and mutual funds. Thus, we cannot ascertain
whether these factors are crowded or not, and we largely sidestep this quant feud by mostly pursuing strategies that are independent to