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Approaches to quantitative equity investing have evolved markedly. Thirty years ago, the focus was on alpha generation, but with the recent decade’s acceptance of smart (alternative) beta, the focus is turning to transparent methods of construction for factor investing. In this article, the authors present an approach for evaluating various methods of portfolio construction that lead to the same factor exposures. Four portfolios are of interest: factor weighted, cap weighted, equal weighted, and risk parity weighted. The authors compare these portfolios based on standard performance statistics as well as new metrics of value-added, such as performance participation rates and portfolio sector concentrations. The results indicate that once the desired factor exposure is achieved, it is beneficial to build the portfolio with the most desirable characteristics in terms of diversification.

Many investors that adopt minimum volatility strategies are inadvertently allowing unnecessary volatility in their minimum volatility allocations by ignoring the underlying currency assumptions used in building their portfolios. Specifically, portfolio country weights can vary widely depending on the currency used in calculating risk. In this article, the authors provide empirical evidence that the currency of the risk calculation should match the currency used in evaluating the portfolio’s performance. In practice, many investors are well aware of the latter but unaware of the former, which can be embedded in commonly available benchmarks and investment vehicles. Any mismatch can lead to increased volatility.

Investors are increasingly interested in portfolios that are not cap-weighted and that give exposure to specific factor premiums, which we call smart beta portfolios. In this article, we focus on the asset weighting used in smart beta portfolios and show that risk balancing when building factor exposure portfolios yields the most efficient capture of the factor exposure premium. This efficiency comes from both adequate intended factor exposure, which captures positive factor risk premium, and reduced risk concentration in unintended factors, which comes directly from the risk balancing. Other commonly used weighting schemes tend to have less efficient factor premium capture due in part to these unintended risk concentrations, which enter the portfolio because risk is not explicitly taken into account when weighting the assets.

In contrast to factor-based smart beta, diversification-based smart beta assumes that, seemingly naively, all investments are the same in some dimension. The four possible dimensions—portfolio weight, expected return, risk-adjusted return, and risk contribution—lead respectively to four naive beta portfolios: equally weighted, minimum-variance, maximum-diversification, and risk parity portfolios. In this article, the authors show how these four portfolios outperform the capitalization-weighted S&P 500 Index due to their sector differences and, especially, the index’s aggressive shifts into specific sectors. Among the three naive beta portfolios that use risk inputs as part of their portfolio construction, both minimum-variance and maximum-diversification portfolios tend to be highly concentrated in certain sectors. The authors develop an analytic framework based on a partitioned correlation matrix, modeling sectors as two groups (mostly defensive versus cyclical). The results shed light on material differences between the risk parity and optimized portfolios in their level of diversification. Empirical examples of sector portfolios within the universe of the S&P 500 Index show the triumph of naive beta over the index, which suffers from strong sector biases and ill-timed allocation shifts.

Practical Applications Summary In Not All Factor Exposures Are Created Equal, from the 2018 Quantitative Special Issue of The Journal of Portfolio Management, authors Eric Sorensen, Mark Barnes, Nick Alonso, and Edward Qian (all of PanAgora Asset Management in Boston, MA) evaluate the use of different portfolio construction methodologies for managing factor portfolios. They consider four types of portfolios that hold the factor exposure constant and have varying security weighting schemes: factor weighted (FW), cap weighted (CW), equal weighted (EW), and risk parity weighted (RP). The find that using a risk-aware weighting scheme exemplified by their RP portfolios generally achieves both strong upside participation and an advantageous defensive posture when a factor underperforms. They ascribe the advantage of the RP approach to sector positions, particularly in emphasizing exposure to defensive sectors such as consumer staples, healthcare, and utilities.

The ongoing globalization of world economies and introduction of the euro have prompted the reevaluation of country and sector effects on security returns. Here is research on the relative importance of countries and sectors and the changes over time. While sectors seem to be growing in importance, country effects remain significant. Given a belief that this situation will persist for some time, the authors argue that active strategies cannot afford to ignore country effects.

Overview Investors who still believe that a bold market cap-weighted portfolio always beats the less sexy quantitative approach to passive, diversified investing may be surprised by the results of new research showing that diversification-based beta, or “naive beta,” outperforms the S&P 500 Index . Authors Edward Qian , Nicholas Alonso and Mark Barnes (Director), all of PanAgora Asset Management , examine four types of naive beta—equally weighted, minimum variance, maximum diversification and risk parity—and find that the risk-parity portfolio offers the best diversification and the lowest turnover. “We’d like to remind people that in investing, ‘smart’ doesn’t always mean ‘good performance’, and ‘naive’ can be a winning strategy,” says co-author Edward Qian.