Smart beta investing goes by many names, including alternative beta, advanced beta, systematic beta, and even rules-based active. Interest in this approach came from academic research arguing that, on an ex-post basis, the capitalization weighted market portfolio/index lacks investment efficiency. That is, weighting schemes other than capitalization weighting produce portfolios that historically have realized higher risk-adjusted returns (Sharpe ratios) than the capitalization-weighted market.
To address the potential problems of capitalization weighting, various new non-capitalization-weighted products have been developed, for example, fundamental indexing, equal-weighted and diversity indexes, and minimum-variance portfolios. These portfolios have shown, based on historical performance, to potentially produce higher risk-adjusted returns compared to the market.
With the introduction of these solutions, an obvious question arises: What explains the outperformance of these strategies compared to the market?
Various academic studies that analyzed the historical market outperformance of these strategies reached similar conclusions. The strategies appear to historically outperform the capitalization weighted market because they tend to have high exposures to equity common factors: market, value, size, momentum, volatility and quality.
These common factors can explain cross-sectional differences in historical returns and/or risk for individual securities, much like the market (i.e., the Capital Asset Pricing Model [CAPM]).
Factor performance
Studies show that the outperformance of various smart beta strategies relative to the cap-weighted market is largely explained by exposures to common factors. For example, outperformance of fundamentally weighted portfolios can be explained by their exposure to value, equal-weighted and diversity portfolios by size, and minimum-variance portfolios by low volatility.
To find out how each factor performs over time, we analyzed its historical performance characteristics. According to our back-tested performance results, these common factors depict the following characteristics compared to the market:
Momentum: Higher total return and market-like drawdown
Value: Higher total return and market-like total risk and drawdown
Quality: Higher total return, market-like risk and lower drawdown
Volatility: Market-like total return, lower total risk and lower drawdown
Factor diversity
The common factors just described depict low or negative pair-wise active return correlations. Our back-tested performance results show an average off-diagonal pair-wise correlation of -44% among the four factors. This result may indicate that market underperformance of one factor may be offset by the market outperformance of other factors. Further, an examination of calendar year returns shows no instance of all factors underperforming the market at the same time.
An obvious question is, can a factor combination portfolio potentially provide a more stable return stream than individual factors? To answer this question we create a Factor Diversity Index (FDI), which is an equally weighted combination of individual factor indexes. In our back-tested results, the average tracking error across individual factor indexes is 5.2%, and the tracking error of the FDI is 2.4%.
The gain from diversification, based on our back-tested results, amounts to over 50%. We believe this result indicates that the FDI can dominate individual factors. That is, a combined factor portfolio may experience a higher Sharpe ratio, higher information ratio and lower market underperformance than individual factor portfolios over time.
Smart beta investing may allow for a more efficient portfolio structure as a result of the potential diversification benefits available through factor combinations and uncorrelated alpha, as well as higher implementation efficiency. In short, lower implementation costs.
As a result, we believe allocations from both traditional passive and active will continue to drive the growth in smart beta investing.
Khalid (Kal) Ghayur is Managing Director, Quantitative Investment Strategies, Goldman Sachs Asset Management