era.jpg

Authors

Lyxor_PortraitEN_Berthon_01.jpg

ferreiravrai.jpg

asseraf.jpg

04 Jul 2015 -

A New Era For Hedge Funds?

Foreword Since the financial crisis, excluding the Euro crisis period, hedge funds have lagged traditional assets until last year. This stands in contrast with the outstanding hedge funds’ track record delivered over recent decades. Criticism against the lack of hedge funds outperformance since the financial crisis climaxed in 2014.... [+]

Since the financial crisis, excluding the Euro crisis period, hedge funds have lagged traditional assets until last year. This stands in contrast with the outstanding hedge funds’ track record delivered over recent decades. Criticism against the lack of hedge funds outperformance since the financial crisis climaxed in 2014.

The ability of hedge funds to generate sufficient alpha, while adequately capturing the market beta was then put to question.

In this 12th edition of the White Paper we aim to respond to these questions and to estimate a sustainable regime of performance achievable by hedge funds over the coming years. In particular:

  • We review the main causes for the recent performance lag, which include unprecedented accommodative monetary policies and tighter regulations. 
  • They profoundly altered the Volatility / Correlation / Dispersion regimes, which are key drivers of hedge fund returns. 
  • We argue that evidences of a sustainable turn in these regimes are multiplying, which bode well for the alpha generation environment. A new era might be gradually shaping up for hedge funds. 
  • Besides, we put to test hedge funds’ structural market exposures under 3 long-term macro- economic scenarios, with a view to estimate a sustainable beta performance contribution.
    It is reasonable, in our view, to expect hedge funds to deliver an annual excess returns in the 5-6% range, with low volatility.
    We hope that this 12th issue addresses some of the questions our readers may have, while providing credible assumptions to estimate the performance of hedge funds going forward. 


Jean-Marc Stenger
CIO for Alternative Investments.

cover.png

Authors

LYXOR_PortraitEN_Cazalet.jpg

ban_01.jpg

03 Mar 2014 -

Hedge Funds In Strategic Asset Allocation

Foreword Total assets under management for the hedge fund industry reached an all-time high of USD 2.6 trillion in 2013. With lower expectations for traditional assets, many institutional investors, including pension funds and corporate, are lending increasing allocation to alternative assets to secure both performance and resilience for their portfolios.... [+]

Total assets under management for the hedge fund industry reached an all-time high of USD 2.6 trillion in 2013. With lower expectations for traditional assets, many institutional investors, including pension funds and corporate, are lending increasing allocation to alternative assets to secure both performance and resilience for their portfolios. As a result, hedge funds are now growing faster than any other type of asset. They are expected to reach USD 3.3 trillion by 2015 with a compound annual growth rates of around 15%. 

 

This 11th white paper looks at hedge funds from a new perspective, in the context of Strategic Asset Allocation (SAA). We see the growth of the assets managed the industry as an implicit consequence of the different approach taken with regard to hedge fund investments.

 

Numerous studies using pre-2008 data have shown the benefits of adding hedge funds to SAA. Hedge funds were previously considered to be a stand-alone asset which should

account for a small percentage of overall portfolios. Now, in the aftermath of the financial crisis, a new paradigm has appeared: hedge funds are becoming mature investment styles exhibiting significant and persistent performance divergence both with each other and when compared to traditional assets.

 

As such, hedge fund strategies should be disaggregated into sensible sub-categories which should naturally migrate from a stand-alone asset into the broader equity and bond

asset-mix. In this context, we propose a reassessment of the relationship between hedge fund strategies and traditional markets to introduce an updated SAA framework with hedge funds.

 

To highlight the above points, this paper addresses the following structural questions:

• What are the stylized facts of hedge fund performance in the post-2008 environment?

• How should we classify hedge funds in order to better reflect their true characteristics?

• What is the best way to integrate the new classification process into a SAA approach?

 

We hope you will find this article both interesting and useful in practice.

