Bridgewater Pure Alpha II Fund, the flagship fund of Bridgewater Associates – the world’s largest alternative investment management firm – has generated outstanding returns in the last twenty years with especially impressive performance since the Fall of 2008. Using MPI’s new Factor Search™ application, MPI’s endeavor is to capture dynamic betas embedded in Bridgewater fund’s returns and attempt to reproduce its systematic performance behavior using a basket of indices or exchange-traded funds. This case study reveals how quantitative analysis and beta modeling techniques can be used by institutional investors to better understand fund behavior, anticipate performance and improve due diligence, risk management and portfolio monitoring of investment funds.   Click here to download the PDF.

First, let’s turn our attention to the author’s use of two charts from a research report produced by a Morgan Stanley strategist. One of the charts is showing a rolling 60-month correlation of the HFRI Equity Hedge index with the S&P 500 Index over the past 20-years. The other one shows 60-month rolling residual “alpha” returns of the HFRI index net of market factors over the same period.

1. Correlation with the market

The author observes steadily increasing correlation of the index with the S&P 500 and makes the following conclusion:

“For one thing, the correlation between hedge fund returns and the S&P 500 has risen to nearly 100% in the past couple of years, meaning the gap between hedge fund returns and pure market returns is vanishing rapidly.”

2. Excess Market Performance

Next, the author observes a steady decline in residual return to being slightly negative in the last 60-month period:

“For another thing, whatever gap there is between hedge fund returns and market returns may not be positive – the annualized excess hedge-fund return for the past five years has turned negative this year, meaning you were actually better off just sitting in the market than putting money in a hedge fund.”

Although, we believe, charts reflect reality faithfully, both conclusions are incorrect. High correlation by itself is not a sign of an inability to generate superior returns. It is quite possible to have a product highly correlated with the market and still deliver great performance. The observed trend in correlation is directly related to textbook diversification argument: the more funds in the equal-weighted index, the lower is specific, diversifiable risk and the higher is the correlation/R-squared to systematic factors/market. In the chart below we show the historical number of funds in the HFR Equity Hedge category and note that the number of funds increased more than 10-fold over the 20-year period.^[1]

The second observation about negative “gap… between hedge fund returns and market returns” is also incorrect. First, the chart in the article depicts residual return or alpha (after market factors are taken out) rather than excess market return as the author implies. The fact that alpha is steadily diminishing in the index is also a direct consequence of diversification:  the more funds are in the index, the more the alpha is diversified away. However, it doesn’t go away but is transformed into dynamic factor bets (which are sometimes referred to as alternative beta) representing “common wisdom” of thousands of hedge fund managers.

Therefore, a slightly negative residual return over the past 60-month period means that the index is on par or underperforms a dynamic combination of market factors rather than the market itself. In fact, the HFRI Equity Hedge index outperformed S&P 500 index by a large margin over the trailing 5-, 10- and 20- years as shown in figure below.

Moreover, the index–being the average of about a 1,000 equity hedge funds—has outperformed the market in practically every 5-year period over the past 15-years. In the figure below we show index excess S&P 500 performance over each 5-year interval. Even in the latest 5-year challenging period (through Oct) it still managed to outperform the market.

Therefore, hedge fund index seems to be a great investment – quite contrary to what the article implies. But wait, a fair performance comparison has to be based on comparing two fundamental sides of the equation: relative returns and risks.  Therefore, the most important fact is that this index, representing dynamic bets of thousands of managers managed to deliver such superior performance with roughly half of the market risk. In the figure below we compare volatility of the index and the market over the same 5-, 10- and 20-year periods.

Now, this makes the HFRI Equity Hedge Index a phenomenal investment (both on absolute and risk adjusted basis)! But, since it’s not investible^[2], here’s a natural question: are there any other investment products that have similar risk/return profile? We looked at all 6,000 mutual funds in the Morningstar database in all equity, income, balanced both global and domestic categories and couldn’t find a single mutual fund that had similar or better risk/return characteristics!^[3] Attractive properties of hedge fund indices were first studied in the 2010 paper “Hidden Benefits of Equal-Weighting: The Case for Hedge Fund Indices.”

So who would argue against hedge funds and their fees when even their simple average is better than each and every existing mutual fund?

