via transparent rule-based indexes (calculated by independent index providers)
and an easy to trade investment vehicle such as ETFs, or any other type of Collective Investment Schemes.
- Research publications
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- Regulatory documents
- UK reporting status
- Notices to shareholders
- Legal mention
- Rebalancing reports
- Performance attribution reports
Find here all the publications from our research team
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.
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.
In this section you will find the latest monthly factsheets and the latest fund profiles.
Find here the latest KIIDs and prospectus as well as annual and semi-annual reports.
Your shareholding in the funds below constitutes an interest in an offshore fund from a United Kingdom (“UK”) taxation perspective, with each share Class treated as a separate ‘offshore fund’.
In this respect, investors will be required to include on their tax return any distributions received during the year and their proportionate share of reportable income in excess of any distributions received.
Reportable income by share class for Ossiam funds are available in the "investor reports" below.
For more information on the UK reporting status please contact us.
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.
Personal data protection policy
The policy defines the terms and conditions for the processing of personal data by Ossiam.
In case of question/inquiry, you can contact Ossiam Data Protection Officer (DPO) (email@example.com)
Responsible investment and stewardship policy
Ossiam is committed to being a responsible investor and considering environmental, social and governance (ESG) matters within its investment process. Our responsible investment approach encompasses the beliefs, standards, procedures and activities through which we fulfil our responsibility to our clients as an integral part of managing their assets. Our investment approach and the business processes which put it into operation prioritise high standards of stewardship, clear accountability, striving for best practice and compliance with regulatory standards.
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.
Alpha: A measure of performance on a risk-adjusted basis. Percentage by which the portfolio or smart beta index outperforms (adjusted for risk) the comparable market capitalization weighted index from which the stocks are picked.
Asset allocation: The breakdown of the investments of a portfolio among various asset classes (such as equities, bonds, money market instruments, etc.), built in order to optimize the risk/return ratio of the investor's portfolio, in light of his or her financial situation, investment objective, investment horizon and appetite for risk.
Benchmark: A reference used as a comparison to determine the returns and risk of a portfolio. The benchmark may be an index or a combination of several reference indices.
Beta: The Beta of an investment indicates whether it tends to evolve in the same direction as the market, and in what proportion. Thus, the beta coefficient measures the sensitivity of a portfolio or share to market movements or an index.
Bid/Ask: Bid - Purchase price given or displayed by a market maker on an exchange (or over-the-counter), which corresponds to the sale price for the end investor. Ask - Sale price given or displayed by a market maker on an exchange (or over-the-counter), which corresponds to the purchase price for the end investor. There is usually a difference between the bid price and the ask price for an asset on the order book. This difference is called the spread and varies from one ETF to another. It tends to increase when the volumes being traded are low. One of its drivers is the liquidity of the underlying.
CAPE: CAPE - Cyclically Adjusted Price Earnings. The CAPE ratio is the PER adjusted for cyclical factors. It relates the share price to corporate earnings over the past ten years. Compared to the standard PER, it eliminates short-term fluctuations in earnings, which can distort the picture. Relative CAPE - CAPE standardized to make individual sectors comparable. It is defined as the CAPE® ratio divided by its median in past years. A Relative CAPE® ratio of 1 shows that the current valuation is in line with the long-term average, while figures lower (resp. higher) than 1 point to the sector’s possible undervaluation (resp. overvaluation).
Correlation: Measure for the dependence of the performance of two securities, indices or asset classes on each other. The correlation can be between -1 and 1, whereby 1 means they perform exactly in parallel, -1 that they perform in exactly opposite directions. The closer to 0 the correlation, the more independent the two performances are.
CPPI: Constance Proportion Portfolio Insurance, a method of portfolio insurance in which the investor sets a floor on the currency value of his or her portfolio, then structures asset allocation around that decision. The two asset classes used in CPPI are a risky asset (usually equities or mutual funds), and a riskless asset of either cash, equivalents or Treasury bonds. The percentage allocated to each depends on the "cushion" value, defined as (current portfolio value – floor value), and a multiplier coefficient, where a higher number denotes a more aggressive strategy.