 

Jean-Marc Stenger

Chief Investment Officer for Alternative Investments

b1963f94c5.jpg

Authors

LYXOR_PortraitEN_bruder.jpg

06 Jun 2013 -

Regularization of Portfolio Allocation

Foreword The practice of active management consists in a permanent reassessment of the expected returns and risks of the set of opportunities available in the market. From this perspective, it seems that the Markowitz ‘theory’ is the perfect tool to help investors handling this trade-off. All things being equal, doesn’t Markowitz’s result qualitatively... [+]

The practice of active management consists in a permanent reassessment of the expected returns and risks of the set of opportunities available in the market. From this perspective, it seems that the Markowitz ‘theory’ is the perfect tool to help investors handling this trade-off. All things being equal, doesn’t Markowitz’s result qualitatively formalize the obvious?

 

1. The higher the reward (Sharpe ratio), the larger the amount invested.

2. The higher the risk (volatility), the lower the amount invested.

 

However, the quantitative practice of Markowitz is shown to be more complicated than expected. Not only are expected returns and Sharpe ratios difficult to estimate on a regular basis, but also the dynamics of the risk structure (the covariance matrix) which generates large instabilities in final trade recommendations. The variations of outputs can sometimes be very counter-intuitive and necessitate significant ‘cooking’ to make it up. As a result, many investors eventually give up using Markowitz in practice. They consider it a nice normative approach but more suited for textbooks than for real life investments. 

 

So, do portfolio managers have to resign themselves not to use quantitative measures, but instead to confine them to risk management? Is there any way to construct an efficient and practical fund management tool based on the Markowitz approach? At Lyxor, we are of the view that Markowitz, yet too simplistic a model, remains an excellent starting point to design efficient, computer-assisted, investment decision tools. It just has to be "stabilized" to account for the estimation noise surrounding the parameters. It also has to be adapted to commonly agreed practices drawn either from regulation or from consensus. In search for a comparison, using Markowitz in real life is like using binoculars in a car on a bumpy road. It is not that it stops working; it is just much too sensitive for this environment. It must be stabilized.

 

It is the aim of this 10th white paper to provide investors with a survey of different regularization techniques that can make Markowitz suitable for real-life issues. This look

under the hood can be somewhat technical and dry, but it gives a fair view on the recent development on the matter, whose importance shall not be underestimated. We hope you will find this journey into regularization techniques both interesting and useful in practice.

 

Nicolas Gaussel

Chief Investment Officer

TEH10351-1999CL03.jpg

Authors

LYXOR_PortraitEN_bruder.jpg

02 Sep 2012 -

How to Design Target-Date Funds?

Foreword Target-date funds are usually composed of several funds representing different investment styles or asset classes. As their name suggests, they have a target date for retirement, such as 2020 or 2040. The investment firm running these funds makes the asset allocation decisions on behalf of investors based on the target date. Vanguard founder John... [+]

Target-date funds are usually composed of several funds representing different investment styles or asset classes. As their name suggests, they have a target date for retirement, such as 2020 or 2040. The investment firm running these funds makes the asset allocation decisions on behalf of investors based on the target date. Vanguard founder John C. Bogle, a zealous supporter of simplicity in investment matters, used to advocate the old rule of thumb that you should hold your age in bonds and the rest in equities. 

This rule seems to be in line with what many investors expect. However, hardly scientific, it appears to be inconsistent with financial theory. The literature on optimal portfolios suggests allocating a percentage between equity and bonds, mainly determined by the equity premium and investors’ risk aversion and to a lesser extent by the investment horizon. As a result the equity/bond asset mix should remain fairly stable during the investor’s lifetime. 
The recent growth in AUM for target-date funds, as well as their increasing use in defined-contribution plans, makes it critical to work on this asset allocation puzzle both in theory and in practice. This is the aim of this 9th White Paper. 