1. Only unique share classes counted
2. The index can be replicated but low tracking error is the key, otherwise these attractive properties would be “lost in replication.” For more information see our asset replication page
3. Specifically, the index has the lowest risk than any fund that outperformed S&P500 Index in at least 10 out of 11 5-year periods.

By the end of September, John Paulson’s two flagship funds, the Paulson Advantage and the leveraged Advantage Plus fund, have lost 32% and 45%, respectively, for the year. As these losses loom large, many analyses and commentaries are trying to explain what went wrong. These reports, such as the one published in 15th The New York Times on October 14th, point to concentrated and leveraged positions in Paulson’s portfolios. It’s not clear, though, whether such poor results of the funds are due to specific security bets, focused sector/asset class concentration and leverage, or both. In addition, most reports are either based on insider information or incomplete portfolio holdings[1]. There seems to be a growing interest in having an objective quantitative analysis verifying these statements.

To detect if some dynamic beta exposures can explain the funds’ past behavior, we performed a returns-based style analysis (RBSA) using only the funds’ monthly returns. Applying MPI’s proprietary Dynamic Style Analysis (DSA) model, we attempted to explain the funds’ last two years of performance with standard sector market indices. The graphs below picture the historical factor exposures of the two funds. R-squared for both funds are at 93%, and predicted R-squared[2] close to 85%, indicating very credible analyses.

Three observations can be made from the charts above:

1. Both funds appear concentrated: in the last two years, over 90% of the funds’ return variation could be explained by two S&P 500 sector indices (Financials and Information Technology) and a Gold index. It doesn’t mean, though, that positions in other sectors don’t influence the fund’s behavior, as deviations between our analysis and the actual holdings and/or management decisions made by the fund are expected and inherent in any statistical analysis. The goal is to provide analytic insights and a better understanding of return behavior.

2. The Advantage Plus fund behaves as having much higher leverage level than the Advantage fund. Our quantitative analysis cannot provide an exact level of leverage of a fund; however, a negative exposure to short-term instruments usually indicates an “implied leverage” – possibly resulting from the use of derivatives. For example, the Advantage Plus has 90% negative cash exposure in September 2011, with a corresponding 190% long market position implying 90% leverage.

3. Exposures to sectors vary over time indicating active decision-making. Looking at the charts above, one could observe market exposure (non-cash exposure) increasing until June 2011 – eventually exceeding 100% (indicating possible increase in leverage) – but then started to decrease, indicating a deleveraging process that started this summer.

The quality of our analysis can also be evaluated by comparing the cumulative performance of the funds (in orange) against their style returns (in purple). These “Style” portfolios are essentially hypothetical and tracking portfolios created from the monthly dynamic weights of the market factors shown in the DSA analysis. The parallel movements of these two indicate that the funds’ performance can be effectively explained by the dynamic beta exposures identified by the model. Given that our replication “style” portfolios have a very simple structure with only four factors – three active market indices and cash (or cash equivalent) – the closeness of tracking is quite remarkable.

The difference between the fund’s total return and its style return is often called selection return, which could be viewed as a proxy of the manager’s stock selection ability. The performance attribution chart below shows that the main portion of the funds’ negative performance this year could be attributed to their exposure to financials, information technology and gold. The negative selection return could indicate that the investments’ individual stocks underperformed their respective sectors. And while recent media articles focused on the funds’ large positions in Bank of America, Citibank and HP — three stocks that significantly underperformed their sector averages from January to September 2011 – our analysis shows that these specific bets didn’t affect the performance YTD as much as their respective sector exposures. We would like to stress again that our analysis is based on past returns and may not reflect current holdings of the funds.

In many cases, fund NAVs and a scant strategy description is the most information an investor can get from a hedge fund. Using Paulson’s Advantage funds analysis as an example, we demonstrate how dynamic returns-based analysis with simple market factors can be utilized in the quantitative assessment of a fund. And while such an analysis is purely quantitative and is not based on fund holdings information, it is remarkable how much information could be uncovered from fund performance data when using precise dynamic analysis techniques.

[1] For instance, holdings information available in SEC’s 13F filings represents the equity holdings of the full Paulson Co., which includes funds other than the Advantages.

[2] Predicted R-Squared is MPI’s proprietary explanatory power and cross-validation statistic.

