“Levered and Inverse Exchange-Traded Products: Blessing or Curse?” That’s the question Colby J. Pessina and Robert E. Whaley posed in our first research article. Their answer: Curse! Levered ETPs are the third generation of exchange traded products — the first being conventional ETFs holding underlying components, the second, fully collateralized funds. Levered and inverse ETPs are designed to provide geared long and short exposures to the daily returns of different benchmark indexes. This article demonstrates that, over multiple holding periods, these products are designed to fail. They exist only as a mechanism for placing short-term directional bets. The authors say, “Levered and inverse products are not, and cannot be, effective investment management tools.” Investment professionals catching up on their learning in this area will find in this article an excellent background on the origins and status of ETPs and a contextualized refresher on the economics notions of “normal backwardation” and “contango” in the futures markets and non-carry markets.

Our second article in this issue applies volatility timing to a multi-mutual fund strategy, asking: “Should Mutual Fund Investors Time Volatility?” Yes, they should. Feifei Wang, CFA, Xuemin (Sterling) Yan, and Lingling Zheng find that increasing investment in an actively managed mutual fund when fund volatility has recently been low and decreasing investment when volatility has been high leads to a significant improvement in investment performance. Specifically, volatility-scaled funds exhibit significantly higher alphas and Sharpe ratios than the original (unscaled) funds. The authors also find that scaling by past downside volatility leads to even greater performance improvement than scaling by total volatility.

Our next article asks whether value investing is really over. In their article “Reports of Value's Death May Be Greatly Exaggerated,” Robert D. Arnott, Campbell R. Harvey, Vitali Kalesnik, and Juhani T. Linnainmaa offer several explanations for value’s long period of poor performance. Along the way, they provide a wonderful resource of charts and analysis for anyone who wants to engage in the value and broader factor history discussion. The authors interrogate the three most plausible explanations for the large drawdown of value investments over the last 13 years. The most important of these being that the common price-to-book definitions of value fail to account for intangibles. Many of the large modern companies have large investments in intangible assets leading to a misclassification of value companies as growth. The solution is to capitalize these intangibles and the article includes excellent illustrations of the effect of this important revision of the value metric to its historic performance: a much less severe drawdown over a much shorter period. If you’re looking for key points in the argument for value being alive and well and due to rebound, this is the go-to article.

In “Toward ESG Alpha: Analyzing ESG Exposures through a Factor Lens,” Ananth Madhavan, Aleksander Sobczyk, and Andrew Ang analyzed the link between funds’ bottom-up, holdings-based ESG scores and their style factor loadings. They find that funds with high ESG scores have profiles of factor loadings that are different from those of low-scoring ESG funds. Funds with a high environmental (E) score, for example, tend to have high quality and momentum factor loadings. In unpacking ESG scores into components that are related to established factors and those that are not, the authors find strong positive relationships between fund alphas and factor ESG scores. In short, if we know about factors and their performance, then the relationship between these factors and bottom-up ESG scores are worth knowing too.

In our final research article, authors Mark Kritzman, CFA, Ding Li, Grace (TianTian) Qiu, and David Turkington, CFA, take a fresh look at portfolio construction using scenario analysis. The novelty here is “Portfolio Choice with Path-Dependent Scenarios” that is, economic scenarios that form a path rather than single-horizon averages. Specifying scenarios as paths produces a richer set of data such as associated portfolio return paths and therefore richer portfolio metrics on which to make investment decisions. The article is full of examples of how to make use of these scenario paths, assign probabilities to these scenarios and generate forecast asset class returns in consistent and intuitive ways, ultimately turning these into investment portfolios. This approach contrasts with Monte Carlo simulation, for example, because it is more direct and intuitive and requires fewer scenarios.