To reconcile the two approaches, we show that investors should indeed invest a stable proportion of their ‘human capital’ in equities, as prescribed by the theory. So-called human capital is defined as the current capital plus the present value of future contributions. At the age of retirement, human capital and current capital coincide. However, the further from retirement, the more human capital outweights current capital. Hence, the allocation in equities, owing to its stability in terms of human capital, appears to grow in terms of current capital as the time to retirement increases, which is qualitatively consistent with John C. Bogle’s rule of thumb. 

This 9th White Paper demonstrates the stylised analysis described above in a fairly precise framework which provides a great deal of insight into the effective design of target-date funds. We hope you will it find both interesting and useful in practice.

Nicolas Gaussel
Chief Investment Officer
Lyxor Asset Management

08-Page-24.jpg

Authors

LYXOR_PortraitEN_bruder.jpg

02 Dec 2011 -

Trend Filtering Methods For Momentum Strategies

Foreword The widespread endeavor to “identify” trends in market prices has given rise to a significant amount of literature. Elliott Wave Principles, Dow Theory, Business cycles, among many others, are common examples of attempts to better understand the nature of market prices trends.   ... [+]

The widespread endeavor to “identify” trends in market prices has given rise to a significant amount of literature. Elliott Wave Principles, Dow Theory, Business cycles, among many others, are common examples of attempts to better understand the nature of market prices trends.   


Unfortunately this literature often proves frustrating. In their attempt to discover new rules, many authors eventually lack precision and forget to apply basic research methodology. Results are indeed often presented without any reference neither to necessary hypotheses nor to confidence intervals. As a result, it is difficult for investors to find there firm guidance and to differentiate phonies from the real McCoy.   

This said, attempts to differentiate meaningful information from exogenous noise lie at the core of modern Statistics and Time Series Analysis. Time Series Analysis follows similar goals as the above mentioned approaches but in a manner which can be tested. Today more than ever, modern computing capacities can allow anybody to implement quite powerful tools and to independently tackle trend estimation issues. The primary aim of this 8th White Paper is to act as a comprehensive and simple handbook to the most widespread trend measurement techniques.   

Even equipped with refined measurement tools, investors have still to remain wary about their representation of trends. Trends are sometimes thought about as some hidden force pushing markets up or down. In this deterministic view, trends should persist. However, random walks also generate trends! Five reds drawn in a row from a non biased roulette wheel do not give any clue about the next drawn color. It is just a past trend with nothing to do with any underlying structure but a mere succession of independent events. And the bottom line is that none of those two hypotheses can be confirmed or dismissed with certainty.   

As a consequence, overfitting issues constitute one of the most serious pitfalls in applying trend filtering techniques in finance. Designing effective calibration procedures reveals to be as important as the theoretical knowledge of trend measurement theories. The practical use of trend extraction techniques for investment purposes constitutes the other topic addressed in this 8th White Paper.

Nicolas Gaussel
Global Head of Quantitative Asset Management
Lyxor Asset Management

07-Page-27.jpg

Authors

LYXOR_PortraitEN_bruder.jpg

02 Jun 2011 -

Risk-Return Analysis of Dynamic Investment Strategies

Foreword The industry of investment funds has dramatically changed over the past ten years, and we observe today a convergence between hedge funds and traditional asset management. For example, institutional as well as retail investors may now have access more easily to absolute return strategies in a mutual fund format. This convergence has been recently... [+]

The industry of investment funds has dramatically changed over the past ten years, and we observe today a convergence between hedge funds and traditional asset management. For example, institutional as well as retail investors may now have access more easily to absolute return strategies in a mutual fund format. This convergence has been recently accelerated with the emergence of newcits and the increasing number of regulated hedge funds. Therefore, the decision-making process of investment is today more complex with the strong development of these dynamic investment strategies and the large number of underlyings and assets. Managing the exposure to risky assets is the main difference between these investment styles and the traditional long-only strategies. This difference is however conceptually huge and is not always understood by investors and fund managers themselves. 