As highlighted in our Fairholme analysis, concentrated stock bets often lead to concentrated sector bets and both contribute to the fund’s performance. It’s easy to run for the exit after experiencing losses like this but, as always, one should first do a quantitative analysis to understand where the losses are coming from. If one finds significant sector overweighting in an otherwise solid fund, investors may consider offsetting such sector bets with other investments that will help the overall portfolio achieve sector allocations that are close to the benchmark. Thus, if one believes that the manager’s stock selection ability is still there, the heavy exposure to financials in Fairholme could be offset by adding a fund with negligible financials exposure or eliminated by hedging the exposure with a short-financials ETF such as ProShares SEF.

There also exists a “stock-picker fund” that tasks itself with performing such risk management for investors. American Funds Growth Fund of America (GFA) is a multi-strategy domestic equity fund run by several management teams each implementing its own unique investment strategy. Their portfolios are then blended into the fund which is diversified by design. American Funds indicates that, since the product’s launch in 1973, it has outperformed the S&P by 3% a year. The combination of risk management, stock selection ability and exceptional long-term performance previously attracted huge AUM and GFA consistently maintained the status of the largest US equity fund for over six years. Additionally, as of late last year, it was the most popular fund in the enormous U.S. 401k market according to a study done by Brightscope in the October 3rd, 2010 issue of Investment News.

As of September 2011, however, the fund has lost its spot as the largest mutual fund with deep withdrawals. Morningstar indicates that the fund now has $137 billion in assets. In the face of an industry wide flow to passive products, a recent Bloomberg article questions the ability of such a large, focused stock selection fund to outperform in the current market environment. The previously cited Investment News article also indicates that many advisors feel that the fund’s size prevents it from being nimble enough to replicate its past performance.

Given the enormous interest in this fund, we felt compelled to provide some quick insights into the fund’s recent performance since returns-based style analysis (RBSA) is perfectly suited for such a task (as it does not require daily positions on the hundreds of stocks in the portfolio). We performed our analysis with MPI’s Stylus Pro software using Class A shares (AGTHX) 2011 daily NAVs through Oct 14th from Lipper/ThomsonReuters. Note the fund’s losses in Aug-Sep in the chart below, albeit partially offset by Oct gains thus far. It’s not yet clear, though, what portion is due to systematic market conditions.

The first step is to look at the performance relative to S&P 500 Index. The picture is a bit sharper as both months show significant underperformance vs. the index.

We then performed RBSA using daily returns of the S&P 500 sectors and MSCI AC World ex US Index to better assess the fund’s daily exposures. As always, please note that we are not determining what the actual holdings were but, using only return streams, indentifying the mix of indices that best replicates the performance of the actual fund. A high level of diversification (the fund held over 400 positions per latest SEC filing) results in the analysis having a very high R-squared of 99.5%, which makes such an analysis very credible. Note that the exposures are very stable in the chart below – in line with the fund’s low turnover. Some notable observations:

• Significant exposure to foreign equities, and
• Very recent increase in cash exposure.

We would like to again stress that the results of style analysis are not equivalent to actual fund holdings, although they could be very close. For example, AGTHX has 16% in foreign stock holdings as of June according to the company’s website while our analysis shows exposure to MSCI AC World x US index at around 10%. The differences could be attributed, for instance, to ADRs spread between S&P500 sectors or foreign stocks that behave more like U.S. equity. One should keep in mind that exposures derived through RBSA could prove to be more valuable than holdings information as they represent how fund positions are being viewed by market participants. And while the list of holdings represents simply a snapshot of accounting data, exposures mimicking the funds behavior represent market’s assessment of these holdings: it could happen that a fund’s positions behave closer to sectors that are different than the sectors they’re being assigned to.

One could also view the allocations above as a replication portfolio that mimics the fund’s performance. We call the performance of such a portfolio, a mix of passive indices, a fund’s Style Return, which could be achieved by simply investing in sector indices such as ETFs. In RBSA, the difference between the fund’s return and its Style Return is often called Selection Return, which could be viewed as a proxy for the fund’s stock selection ability. It is important to note that the Selection Return derived from RBSA is purely quantitative and may be due to other factors that are not associated with stock picking – especially in low R-squared cases. A high level of R-squared, by definition, leaves little room for variability in selection, which is confirmed in the figure below. Selection represents a fraction of the total return volatility – which was noted in the Bloomberg article.