Diversification: Portfolio constrction reducing the exposure to the risk of capital loss by investing in uncorrelated assets at the same time. Diversification therefore consists in distributing an investment among different business sectors, asset classes, geographic regions, etc.
Diversification indices: Indices that focus on broader diversification by an alternative weighting of the securities in the index, for example by weighting all the securities equally.
Gain: Gain generated on the difference between the purchase price of an asset and its subsequent sale price (exclusive of charges and fees paid in connection with the purchase and sale of an asset).
ESG: ESG integration consists of integrating Environmental, Social and Governance (ESG) criteria into the management of investments.
ETF: Exchange Traded Fund that tracks an index. ETFs passively mirror an index, without any active management being involved. Units of ETFs are traded on the exchanges like equities.
Fundamental indices: Indices weighted by fundamental criteria such as dividend yield, price/earnings ratio or other value criteria.
Hedging: Strategy whose objective is to totally or partially offset the risk associated with an investment by making a second transaction that combines long and/or short positions in transferable securities, precisely in order to reduce the risk of the first position. For example, the risk of a long-term position in equities may be offset by the purchase of put options. Thus, investors may limit the impact of a decline in the prices in their portfolio.
Index: Instrument to measure capital markets’ performance. Indices exist for various asset classes (shares, bonds, commodities), countries and regions, and sectors. An index brings together the securities representative of the market to be tracked in line with fixed rules.
Index based on market capitalization: Traditional stock indices where weightings of the individual shares are proportional to their market capitalizations, meaning the share prices multiplied by the number of shares issued, adjusted for free-float. Most of the main stock market indices are compiled using this method.
ISIN (International Securities Identification Number): This unique international identifier is used to distinguish financial instruments, such as shares, bonds, funds, etc. The ISIN code is a unique international alphanumeric code of 12 characters (of which the first two letters identify the country of issue of the security, e.g. FR for France). Shares always keep the same ISIN codes, even if they are traded in different currencies or listed on different markets. Each ETF has its own ISIN code.
KIID (Key Investor Information Document): The KIID is a regulatory document, standardized on a European level, providing investors with key information on a fund in terms of its objectives, risks, performance and costs, so that, when making investment decisions, investors are in a position to understand the nature of the fund and the risks associated with it. This document must be provided to the investor prior to any subscription. Introduced by the European Directive 2009/65/EC of 13 July 2009, this document replaces the simplified prospectus.
Loss: Loss resulting from the difference between the original acquisition price of an asset and its subsequent sale price.
Market Maker or Liquidity provider: A financial institution whose role is to facilitate the trading of financial instruments, including ETFs. During trading hours, the market maker provides quotations for the product on the relevant stock exchange and places bid/offer orders continuously, so that investors can trade their financial instruments (such as ETFs) at any time.
Maximum Drawdown: The maximum drawdown, or "maximum successive loss", is an indicator of the risk of a portfolio chosen on the basis of a certain strategy. The maximum drawdown measures the largest decrease in the value of a portfolio. Precisely, it corresponds to the historical maximum loss incurred by an investor who bought the highest and resold at the lowest, for a fixed period.
NAV / NAV per Share: The Net Asset Value corresponds to the total value of the fund assets. The NAV per Share is the NAV divided by the number of shares in the fund.
OTC – Over-the-counter: A trade which takes place off the exchange directly between two identified parties (counterparties). On an OTC market, market makers seek to provide liquidity for the various assets for which they propose bid and offer prices.
PER (Price/Earnings Ratio) : The PER divides the share price of a company to its earnings per share. The PER serves as a key indicator for how shares are valued. The lower the PER, the more favorably the share is priced on the stock market.
Physical replication: Physical replication consists in holding the securities comprising the replicated index. This replication may be “perfect”, in this case the fund holds all the securities of the index and in the same proportions as in the index. It may also be done through "sampling", in this case the fund holds only part of the securities of the index selected in order to offer performance as close as possible to the performance of the index.
Price return: A “Price Return” index tracks the evolution of prices of its component equities or bonds, without including dividends that may be paid by such equities or coupons in the case of bonds.