The traditional way to analyze and evaluate a strategy is to use risk-adjusted performance measurement tools like the Sharpe ratio (or the information ratio) and the Jensen’s alpha. These financial models have been developed to compare long-only strategies, but they are not well adapted to dynamic trading strategies. Indeed, dynamic strategies exhibit non-normal returns and nonlinear exposures to risk factors. In the nineties, practitioners and academics developed alternative models to take into account these properties. And some extensions of the Sharpe ratio like Sortino, Kappa and Omega ratios have become today very popular to analyze the performance of hedge funds returns. Another way to understand the risk-return profile of dynamic strategies has been proposed by Fung and Hsieh (1997) by incorporating non-linear risk factors in a Sharpe style analysis. These different measures define the empirical approach in the sense that they are computed on an ex-post basis but are not very adapted for an ex-ante analysis. 

All these models are relevant, however they provide a partial answer to understand the true nature of a dynamic strategy. Let us consider for example a long exposure on a call option. Since the seminal work of Black and Scholes (1973), we know that this investment profile is equivalent to a delta-hedging strategy. Therefore, a long position on a call option is a trend-following strategy with a dynamic exposure to the underlying risky asset. Computing a risk-adjusted performance or performing a style regression are certainly not natural tools to analyse this dynamic investment strategy. A better way to understand the risk and return of such strategy is to use the theory of options. In this case, the performance of the strategy is analyzed by investigating both the payoff function and the premium of the option. And this last one is split into an intrinsic value component and a time value component. Moreover, one generally computes the sensitivity of the premium to different parameters like volatility, time decay or the price of the underlying asset. This analytical approach permits to apprehend the option strategy in a more satisfactory way than the empirical approach, which consists in analyzing the ex-post risk-return profile of the option strategy, by computing some statistics on a real-life, investment or on some backtests. 

For example, with the analytical approach, we may show that the trend of the underlying asset only concerns the payoff function and has no effect on the option premium. Such property could not be derived from the empirical approach. Another interesting example of dynamic trading strategies is the constant proportion portfolio insurance (CPPI) developed by Hayne Leland and Mark Rubinstein in 1976. The large literature on this subject is mainly related to the analytical approach and the analysis of CPPI strategies is closer to the theory of options than the models developed to compute the performance of traditional mutual funds. However, the technique of CPPI is certainly one of the most famous dynamic strategy in asset management. 

Given the previous remarks, we may think that the analytical method may be extended to a large class of dynamic trading strategies, and not only reserved to options and CPPI. This seventh white paper explores this approach. In this white paper, we develop a financial model to better understand the risk-return profile of several dynamic investment strategies, like stop-loss, start-gain, doubling, mean-reversion or trend-following strategies. We show that dynamic trading strategies may be decomposed into an option profile and a trading impact. In some sense, the option profile may be viewed as the payoff function of the strategy whereas the trading impact may represent the premium to buy such strategy. In this framework, implementing a trading strategy generally implies a positive cost, which has to be paid as explained by Jacobs (2000): 

“Momentum traders buy stock (often on margin) as prices rise and sell as prices fall. In essence, they are trying to obtain the benefits of a call option – upside participation with limited risk on the downside – without any payment of an option premium. The strategy appears to offer a chance of huge gains with little risk and minimal cost, but its real risks and costs become known only when it’s too late.” Using this framework, we are also able to answer some interesting questions, which are not addresses in the empirical approach. Here are some of them. In which cases the pro¬portion of winning bets (or hit ratio) is a pertinent measure of the efficiency of a dynamic strategy? Which dynamic strategies like or don’t like volatility? What is the best and the worst configuration for a given dynamic strategy? What is the impact of the length of a moving average in a trend-following strategy? What is the theoretical distribution of the strategy returns? Why long-term CTA differs from short-term CTA? What are the risks of a mean-reverting strategy? By answering to all these questions, we provide some insights to understand why and when some strategies perform or not and what are the good metrics to evaluate their performance. We hope that you will find the results of this paper interesting as well as useful in practice.