We still do not have a good explanation for the Aug-Sep underperformance vs. S&P 500, however, and it does not seem like selection accounts for the gap. At this point, it is worth looking at various sector and style bets of the fund vs. the benchmark. We present such an analysis in the figure below where overexposure to certain sectors is shown above X-axis while under-exposure is shown below the X-axis. The fund behaves as if it is overexposed to Consumer Discretionary, Materials and Foreign stocks while being under-exposed to Industrials and Consumer Staples. Also, note the increased cash exposure not present in the index.

Next, we quantify the monthly impact of sector bets on the fund’s performance relative to the benchmark. Timing Return – driven by differences in sector exposures between the fund and the benchmark – is part of the total excess return vs. the benchmark. Please note that the Timing Return in our RBSA does not necessarily insinuate swings in allocations or true “market timing” (AGTHX has been a relatively style consistent fund), it is simply showing the performance attributed to differences in exposure between the Style Return and Benchmark Return. From this returns-based analysis, it appears that both August and September returns were primarily driven by such exposure differentials.

Also, overall monthly volatility of the fund’s Timing Return this year is much higher than that of the Selection Return. In the current market environment, sector bets should be examined more closely than individual stock bets as the impact of the over- and under-exposure of sectors can be quantified. In the figure below we show both positive and negative contributions to AGTHX performance from the exposure differentials observed in our analysis of the fund’s NAVs. It is easy to see that the positive and negative Aug-Sep sector contributions almost cancel each other out, with the notable exception of the foreign stock contribution (including Emerging Markets), Consumer and, especially, Materials sector. Indeed, the fund’s underperformance during these months appears to be largely driven by its exposure to these sectors.

As always, our analysis is purely based on returns and does not reflect actual holdings, but it does provide meaningful insight for investors and advisors. The results of such a short-term analysis do not necessarily lead one to judge AGTHX conclusively though. Certainly, the fund has enjoyed a pretty amazing run over the past 39 years. However, those looking for evidence that the fund’s large size has hurt its ability to pick stocks will not find anything here to dismiss that suspicion. On the contrary, most of the fund’s recent returns can be attributed to its overall asset allocation and sector exposures in our returns-based analysis. Indeed, the fund has underperformed its “Style” or tracking portfolio (the mix of passive indices that best explain its performance) over this period. Much of the fund’s recent sector exposures appear to have contributed to its underperformance against the S&P 500 and returns-based Selection Return has been generally negative over this short period. The better take-away might be that the S&P 500 is not really the most appropriate benchmark to judge AGTHX’s performance. It is difficult to assess the value of a growth-oriented equity product with overseas exposure against the S&P 500 – which is why many prefer to use a blended “style” benchmark to determine a manager’s value. Growth Fund of America has been a very stable fund from a returns-based style perspective – perfect for long-term, strategic investors like 401k participants seeking predictability and, ideally, some alpha. One might conclude from this very short-term analysis, however, that the real value here is not as a “stock picking”, alpha-focused fund but as a consistent active component of a strategic portfolio. Viewed from that perspective though, the recent flows to ETFs and other passive investments become pretty understandable.

Jeff Schwartz contributed to this article

Even though exposures derived in such an analysis should not necessarily coincide with holdings information (after all, we use only the fund’s NAV!), still the precision of the analysis is remarkable. The analysis shows the major exposure to Financials, peaking over 70% earlier in the year. Note that the fund is fully invested, confirmed recently in a Berkowitz interview. What’s interesting, though, is that the fund’s exposure to Real Estate in September has almost tripled since sector data was last available four months ago. Another significant exposure of the fund is to International Stocks (incl. Emerging Markets). Given that all three segments (Financials, Real Estate and Emerging Markets) took a dive during the course of year, it wouldn’t be surprising if just these exposures alone could have impacted the fund’s performance in a negative way. The figure below quantifies this in an intuitive way.

Year-to-date the fund underperformed the S&P 500 Index by 23.8% (Excess) with almost two-thirds due to sector bets (Timing) and the rest attributed to poor security picks such as AIG or St. Joe’s (Selection). Yet another reminder that any buy/sell transaction has at least two sides to it and in the case of Fairholme investors seem to focus their energy primarily on the least important one. And it’s remarkable how much information could be gleaned from publicly available performance data using delicate quantitative analysis.

