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ESG integration in investors’ portfolios is becoming a market standard. ESG ratings measure the strengths and weaknesses of a company along many specific criteria related to Environmental, Social and Governance issues. Many reasons lie behind this gradual switch, from genuinely motivated investors willing to align their investments to their values, to investors that recognise the reputation risk related to controversial investment practices, finally to investors that recognise the financial risks coming from companies with poor ESG profiles.If investors agree on the usefulness of ESG integration for risk management purposes, there is still no clear consensus about the ability of ESG integration in delivering higher returns. The prevalently negative assessment between Corporate Social Performance (CSP) and Corporate Financial Performance (CFP), in vogue in the ’1970 has changed significantly over time, and now the overwhelming majority of empirical research share a clear optimism about the link between CSP and CFP. Although we do not share the most extreme optimism regarding the power of ESG as performance-enhancer, we recognize the connection between economic strength and sustainability from a general perspective. Therefore, if ESG filtering does not bring out-of-sample outperformance, it is not necessarily because ESG information is not relevant. There is indeed no fundamental reason for an aggregated metric such ESG to deliver consistent outperformance over time . ESG ratings average very diverse indicators and therefore are not well suited to differentiate stocks from different sectors or countries for financial purposes. Knowing if ESG can bring performance is an important issue since both regulation and industry trends are pushing investors towards widespread ESG integration. In this research note we will show how simple ESG filtering approaches fail to outperform their benchmark, even if we must acknowledge that, overall, they do not bring specific underperformance either. Our results are in line with what investor can achieve by tilting their portfolios towards the best ESG performers: As an example, over the period Dec, 2010 to Dec, 2018, the MSCI World ESG Leaders Net Return USD Index delivered a compounded 71.42%, slightly less than the market benchmark MSCI World Net Return USD Index at 73.19%. Even if the former shows a better ESG profile, ESG itself did not bring any particular performance improvement. Optimistically-minded investors can still see that the gap is very little and worth to pay in order to achieve an improved ESG profile, but this does not dispense us from questioning why. While several motives may operate simultaneously, both the diversity of the investment universe and the lack of granularity in ESG ratings (which are usually used in their aggregated form) represent a challenge from a financial perspective. In other words, the lack of consistent outperformance for simple ESG filtered strategies is due to the difficulty to extract useful information from large and sparse data such as ESG ratings in an efficient way. In the second part of this note, we explicitly design a machine learning algorithm that enables us to extract useful information from ESG data. More precisely, it identifies strong and consistent patterns between companies’ ESG profiles and their expected likelihood to outperform. We finally show how granular ESG information and machine learning provide robust financial signals and how to use them efficiently even in simple investment strategies. The investment universe considered in this paper is the capitalization-weighted Solactive GBS World Index which includes the largest companies listed in the US, Canada, Western Europe, Japan, Australia, New Zealand, Hong Kong and Singapore, from October 2012 to December 2018. Portfolios are calculated in USD. Stock prices and dividends are taken from Thomson Reuters/Datastream while ESG ratings from Sustainalytics.
In this paper we introduce an aggregated controversy metric, derived from ESG data, which targets specific issues companies face in the environmental, social or governance fields. By building portfolios based on this controversy measure, we show that in Europe and the US stocks that undergo severe controversies significantly undeperform their benchmarks and portfolios consisting of stocks with low or no controversy at all. The main reason for this is that, in both cases, markets tend to react strongly to changes in controversy levels and penalize stocks that experience ESG downgrades. Our results make a clear case for the potential benefits of excluding stocks with high controversy levels from investment universes. These findings were not confirmed for the Asia-Pacific region, where the portfolio consisting of highly controversial stocks outperformed its benchmark, although their number was lower compared to Europe and the US.
In this paper, we use a performance analysis framework to analyze Smart Beta strategies against their benchmark. We apply it to Minimum Variance Strategies for which live track records exist for all major equity markets. We illustrate how naïve return comparison could be misleading and suggest ways to assess the added value of these strategies. We extend our analysis to other equity strategies and look at robustness across regions.