Thierry Roncalli
Head of Research and Development 
Lyxor Asset Management

06-Page-4.jpg

Authors

LYXOR_PortraitEN_Eychenne.jpg

02 Mar 2011 -

Strategic Asset Allocation

Foreword The primary goal of a Strategic Asset Allocation (SAA) is to create an asset mix which will provide an optimal balance between expected risk and return for a long-term investment horizon. SAA is often thought as a reference portfolio which will be tactically adjusted based on short-term market forecasts, following a process often called Tactical... [+]

The primary goal of a Strategic Asset Allocation (SAA) is to create an asset mix which will provide an optimal balance between expected risk and return for a long-term investment horizon. SAA is often thought as a reference portfolio which will be tactically adjusted based on short-term market forecasts, following a process often called Tactical Asset Allocation (TAA). Many empirical studies support that SAA is the most important determinant of the total return and risk of a broadly diversified portfolio. As a matter of fact, long-term allocation needs long-term assumptions on assets risk/return characteristics as a key input. With 30 years in mind as a typical horizon, the issue of determining expected returns, volatilities and correlations for equity, bond, commodity and alternative asset classes is a complex task, faced by most institutions. Two main routes can be explored to address this issue. 


The first one basically consists in stating that past history will repeat itself similarly and historical figures can serve as a reliable guide for the future. This method, sometimes referred as unconditional, determines expected returns based on historical returns, disregarding any world shocks or structural economic changes that could arise. As a result, this approach appears unsatisfactory and not adapted to the SAA problem. 

A second route consists in relating long-run financial expected returns to long-run macroeconomic scenarios. The assumption here is that market prices do not differ, on the long term, from their so called fundamental value which is determined by the returns of physical assets. Hence, this fair value methodology consists in first, establishing a link between financial prices and economic fundamentals and second, determining the long-run value of those fundamentals. 

This sixth white paper explores the second route. First, the long-run short rate is defined based upon macro-economic quantities such as the long-run inflation and real potential output growth. Using this long-run short rate as a reference numeraire, risk-premium of bonds, equities and some alternative asset classes are then successively derived. Eventually, using a typical mean-variance framework, numerical results are obtained. 

Overall, the approach described herein provides an original line of thought to address allocation issues in a consistent set-up. The traditional segmentation between SAA and TAA, which could appear artificial, finds a clear justification. In the SAA step, allocation is built to adapt to the expected long-term stationary state of the economy. In turn, the TAA step allows for local fluctuations (business cycle) around this steady-state to be taken into account. We hope you will find this article both interesting and useful in practice.

Nicolas Gaussel
Publishing Director 
PhD, Global Head of Quantitative Asset Management

Page-17.jpg

Authors

LYXOR_PortraitEN_bruder.jpg

sergedarolles.png

Client only

02 Jan 2011 -

Portfolio Allocation of Hedge Funds

Foreword This fifth Lyxor White Paper tackles the issue of how Quantitative Methods can help in building a portfolio of Hedge Funds. To address this question one has to bear in mind that most of the so-called 'Portfolio Theory' has been developed under 'Perfect Markets' assumptions, with large cap equities, government bonds and FX markets in mind. 'Perfect... [+]

This fifth Lyxor White Paper tackles the issue of how Quantitative Methods can help in building a portfolio of Hedge Funds. To address this question one has to bear in mind that most of the so-called 'Portfolio Theory' has been developed under 'Perfect Markets' assumptions, with large cap equities, government bonds and FX markets in mind. 'Perfect Markets' share three essential characteristics:


1. Any relevant information when disclosed to somebody has to be disclosed to everybody; 

2. Liquidity is high, market impact is limited and transaction costs are negligible; 

3. Distributions of returns are close to be normal. 

When it comes to hedge funds, these assumptions might hardly be satisfied. Hence, 'Portfolio Theory' is to be used with care. 