Of the top funds in our analysis a year ago, only 6% remain in the top funds’ portfolio while 3% now make up the bottom funds’ portfolio this year. Of the bottom funds a year ago, over 7% remain at the bottom and while over 12% went from being among the bottom funds to being among the top funds’ portfolio. However, it must be noted that the top performers a year ago, still rank in the top quartile and the bottom funds a year ago, still rank at the bottom quartile among their peers this year.

We examine factors describing the best and worst performing funds on an aggregate basis. When funds are aggregated in a group, their common factors crystallize and specific bets are diversified away, which provides the basis for such an analysis. The analysis suggests that the top and bottom funds, on average, were exposed to different capitalization and style factors which can help explain their very diverse performance. Please note that our conclusions may change if a different timeframe is used to select the best/worst funds.

Selection of Top/Bottom Fund Groups

-          Based on the universe of 680 funds, the total annualized performance is calculated during the last 52 weeks to rank the funds. Using the top 5% (34 funds) and bottom 5% (41 funds) equally weighted, daily rebalanced portfolios are created to try to identify why, on average, one group performed better in terms of style exposures.

-          The top 5% funds’ cumulative returns are approximately 9.67% higher than the MSCI Europe Index while the returns of the bottom 5% are 11.30% lower. In the chart below we include the results for the top funds and bottom funds as of our original analysis of October 2010. It is worth noting that the top funds remain in the top quartile of the peer group and that the bottom funds remain at the bottom quartile.

Chart 1: Cumulative Performance Chart

Returns-Based Style Analysis Highlights

-          The average RBSA style loadings show that the peer universe, comprised of 680 funds, is diversified with exposures across all size and style factors, with large value making up about 35% of the exposures and large growth a further 16%. Cash and cash equivalent exposures make up almost 11% of the exposure. This exposure might reflect the willingness of managers to increase their cash holdings over the past few months in light of the unresolved European sovereign debt problems.

-          When comparing these results to the results of the same analysis done last year [1], it becomes evident that the style exposures for the peer group are different. The largest exposures were to small cap value and large cap growth, which made up close to 80% of the portfolio. The results of the most recent analysis, which can be seen in Chart 2 below, show an exposure to small value of slightly lower than 10%; large value makes up close to 30% of the total exposure. Exposure to cash and equivalents shows up in the most recent analysis, making up close to 10% of the portfolio, whereas a year ago, the peer group showed no exposure to cash.

-          Using MSCI Europe style and capitalization indices as well as the MSCI Emerging Eastern Europe index as factors, our RBSA analysis demonstrates that the top and bottom funds have exposures to different factors. As shown in Chart 2 below, the top funds had a large exposure to Cash and Cash Equivalents which increased from close to 30% to 40% in the 12 weeks prior to August 2011. This increased exposure to Cash allowed the top funds to avoid the steep losses suffered during August and September. On the other hand, the bottom funds small Cash exposure did not shield them from the drop in the market.

-          As expected, the benchmark displays no exposure to cash or cash equivalents, proxied by the EONIA Index. Comparing the exposures of the portfolio and benchmark helps us understand the excess performance sources for the top and bottom portfolios.

Chart 2: Universe, Funds’, and Benchmark Average Asset Loadings

-          The most recent exposures for the top and bottom funds of our October 2010 analysis show that the funds have changed their style over the past 52 weeks. These exposures can be compared to those for the most recent top and bottom funds. The top funds of 2011 outperformed the top funds of 2010 due to having a larger exposure to cash or cash equivalents; on the other hand, the bottom funds of 2011 underperformed the bottom funds of 2010 because they had a lower exposure to cash or cash equivalents.

-          Over the period analyzed, if we remove the exposure to cash, on a re-scaled basis, the style of the bottom funds tends to converge, with value strategies dominating the overall style. The largest exposures of both bottom funds’ portfolio are to large and small value. The top funds show that small value exposure dominates the style of the best funds of 2010, whereas it is mid growth exposure that dominates the best funds of 2011. This style drift can be seen in the chart below.

Chart 3 – MSCI Europe Style and Capitalization Map

-          The analysis suggests that the worst performing funds in the past are quick to adjust their exposures in order to avoid further losses by increasing their cash exposures, but they do not completely change their style. From the chart above, one can see that both groups of bottom funds are much more exposed to value stocks than to growth stocks i.e. to shares in companies whose earnings are expected to grow at an above-average rate relative to the market. On the other hand, the best performing funds tend to maintain a more diversified profile while increasing their exposure to cash in order to protect prior gains.