In this paper we analyze the problem of multi-factor investing from an active fund-of-fund selector perspective, or investors who seek to achieve a diversified exposure to different factors using liquid, transparent and cost-efficient ETFs. We compare Ossiam’s European ETF with a clear identified factor exposure (Size, Value, Volatility and Dividend) and ETFs from other providers that track MSCI’s factor indices. The main result of this study is that even for a very simple multi factor allocation schemes (as the equally weighting), the choice of single factor ETFs serving as building blocks matters. More precisely, the methodologies underlying Ossiam’s ETFs make them more suitable from a multi-factor perspective, as they turn out significantly different from each other and from the capitalization-weighted benchmark. This is less evident for ETFs from other providers that track MSCI’s factor indices since, by construction, their methodologies forces them to be closely linked to their benchmark. As such, for a fund-of-fund manager, it is very difficult to find “space” of diversification within the other providers’ ETF with MSCI underlying factor indices, as they are all similar to each other and to the benchmark. In other words, these strategies do not respect the underlying factor premium because they aim, among other things, to capture it while being very close to the benchmark. Conversely, Ossiam’s ETFs respect and preserve the very dynamic nature of factors’ premia, which means that some ETFs will outperform the benchmark (because the underlying factor is) while some will not. In this sense, they are more pure and more suited for multi factor investing.
As we show in this study, for all three allocation schemes we consider (equally weighting, risk parity and momentum tilting) we do not notice any benefit from combining other providers’ ETFs, while Ossiam’s ETF-based multi factor portfolios are able to achieve both absolute and relative outperformance over the benchmark.
We assess the value added of a multi-factor portfolio from a performance-agnostic point of view. First we introduce a broad general de nition of factor, that encompasses usual factors like Size or Value, then we prove that static longshort multi-factor strategies (as the equal weighting of factors)
are indeed factors according to our de nition. This result is new in the literature and states that, by investing in a long-short static multi-factor strategy, one is indeed investing into a new (synthetic) factor. Finally we test the strength of such a synthetic factor compared to each single factor by looking at its predictive power. We empirically test the equal-weighting of Value, Size, Momentum and Low Volatility in the US and Europe. Our conclusion is very clear in both regions: the equal-weighting of these four standard factors is a synthetic factor that has no predictive power on stocks' return, while each of the factors shows clear ability to distinguish among stocks. In other words, the measure that underlies this equal-weighting of factors has zero predictive power on cross-sectional di erences in stocks' returns.
The ambitious objectives stated in the latest Paris Agreement 2015 to limit the average global increase in temperature at the 2°C before preindustrial levels, and possibly to 1:5°C, will need tectonic shift in infrastructure, energy production, industrial processes and, down in the scale, to our lifestyles. As investors will play a key role to finance these objectives, they need a robust framework to measure the impact of their investments while monitoring long-term risk related to climate change. Derived from standard financial reporting, we propose such a framework to measure a complete carbon footprint for dynamically rebalanced funds. We enhance this with the derivation of carbon performance attribution of funds versus their benchmarks, both for absolute (ex. carbon emissions) and relative measures (ex. carbon intensity).
This paper intends to explain the modeling strategy, the empirical Bayesian implementation (estimation), and to illustrate a simple but plausible application of the power of Markov switching models in classical performance and risk analysis. We apply such models for strategies based on US stocks and compare an extension of the standard four-factor model including a new volatility factor to a Markovswitching three-factor model.
In this paper, we use a quantitative approach to compare different alternative beta strategies, based on statistical relations among their returns.
Using correlations, principal component analysis, regression factor models and minimum spanning tree graphs, we identify and quantify statistical closeness of these portfolios. Our results show that, when measured by return comovements and common systematic risk exposures, different alternative beta portfolios are on average quite close to each other.Surprisingly, in some cases returns of portfolios with different strategic approaches can be more similar than those of two portfolios representing different variations of the same approach. Using a formal clustering technique we show how to identify distinct clusters within a set of alternative beta portfolios. Given potential redundancy of alternative beta,their clusters can give better diversified set of building blocks for multistrategy allocations than individual strategies themselves. We build several portfolio allocations using clusters of alternative beta strategies as building blocks and compare individual strategy-based and cluster-based
allocations, both within static and dynamic allocation frameworks. We find that cluster-based allocations have better risk-return pro le with
respect to allocations of individual strategies, despite having somewhat less diversified factor exposures.