Information issues are usually addressed by setting up tight due-diligence procedures. However history teaches that, in practice, putting in place and maintaining the necessary level of rigor in those processes is no easy task. Another possibility consists in accessing hedge funds through so-called managed account platforms. Such platforms provide simple yet efficient solutions to ensure that most due diligence rules are permanently enforced. Investments are made within a transparent fund envelope, following predefined investment guidelines which are enforced independently of the hedge fund manager. On top of that, some of these platforms help in standardizing and improving hedge fund liquidity. Putting aside the debate on the tracking error between hedge fund strategies and their platform equivalent, they provide a very satisfactory way to create an investment universe where both information and liquidity issues are controlled rigorously. 

However, even managed under such 'secured' format, the distribution of hedge fund returns can be far from Gaussian. Running dynamic strategies, hedge funds can indeed generate option-like payoffs and hence asymmetric and fat tailed returns. Traditional Mean-Variance approaches are then doomed to be inadequate. Hence, one has to look for other approaches which, in turn, open the door to new unexpected pitfalls, some of them being developed in this White Paper. As an example, trying to account uncritically for skewness and kurtosis with monthly data, can lead to situations where the number of parameters to be estimated is much larger than the number of available data... 

This White Paper has been written with a view to help investors understand, compare and find their way among the different available quantitative techniques. Our results suggest that even if those methods can provide useful insights, each of them only brings partial answers. As a consequence, we claim that hedge fund investment still cannot be considered as a commoditized job. It remains an expert field in which delegation and incentives structures have to be carefully designed. We hope you will find this study both interesting and useful in practice.

Nicolas Gaussel
Publishing Director 
PhD, Global Head of Quantitative Asset Management

04-Page-1.jpg

Authors

LYXOR_PortraitEN_Eychenne.jpg

sergedarolles.png

02 Sep 2010 -

Time Varying Risk Premiums & Business Cycles: A Survey

Foreword Anybody confronted with the problem of investing, whether its own money or the money of its organization, starts by thinking about which evidences he/she can elaborate upon to define an efficient investment policy. And here the difficulties begin...... [+]

Anybody confronted with the problem of investing, whether its own money or the money of its organization, starts by thinking about which evidences he/she can elaborate upon to define an efficient investment policy. And here the difficulties begin...



Defying widespread perception that capital markets are a place for short-term good deals, many empirical studies illustrate how active variations around a predefined investment policy generally destroy value. Both market timing overlays and security selection layers appear vain, whether one considers the performance of mutual funds or institutional portfolios. As a consequence, the Efficient Market Hypothesis (EMH) seems to be a fairly reasonable starting point to address investment issues. A financial equivalent to democracy in politics, EMH states that prices reflect the collective wisdom of all market participants and can never be wrong. 

As a consequence, phenomena such as temporary undervaluation, cheapness, or market bubble simply cannot exist. The expected return of a security or a market does not depend on its price. Since investors are risk averse, this expected return has to be higher than the government bond rate, the difference between the two being called the risk premium. Eventually, investment policy should be designed to allow investors to benefit from those long-term risk premiums in an optimal manner with respect to their specificities. Along those lines, some years ago, many question the relevance of endeavoring Tactical Asset Allocation programs and recommended sticking to pure long-term approaches. 

However, recent market behavior challenged seriously both the existence and the stability of the risk premium paid by equity markets. The Eurostoxx topped its maximum value more than 10 years ago, in March 2000 and is valued to date less than 50% of it; the Topix reached its maximum 21 years ago, in December 1989, before losing more than 70% to date. As far as the S&P 500 is concerned, it reached a maximum 10 years ago in March 2000, and briefly rose above it (38 days) in 2007 before losing an average 25% to date. When it comes to the one-year observed volatility of those major equity markets it ranges typically between 10% and 30% with a maximum around 40/45% attained in October 2008. 