-          Style attribution analysis shows that the overweight exposure to cash is the main factor behind the top funds’ above average performance. On the other hand, the bottom funds overweight exposures to emerging Eastern Europe and mid value were the most damaging bets.

Chart 4: Excess Return Contribution

Conclusions

During a turbulent time for European equity markets, only 12 out of 680 funds (i.e. 1.8%) managed to generate positive returns over the 52 weeks ending on September 30^th 2011. A portfolio created from the top 5% best performing funds would have generated positive returns in excess of the benchmark, although the absolute performance was of 16 basis points. It is important to understand how the best performing funds weathered the storm and managed to not lose any money. A sizable exposure to Cash and Cash Equivalents, which increased in the weeks leading to the end of July, served to protect the top funds from going into the red. On the other hand, the worst performing funds were completely exposed to the markets, which ultimately hurt their performance.

As displayed above, the equally weighted portfolio of the best 5% performing funds of our October 2010 analysis remain at the top quartile of the peer group and the worst performing 5% funds remain at the bottom quartile of the peer group. This finding suggests that over a period of one year, performance persistence of European equity funds works both ways, good managers tend to remain at the top and bad managers tend to remain at the bottom.

UNIVERSE DEFINITIONS & ASSUMPTIONS

• Database provider: Lipper, a Thomson Reuters Company
• Registered for sale countries: Austria, France, Germany, Italy, Netherlands, Offshore, Spain, Sweden, Switzerland, and the UK
• Filters: Primary share class, at least 1 year of performance history, Asset Type: Equity, Lipper Global Category: Equity Europe, AUM: minimum EUR 10 Million, Denominated in EUR. Equity Europe, as classified by Lipper, are “Funds with the primary objective to invest in Equity Markets of Europe.”
• Number of funds analyzed: 680
• Date interval: Last 52 weeks starting on October 4, 2010 and ending on September 30, 2011
• RBSA Model: Locally Weighted Regression
• Currency: EUR
• Analysis frequency: Weekly (with compounded daily data)
• Cash proxy (Risk Free Rate): EONIA Index
• Benchmark: MESCI Europe Index
• Style factors: MSCI Indices: Small Growth, Small Value, Mid Growth, Mid Value, Large Growth, Large Value, and Emerging Eastern Europe.
• Analysis performed with mpi Stylus Pro™

Style Return: Return of the Best Fit Portfolio for the Manager Series, where the holdings of the portfolio are the Style Indices.

Selection Return: Calculated as the Manager’s Return subtracted by the Style Return. This is an indication of the Manager’s Selection or Stock Picking abilities.

Timing Return: Calculated as the Manager’s Style Return subtracted by the Benchmark’s Style Return. This indicates whether the Manager’s decisions, to over or under weight the style holdings, as compared to the benchmark, added to the portfolio’s return or not.

Style R Squared (R2): Measure of the model’s power in describing the Manager’s past behaviour in terms of style. The higher the Style R Squared value, the better the model’s explanatory power.

Predicted Style R Squared (PR2): Measure of the model’s power in predicting the Manager’s future behaviour in terms of style. The higher the Predicted Style R Squared value, the better the model’s predictive power.

Style Map: Graphic representation of the results of the Style Analysis. The series being analyzed are mapped unto a Cartesian plane, in which the X and Y axis represent exposures to different Styles and Sizes.

Asset Loadings: Weights of the Style Indices, as holdings, of the Style Portfolio, as calculated by mpi Stylus Pro.

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Markov Processes International, LLC (MPI) is a global provider of investment research and technology solutions. MPI’s analytical tools and methodologies are employed by the finest institutions and financial services organizations to enhance their investment research, reporting, data integration and content distribution. MPI offers the most advanced platform available to analyze hedge funds, mutual funds, portfolios and other investment products, as well as asset allocation and portfolio optimization tools.

MPI’s Stylus Pro software is utilized by alternative research groups, hedge fund of funds, family offices, institutional investors, consultants, private banks, asset managers, diversified financial services organizations as well as marketing, product development and IT departments around the world. MPI also offers solutions for investment advisors and private wealth professionals. For more information on past MPI research articles visit http://markovprocesses.com/company/research.htm

We were interested to know whether there were any investment style changes that resulted in the fund’s recent good performance. Following our previous research paper (“Uncovering the Dynamics of Carmignac Patrimoine”), we ran an updated analysis of the fund using MPI’s proprietary Dynamic Style Analysis (DSA) model in our MPI Stylus Pro software. In figure below we show the fund’s exposures (in excess of the benchmark) vs. its performance relative to the benchmark (“Excess” dots).