Minimum variance and low volatility investment solutions have become very popular over the last few years. Many traditional active asset managers and exchange-traded fund providers now offer some kind of volatility strategy, applied to single country, regional or global equity markets. One may reasonably argue that the recent flows into the low volatility strategies might affect their future performance because of the flow pressure on the prices of the companies that generally are selected by this investment approach. More recently, some market participants have even referred to low volatility investing as " a bubble". We look at four objective measures of "expensiveness" to detect whether low volatility strategies applied to developed markets are overcrowded: fund flows, fundamental valuation, relative performance, quantitative approach.
This paper assesses the exposure of low volatility portfolios to interest rate risk and determines whether this hidden risk can explain the difference in absolute and risk-adjusted performances of low versus high volatility portfolios over the period 1990/2014 in the US equity market. The addition of the interest rate risk factor fails to improve the explanatory power of a 4-Factor model (Market, Size, Value and Momentum) for both low and high volatility portfolios. Moreover, it does not reduce the alpha generated, at least by low volatility portfolios. Although the 4-Factor model, completed or not with interest rate risk factor, fails to capture a significant portion of low volatility portfolios' total variations, we find significant small but positive duration ranging between 1.5 and 1.9. This duration is mostly due to the historically high exposure of low volatility portfolios to the Utilities sector. Finally, we do not find statistically significant evidences of the fact that hidden interest rate risk is responsible for the more-than-predicted performances of low volatility portfolios. This pushes us to reconsider the widespread belief that low volatility stocks are essentially bets on interest rates.
Commodity futures have evolved from trading instruments to vectors of long-term investment for this new asset class. As uses and users have changed, new ways of representing the performance of commodities class and investing in it have to be devised. In this article, we outline the drawbacks of the traditional commodity indexing solutions and we expose solutions realigning commodity investing with long term investor goals. In particular, we detail risk management techniques, at the single commodity level as well as at the portfolio level, that can be used to build a balanced investment benchmark.
Smart beta is a new breed of investments which relies on a quantitative investment process in order to correct inefficiencies of capitalization-weighted portfolios. It is based on academic research findings implemented in a transparent way and supplemented with protective mechanisms, such as portfolio constraints, to allow the strategies to work reliably. But should these strategies be managed passively, or should they be under the supervision of managers who are free to change systematic models with discretionary decisions?
Portfolio rebalancing can enhance alternatively weighted index performance, but it's no guarantee
Whereas much has already been said about the performance of Minimum Variance portfolios and their ability to reduce portfolio volatility and drawdowns, the topic of the fundamental characteristics of Minimum Variance portfolios has hardly been covered. In this study we compare the Minimum Variance portfolios to the market-capitalization weighted benchmarks across five fundamental axes: valuation, growth, size and profitability and debt. The study is performed in four different geographic areas: the US, Europe, Global Developed Markets and Emerging Markets. For each geographic zone, the Minimum Variance portfolios are comprised of the most liquid stocks and follow the portfolio construction methodology that is used in the indices underlying Ossiam ETFs.. The results suggest that different Minimum Variance portfolios share a common fundamental pattern and are consistently titled toward high profitability stocks that are less indebted than the market average.
Minimum variance portfolio is an attractive theoretical opportunity to have equity-like return with less market risk. However in practice such portfolios have complex design as minimum variance engine needs portfolio constraints to put the concept at work. Investors face today a broad offer of minimum variance methodologies, so how can one compare the different implementations and evaluate what constraints are really needed in minimum variance portfolio? We show in this paper how to read the constraint map and ensure that the necessary is made to make minimum variance engine work. We also list extra constraints that one encounters in existing minimum variance methodologies and discuss how they impact the ability of the strategy to reduce volatility and improve diversification.
