Smart Beta 2.0 - EDHEC-Risk

1. The Risks of Smart Beta Strategies
Variance (GMV) “portfolio averages about
120 long securities, i.e., about 12% of
the 1000-security investable set”. Likewise,
DeMiguel, Garlappi, Nogales and Uppal
(2009) argue that “Short sale-constrained
minimum-variance portfolios […] tend to
assign a weight different from zero to only
a few of the assets”. This concentration
leads to biases towards low risk (beta)
stocks and utility stocks (Chan, Karceski and
Lakonishok (1999)).
These style biases, and more globally these
risk factors, had been ignored in the
promotion of the performance of firstgeneration
smart beta indices. The foundation
paper on fundamental indexation (Arnott,
Hsu and Moore (2005)), which highlights
the outperformance of this new form of
beta in comparison with cap-weighted
indices, does not contain any measure of
the exposure of fundamentally-weighted
indices to different style factors. The first
documentation on these risk factors, and
notably the value bias that explains a large
part of the outperformance of fundamental
indices was produced by others than the
promoters of these indices and was only
published in 2007/2008. 7 This has then led
the promoters of fundamental indexation
to justify the outperformance with new
arguments that were not raised in the
original 2005 article. 8 Likewise, promoters
of equal-weighted indices have documented
outperformance without correcting
performance for risk factor exposures such
as small cap and liquidity risk 9 and promoters
of equal risk contribution (ERC) strategies,
while pointing out that fundamental equity
indexation suffers from value exposure, did
not report any results on value and small
cap exposures of the ERC strategy in their
foundation paper (Demey, Maillard and
Roncalli (2010)).
We can also note that certain weighting
schemes that are supposed to, for example,
provide well-diversified proxies for efficient
frontier portfolios can lead to considerable
concentration in a small number of stocks
and/or exhibit very pronounced sector or
style biases.
Exhibit 4: Concentration of Minimum Volatility portfolios in low volatility stocks – The universe contains 100 stocks, the volatilities
of which are equally spaced in the corresponding intervals. Correlations between stock returns are assumed constant. The Global
Minimum Variance (GMV) portfolios are long-only. For additional details, see Amenc, Goltz, and Stoyanov (2011). These results are
based on equally spaced volatilities between 16% and 18%.
7 - Perold (2007) argues that “Fundamental indexing is a strategy of active security selection through investing in value stocks”. Jun and Malkiel (2008) assess the
performance of the FTSE RAFI index and find that the alpha of this index is zero after adjusting for market, value and small cap exposure.
8 - Initially, the outperformance of the strategy was explained by a capacity to avoid exposure to the most overpriced stocks by weighting stocks by their economic footprint.
For example, Arnott, Hsu and Moore (2005) explain the outperformance of their fundamental weighting scheme by stating that for cap-weighted indices “an over-reliance
on overpriced stocks and under-reliance on underpriced stocks leads to lower risk-adjusted performance relative to hypothetical fair value weighted strategies…”. Later,
Arnott (2009) describes the methodology as a contrarian strategy. West (2010) describes the methodology as dynamic value strategy which has an ability to time the value
premium.
9 - For example, Dash and Zheng (2010) analyse the performance of the S&P 500 equal-weighted index. While they provide evidence on style exposure to small cap and
value, they do not provide any formal attribution of performance to these exposures.
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