Factor Investing and the Search for Uncorrelated Premium Returns
Investors’ interest in factor-based passive strategies continues to grow. According to ETFGI, European exchange-traded funds (ETFs) weighed by fundamental factors other than market capitalization have attracted net new inflows of $9.4 billion so far in 2017, a 30% increase from the same period a year earlier.
This provides a great opportunity to take a look back at the origins of factor investing. In coming articles, PULSE ONLINE will review the individual strategies covered by the iSTOXX® Europe Single Factor indices.
Identifying the origins of risk and returns
The study of factor strategies originated as a search to pinpoint the sources of risk and returns in the market.
The traditional Capital Asset Pricing Model (CAPM) had since the 1960s dominated portfolio theory, and suggested only one systematic source of returns and risk: the market factor, or beta. CAPM held that all other differences in equities’ returns were attributable to idiosyncrasies, i.e. company-specific drivers independent from market returns.
From the CAPM to multiple factors
Over the following decades, however, empirical research discovered additional parameters that may consistently yield returns separate to the market beta, or so-called risk premia.
In 1991, Robert A. Haugen and Nardin L. Baker introduced the first such factor: low volatility.1 Just one year later, finance professors Eugene Fama and Kenneth French added two more: size and value.2 Their research showed that small-cap companies as well as businesses that were undervalued relative to their book value, outperformed. In 1997, Mark M. Carhart, founding partner of Kepos Capital, threw a fourth factor in the mix – momentum.3
More factors have been added since, including carry – or growth – and quality. They all have one thing in common: they can help explain equity returns and can generate a premium.
Targeted exposure through factor indices
The possibility of targeting investments by such factors wasn’t missed by the indexing business, which started offering products that go beyond traditional market cap-weighted benchmarks.
In 2016, STOXX launched the iSTOXX Europe Single Factor index family, developed in collaboration with Alpha Centauri. It offers investors a unique and innovative way to capture risk premia based on one factor only.
Investors can choose exposure to STOXX® Europe Total Market index members that rank high in six key sources of systematic risk and potential returns: value, carry, momentum, size, low risk and quality.
In the iSTOXX Europe Single Factor indices, each factor consists of several fundamental ratios that are applied to rank stocks. The final index weights result from an optimization process that seeks maximum factor exposure while constraining systematic risk beyond the factor tilt.
Chart 1 shows the indexed excess returns of each factor over the benchmark in the last seven years. What is immediately evident is that the six factors have outperformed the benchmark, albeit in varying degrees.
Uncorrelated returns: looking for the ‘building blocks’
Besides offering higher returns, risk factors can also hold the key to a portfolio characteristic that is precious for investors: diversification. Eugene L. Podkaminer at Callan Associates goes as far as to say that risk factors can provide the diversification that investors historically – and mistakenly – attributed to a portfolio of different asset classes.4 During the global financial crisis, for example, theoretically uncorrelated investments moved in sync.
Data show that the iSTOXX Europe Single Factor indices are highly uncorrelated, and even negatively correlated, with the STOXX Europe 600. Additionally, correlations between individual factors are low.
“Empirical results show that factor indices can provide significant excess returns relative to their benchmark,” said Dr. Jan-Carl Plagge, Head of Applied Research at STOXX Ltd. “But even more interestingly, factor-based strategies are indeed a potential source of uncorrelated returns, supporting portfolio diversification.”
Keeping tracking error in check and improving tradability
The factor indices cap the component number and weights. They add membership constraints around sector, country and beta to the broader market, and impose turnover and liquidity requirements. This aims at maximizing the exposure to the respective targeted factor while staying within a tracking error of 3% relative to the benchmark index.
A passive multi-factor strategy
Although factor premia tend to outperform the market in the long run, they are found to vary significantly over time. This observation, as well as the low correlation among factor returns, supports the use of a multi-factor strategy. The iSTOXX® Europe Multi-Factor index does not merely bundle single-factor indices but uses companies’ aggregated exposure to all factors. Selected companies thus tend to score high across the board.
Capturing pure risk premia
Factor investing can also be used to capture factor premia in total isolation from the market’s directionality and volatility by hedging the latter via a market-short positioning. This eliminates the market’s inherent risk that is carried forward when targeting factor returns.
STOXX has therefore introduced the iSTOXX® Europe Single Factor Market Neutral Indices and the iSTOXX® Europe Multi-Factor Market Neutral Index to follow such a strategy in a passive way. You can read more about them in one of our recent articles.
A growing tool in modern portfolios
The study and exploitation of risk factors has grown into a pillar of modern portfolio theory and continues to attract new followers. Factor-based passive strategies can provide access to targeted sources of risk while additionally offering low correlation to the market and limiting volatility.
Please visit us in coming weeks as we review our single-factor strategies more in depth.
1 Haugen, R. and Baker, N. (1991). The efficient market inefficiency of capitalization-weighted stock portfolios. Journal of Portfolio Management, 17, 35-40.
2 Fama, E. F., and, K. R. French (1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47 (2), 427-465.
3 Carhart, M. M. (1997). On persistence in Mutual Fund Performance. Journal of Finance, 52 (1), 57-82.
4 Podkaminer, E. L. (2013). Risk factors as building blocks for portfolio diversification: The chemistry of asset allocation. Investment Risk and Performance Newsletter, 2013(1).
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