Risk-based indices: Indices that use risk measures, such as volatility, to allocate the stocks. In general, risk-based indices seek to systematically reduce volatility compared to a classic index. Examples of risk based indices are minimum variance or risk parity indices.
Share class: Some funds and ETFs have multiple share classes. These may differ by the currency in which they are denominated, the distribution or capitalization of dividends, their costs or minimum tradeable size. Each share class has a unique specific ISIN code.
Sharpe ratio: Indicator for the portfolio’s profitability in relation to the risk taken. Developed by US economist William F. Sharpe, the figure presents the return in excess of a risk-free interest rate divided by the portfolio’s volatility.
Smart beta: Rule-based investment strategies that use an alternative weighting for the securities covered by an index to deliver better risk-adjusted results than the underlying traditional index.
Spread: The difference between the Bid and Ask price of an asset. The spread generally varies depending on supply and demand for a particular asset and on the volume traded (liquidity).
Stock market price: The price of an asset as determined by levels of supply and demand on the stock market. Please note that the stock market price of an ETF may differ from its net asset value (NAV). It will, however, fall within a range based on the indicative Net Asset Value.
Swap (forward swap agreement): An OTC swap agreement between two parties in relation to an exchange to be executed at a future date and at a pre-determined or determinable price. The expiry dates, delivery dates, quantities and contractual terms and conditions for each agreement are standardized. ETFs using synthetic replication methods use swaps: they swap with a counterparty the performance of the assets they hold for the performance of the benchmark index. The mark-to-market value of a swap is equivalent to the amount owed to the ETF by the counterparty (if the value is positive) or owed to the counterparty by the ETF (if the value is negative). In the absence of collateral, the mark-to-market value of the swap represents the counterparty risk. For ETFs, the level of exposure to the swap is subject to regulations, and limited to 10% of the assets per counterparty in the case of UCITS ETFs.
Swap counterparty: Entity with which the Total Return Swap is entered into.
Synthetic replication: For ETFs managed in synthetic replication, the replication of the index is generated by a Total Return Swap, i.e., a swap under which an investment bank (the counterparty) exchanges the performance of the index for another performance. In synthetic replication, there are two types of structures: - Unfunded structures, in which the fund uses its liquidity (generated for subscriptions in the fund) to purchase a basket of securities that it owns and whose performance is exchanged for the performance of the index in accordance with the provisions of the Total Return Swap; - Funded structures in which the fund transfers its cash to the counterparty, in exchange for the final value of the index at swap maturity, in accordance with the provisions of the Total Return Swap.
TER: The TER (Total Expense Ratio) is the sum of annual total management and operating fees billed to a fund relative to its net assets. For OSSIAM ETF funds (the “Funds”), the TER corresponds to the ongoing fees mentioned in the KIID. The ongoing fees represent the expenses deducted from the fund during the course of a year.
Total Return: In the case of “Total Return” indices, dividends paid by the equities (or coupons in the case of bonds) comprising the index are reinvested into the index, unlike a “Price Return” index. In general, an expression referring to the total profitability of an investment that may be expected, i.e., the sum of the capital gain related to selling the securities, and the dividends (or coupons) collected.
Tracking error: A measure of how closely an ETF follows the index to which it is benchmarked. The tracking error shows how the ETF mirrors the index. The lower the figure, the more exact the tracking.
UCITS (Undertakings for Collective Investment in Transferable Securities): European directive aimed at further harmonizing the European financial markets. It proposes a harmonized regulatory framework for European funds and particularly with respect to:
- rules relating to the organization of management companies managing funds,
- eligibility and composition of assets, risk diversification
- authorization for marketing within EU territory, etc.
Various directives have been successively implemented in the European Union.
Value trap: A well known challenge when investing in a value strategy. It refers to the risk taken by investors who opt for ostensibly favorably priced equities or sectors that then take a very long time to bounce back, if they do so at all.
Volatility: A measure of the magnitude of the fluctuations for a security or index across a certain period of time, measured as the standard deviation of returns. It serves as a measure of an investment’s risk.