We study the regime-dependent relationship between the performance of alternative beta strategies and the Fama-French factors. It is widely believed that the alphas generated by alternative beta strategies can be explained by their exposure to well-known pricing factors such as size and value. However, nothing is known about the dynamic of the relationship between the strategies and the factors in different market regimes. We estimate a four-regime Markov switching model on datasets including market portfolio, factor returns and returns of two alternative beta strategies - equal weight and minimum variance. Regime-switching regression of the strategies' returns conditional on four regimes shows that their factor exposures change significantly across regimes, notably in the case of the minimum variance strategy. This indicates that investment strategies adapt continuously to market conditions via periodic rebalancing and position their portfolios differently with respect to the factors when there is a regime change.
The rise of alternative beta investment strategies is a recent trend that positions itself on the top of another powerful trend: the growth of passive "beta" investing. Passive investing in market indices began almost 40 years ago and received a major support from the financial theory, as the market capitalization-weighted portfolio was claimed to be the most efficient one by the Sharpe-Lintner Capital Asset Pricing Model (CAPM) back in the 1960s. Both theory and empirical research have since then accumulated plenty of evidence that questions the validity of the CAPM and efficiency of the market portfolio. With the "one-for-all" market portfolio solution under attack, a family of new ideas on efficient equity investing, the alternative beta strategies, is proposed to investors. This range of investment ideas is very heterogeneous though its common denominator is the attempt to fix the inefficiencies discovered in the market capitalization weighted portfolios. In this paper we review the reasoning behind the efficiency of the market portfolio, and its aws. We then discuss the rationales behind the competing alternative beta investment approaches.
Minimum variance portfolio is constructed using an optimization procedure that takes as an input a covariance matrix. Inevitable estimation errors in the covariance lead to distortions of optimal allocation and should be addressed in the optimization. One solution is to use advanced estimation techniques that aim at reducing the error. We analyze here the impact of statistical covariance cleaning on the minimum variance portfolio. We build several minimum variance portfolios using different covariance estimators: two shrinkage estimators and two estimators based on Random Matrix theory, and compare volatility of these minimum variance portfolios with a portfolio constructed with a simple empirical covariance. Our results show that in presence of basic portfolio constraints, such as a long-only constraint, the effect of covariance cleaning on ex-post volatility is negligible. This result is in accordance with earlier research of Jagannathan, Ma  and Roncalli  who showed that portfolio constraints in their turn induce important transformations on covariance matrix. It turns out that an explicit cleaning of covariance matrix is dominated by the implicit cleaning induced by portfolio constraints. We also discuss a duality between the two-norm quadratic portfolio constraint and the James-Stein covariance shrinkage estimator and _nd and approximate relation between the degree of covariance shrinkage and the target for the two-norm constraint.
Measuring risk-adjusted returns in up and down markets
Minimum variance portfolios constructed via mathematical optimization allow to significantly reduce equity risk. Minimum variance optimization, though bearing a certain level of complexity, cannot be regarded anymore as a "black box" process. We will argue in this paper that properly designed minimum variance investment approach can be relevant for investors seeking risk-efficient passive equity allocation, that it can be made transparent to investors by giving them more disclosure and intuition on the resulting optimized allocation and the choices made in by the optimization procedure. We discuss here how the minimum variance engine works and how to structure the optimization procedure in order to keep the minimum variance portfolio diversified.
Equally weighted portfolios that allocate the same weight to every portfolio constituent emerged recently as an attractive alternative to the traditional market-capitalization weighted scheme. The equally-weighted versions of traditional equity indices tend to outperform their market-cap counterparts over the long run. The article examines the sources of this outperformance. We show that Equally Weighted portfolio on European stocks outperforms the traditional index because the portfolio is more exposed to mid and small capitalizations, and also due to homogeneous repartition of the weights inside the large cap sector. The rebalancing effect, however did not contribute positively to the portfolio performance.
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In the below table, you will find the latest rebalancing reports of the indices/strategy that Ossiam ETFs replicate.
The below reports detail the Brinson's performance attribution of the indices/strategies replicated by Ossiam ETF versus their benchmark over several periods of time.