By Noël Amenc, Professor of Finance, EDHEC-Risk Institute, CEO, ERI Scientific Beta
In the April 2017 issue of the Research for Institutional Money Management supplement to Pensions & Investments, the first article, produced as part of the CACEIS “New Frontiers in Risk Assessment and Performance Reporting” research chair at EDHEC-Risk Institute, explores a novel approach to address the challenge raised by the standard investment practice of treating attributes as factors, with respect to how to perform a consistent risk and performance analysis for equity portfolios across multiple dimensions that incorporate micro attributes. Our study suggests a meaningful new dynamic approach, which consists of treating attributes of stocks as instrumental variables to estimate betas with respect to risk factors for explaining notably the cross-section of expected returns.
In our second article, we provide a broad overview of initiatives to launch new forms of alternative indexes based on the market value of debt, which can be broadly classified into two different categories — fundamental approaches and diversification approaches. Our main conclusion is that none of the approaches successfully addresses all the key concerns and challenges involved in designing a truly investor-friendly bond benchmark.
The focus of smart beta strategies has recently shifted to encompass the fixed income asset class. We put the search for factors and beta strategies in the context of asset pricing, and we show that compensation for non-market factors is not just allowed, but actually required, by financial theory. We explain the various questions answered by time-series and cross-sectional analyses of risk premia and then focus on fixed-income instruments, presenting the time-series and cross-sectional formulations for the search of priced risk factors. We finally explain the unique challenges encountered in identifying priced risk factors in fixed-income products and present the main findings obtained to date.
The results of our research on smart beta replication costs provide an explicit estimate of costs applied to a range of strategies and show the impact of using different implementation rules or stock universes. Our replication cost analysis is straightforward and can be easily applied to other strategies. This research was produced as part of the Amundi ETF, Indexing & Smart Beta “ETF and Passive Investment Strategies” research chair at EDHEC-Risk Institute.
In research supported by BdF Gestion, we examine the argument that portfolio rebalancing, defined as the simple act of resetting portfolio weights back to the original weights, can be a source of additional performance. Our analysis highlights that size, value, momentum and volatility are sorting characteristics which have a significant outof-sample impact on the rebalancing premium. The selection of small cap, low bookto-market, past loser and high volatility stocks generates a higher out-of-sample rebalancing premium compared to random portfolios for time horizons from one year to five years.
We introduce a new approach with the objective of maximizing exposure to the long-term rewarded equity factors in a “top-down” framework, in a robust and well-diversified manner. Scientific Beta’s Multi-Beta Diversified Max Factor Exposure index dynamically allocates across single-factor indexes in order to retain diversification benefits and obtain maximum exposure while maintaining balance across factors and reasonable diversification levels. We examine the respective merits of the “top-down” and “bottom-up” approaches to multi-factor portfolio construction. “Top-down” approaches assemble multi-factor portfolios by combining distinct sleeves for each factor, while the “bottom-up” methods build multi-factor portfolios in a single pass by choosing and/or weighting securities by a composite measure of multi-factor exposures. We find that focusing solely on increasing factor intensity leads to inefficiency in capturing factor premia, as exposure to unrewarded risks more than offsets the benefits of increased factor scores.
We present the results of the first in-depth survey of institutional investors’ perceptions and expectations of infrastructure investment. It documents the reasons for investing in infrastructure and whether currently available investment products or strategies are helping investors meet these objectives. The findings provide a first step toward integrating infrastructure in long-term investment solutions. Key findings are reported in the following areas: investment beliefs; products and objectives; benchmarking; and ESG (environmental, social and governance).
We ask whether focusing on listed infrastructure stocks creates diversification benefits previously unavailable to large investors that are already active in public markets. This would mean that listed infrastructure is expected to exhibit sufficiently unique characteristics to be considered an “asset class” in its own right. We conclude that what is typically referred to as listed infrastructure is not an asset class or a unique combination of market factors.