Those figures draw two major consequences on investment. First, developed equity markets paid no equity premium in the past ten years, thus questioning the very existence of this premium and the relevancy of long-term investment policies based on it. Second, even if one still believes in equity premium, the empirical uncertainty surrounding the inputs of such long-term policy makes it extremely difficult to define — and even more to freeze — for the next couple of years. Hence, somewhat similar to a vessel in a tempest, it seems that in practice, investors should integrate this absence of evidence and put in place a strategy with less long-term backbone and more tactical monitoring. 

Different ways can then be explored to build robust Tactical Asset Allocation programs in such an environment. In March, we studied the risk budgeting route which is a way to build portfolios with limited views on future returns. We might also elaborate on well-known non-fundamental market characteristics such as the Momentum effect or on the use of options to structure intermediate investment profiles but we leave this for further publication. In this White Paper, we choose to focus on the equity premium itself, its possible variations in time and its determinants. We found that, given their practical importance, it was the right time for an updated survey on those issues. We hope you will find this study both interesting and useful in practice.

Nicolas Gaussel
Publishing Director 
PhD, Global Head of Quantitative Asset Management

03-Page-7.jpg

Authors

LYXOR_PortraitEN_Hereil.jpg

moussavirecadre.jpg

02 Jun 2010 -

Mutual Fund Ratings

Foreword The recent crisis has deeply damaged investors' confidence in the asset management industry. It has re-emphasized that, even though asset managers sometimes call investors their 'clients', asset management is not about selling goods but about receiving delegation or mandate. The central question is therefore a question of trust. More specifically,... [+]

The recent crisis has deeply damaged investors' confidence in the asset management industry. It has re-emphasized that, even though asset managers sometimes call investors their 'clients', asset management is not about selling goods but about receiving delegation or mandate. The central question is therefore a question of trust. More specifically, investors face two fundamental questions. First, 'will the asset manager act in my best interest?' This question is at the heart of any fiduciary relationship and falls under the so-called 'moral hazard' category. It is mostly addressed by the imperative rule of avoiding conflicts of interests and by the design of suited contracts to align each party interest. Second, 'is the asset manager capable and skilled to receive such mandate?' This question falls under the 'adverse selection' category and covers the possibility for investors to assess the capability of a given asset manager. 


As a matter of fact, evaluating asset manager capabilities is anything but straightforward. In the specific case of the mutual funds industry, the universe of possible candidates for delegation is so large that the 'capability' question cannot be addressed thoroughly for each single fund manager. Rating agencies help overcoming this difficulty by providing ratings on large mutual fund universes. Mutual funds are rated depending on their past risk-adjusted performance among a specific peer group. Those ratings are designed to help investors focus on a limited number of well-rated funds, i.e. those funds which have a successful track record. 

This approach looks very reasonable but there remains a major puzzle: it is hardly consistent with the 'efficient market hypothesis' and the wealth of related empirical tests. Those studies, among which the preeminent one by Carhart in 1997, suggest that past risk-adjusted fund returns cannot help in predicting future risk-adjusted performance. As a consequence, two issues arise: Can some asset manager consistently deliver alpha over time? In case such an asset manager exists, how can investors identify it? These questions have become so difficult that some investors have abandoned their attempts at selecting an active asset manager. Instead they move on to passive investments. The crisis has added to the uncertainty, leading most major consulting firms to forecast that traditional actively managed funds will continue to be squeezed by passively managed products. 

In this environment, the point of this paper is to contribute to the debate on how to identify capable asset managers. This is done by proposing a methodology to study the behavior and dynamics of fund ratings. Equipped with this method, answers can be given to questions such as: What is the probability that a five star fund remains five star one year ahead? How many times per annum does the rating of a fund change? Are fund ratings comparable with credit ratings? Do fund ratings have predictive power, etc? We hope that you will find the results in this paper interesting as well as useful in practice.

Nicolas Gaussel
Publishing Director 
PhD, Global Head of Quantitative Asset Management

See the publications

Already registered ?

see the white paper

page on 2

Retour en haut

Retour en haut