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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.
We study the quadratic constraint, also known as the Herfindahl, used to improve the diversification in Minimum Variance portfolios. We argue that this measure, based on the Herfindahl and Hirschmann Index (HHI), provides the most suitable diversification tool for optimization frameworks that use covariance matrix, as it is free from estimation errors in the covariance estimation. This constraint indirectly limits the maximal weight per stock. We provide an explicit upper bound for maximal weight as a function of Herfindahl-Hirschmann target and of the dimension of investment universe. We also prove that small perturbations in Herfindahl-Hirschmann target have little effect on the optimal Minimum Variance solution. This continuity property also applies to the ex-ante volatility of optimal portfolios: small perturbations in Herfindahl-Hirschmann target yield small variations in ex-ante volatility. Finally, volatility reduction achieved by Minimum Variance portfolio with respect to the benchmark is directly dependent (and can be efficiently controlled) by the Herfindahl-Hirschmann constraint. We establish an explicit trade-off between the level of diversification given by Herfindahl-Hirschmann target and the expected volatility reduction.
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?
There are three ways to generate investment performance. The first and the most important one is by using asset allocation to shape future investment return. The other ways are market timing and improved execution. Portfolio rebalancing is a management operation that creates an opportunity to use the two latter sources of performance. It can be a clever form of market timing, selling high and buying low. Additionally, a well-designed rebalancing schedule can reduce trading costs. Every portfolio needs to be rebalanced, so why not make the most of it?
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.
Marked by two financial crises, the past decade was tough to equity investors, with many markets failing to deliver a return in excess of the risk-free rate. This disappointing performance raised doubts about the long-advocated efficiency of passive market portfolios. As a result, alternative, risk-efficient benchmarks are increasingly being proposed to investors. These benchmarks seek to enhance the risk-adjusted performance of broad equity portfolios by integrating risk management techniques into the portfolio construction process. Many major index providers have recently launched risk-efficient versions of their benchmark indices: examples are the iSTOXX Europe Minimum Variance index, which invests in a basket of stocks with reduced volatility by comparison with the market-capitalisation-weighted STOXX Europe 600 Index; or the family of Euro STOXX 50 Risk Control indices, which manage the leverage of the Euro STOXX 50 index in order to target a constant volatility level and protect the investor from the swings of the equity market.
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.
- Empirical research over the past 20 years has shown that market capitalisation portfolios are not efficient, and that equity investors are not properly rewarded for taking on extra risk.
- The minimum variance approach focuses on the analysis and management of portfolio risk without any assumptions about the expected returns. The methodology developed by Ossiam is intended to:
- benefit from asymmetric markets (bear and bull markets);
- mitigate drawdowns and volatility.
- Portfolio constituents are selected and weighted by a multi-stage procedure that includes filters (size, liquidity) and an optimization process. The portfolios of the Minimum Variance indices are adjusted at fixed dates according to their respective methodology (see table below).
Rebalancing reports - Minimum Variance indices
You will find here the latest KIIDs and prospectus as well as annual and semi-annual reports.
In this section you will find the latest monthly factsheets and the latest fund profiles.
Efficient indexing is the core of our business model. We offer investors exposure to smart beta through ETFs that replicate quantitative, algorithmic and rule-based indices. The ETF wrapper enables to do so at reduced costs for the investors, while offering increased transparency and liquidity.
The performance attribution studies reported in this presentation use the classical Brinson Sector attribution of excess performance versus the benchmark.
- allocation: capacity to allocate more to sectors that will outperform the market;
- stock picking: capacity to select the outperforming stocks inside the sector;
- interaction: a cross term in the decomposition, represents covariance between excess weights of sectors in the portfolio versus the benchmark and excess returns of the portfolio sectors versus the benchmark sectors; it calculates the capacity to concentrate more on the sectors/countries where the strategy makes better stock picking.
- total performance of the sector/country portfolio: performance of the sector as defined by its components in the strategy/benchmark with a leverage of 100% in Q2;
- sector/country contribution to index performance: sector contribution to the total performance of the strategy/benchmark in Q2 (taking into consideration the evolution of weights over the period).
Ossiam's Voting Policy is available at the bottom of this page. This policy describes the conditions under which Ossiam plans to exercise the voting rights granted by the holding of the securities in the funds it manages. The 2017 Annual Report on Voting Rights is also available on this page.
Ossiam has designed and implemented a remuneration policy that is consistent with, and promotes, sound and effective risk management, based on its business model that by its nature does not encourage excessive risk taking which would be inconsistent with the risk profile of the UCITS managed by Ossiam. If and where Ossiam identifies those of its staff members whose professional activity has a material impact on the risk profiles of the Funds, it ensures that these staff members comply with the remuneration policy. The remuneration policy integrates governance, a pay structure that is balanced between fixed and variable components, and risk and long-term performance alignment rules. These alignment rules are designed to be consistent with the interests of Ossiam, the UCITS it manages and the investors, with respect to such considerations as business strategy, objectives, values and interests, and include measures to avoid conflicts of interest. Ossiam ensures that the calculation of a part of the performance-based remuneration may be differed over a three year period and subject to the compliance with the risk taking policy of the company. The details of the current remuneration policy is available below.