According to the chart above, the fund continues to invest in gold (S&P GSCI Gold), fixed income (U.S. 3M LIBOR) and buys put contracts (short position in CBOE S&P 500 PutWrite) as a hedging tool. In the first half of the year these defensive positions resulted in significant underperformance against the benchmark; however, the same exposures staged a dramatic comeback for Carmignac Patrimoine in the recent months.

We examine factors describing the best and worst performing funds on an aggregate basis. When funds are aggregated in a group, their common factors crystallize and specific bets are diversified away, which provides the basis for such an analysis. The analysis suggests that the top and bottom funds, on average, were exposed to different sector and duration factors which can help explain their very diverse performance. Please note that our conclusions may change if a different timeframe is used to select the best/worst funds.

Universe Overview – RBSA Analysis

-          The universe is comprised of 268 funds that are classified under Lipper Global: Bond Global [1], with AUM of at least EUR 10 million and denominated in EUR. The analysis takes into account the performance of the Primary Share Class, as defined by Lipper.

-          Using MPI’s Locally Weighted Regression algorithm, we apply Returns Based Style Analysis (RBSA) using mpi Stylus Pro to estimate the average exposures with weekly observations for the period from August 2, 2010 ending on July 29, 2011. We use Bank of America Merrill Lynch Global Broad Market fixed income indices as Style Factors.

-          The average RBSA style loadings show that the peer universe is diversified with exposures across various maturities of corporate and sovereign bonds, as well as high yield instruments in USD and EUR. The highest exposure to Cash and equivalents, proxied by the EONIA index, is approximately 50%. The peer average displays approximate 16.45% hedged exposure to the US dollar which suggests that the average fund in the peer group did the same.

Selection of Top/Bottom Fund Groups

-          Based on the universe of 268 funds, the total annualized performance is calculated during the last 52 weeks to rank the funds. Using the top 5% (14 funds) and bottom 5% (17 funds) equally weighted, daily rebalanced portfolios are created to try to identify why, on average, one group performed better in terms of style exposures.

-          On a cumulative basis, over the period analyzed, the top 5% of funds outperform its peers, benchmark and the bottom 5%. Returns of the top 5% are approximately 11.60% above the Barclays Capital Global Aggregate Bond TR Index while the returns of the bottom 5% are 3.88% below. The peer group’s performance appears to be on a fairly stable path over this period.

-          The top funds consistently outperformed their peers and the benchmark with an overall volatility, as defined by the annualized standard deviation, of 2.16%. This value is lower than that of the benchmark (8.10%) and bottom funds (5.06%). The Information Ratio is 1.43 for the top funds versus -1.00 for the bottom funds.

Chart 1: Cumulative Performance Chart

Returns-Based Style Analysis Highlights

-          Using sector and duration indices as factors and the US Dollar as a hedge instrument, our RBSA analysis demonstrates that the top and bottom funds have different style exposures. The top funds’ negative weight to the US Dollar indicates that these funds hedge up to 50% of their exposure to the US Dollar, which is close to their 45% US High Yield exposure. On the other hand, the bottom funds do not hedge their exposure to the US Dollar and have very small exposures to US (2.02%) and EUR (2.54%) High Yield. Overexposure to US and EUR High Yield and hedging for adverse USD currency movements allows the top funds to outperform the bottom funds. Given that the USD depreciated by approximately 9.77% [2]against the EUR, over the period analyzed, the top funds’ hedges protected their performance while the bottom funds’ performance was negatively impacted.

-          We can verify that our returns based style analysis results are in line with the holdings-based analysis. The top 10 holdings of the funds within the top 5% portfolio illustrate that these funds are mostly exposed to high yield instruments, short term corporates and a small portion of cash and equivalents. The bottom 5% portfolio is mostly exposed to longer-dated sovereign bonds and cash and equivalents. The downside exposure to the US Dollar suggests that the top funds hedged their exposure to currency risk, which the bottom funds did not do.

-          As expected, the benchmark displays no exposure to cash or cash equivalents, proxied by the EONIA Index. Comparing the exposures of the portfolio and benchmark helps us understand the excess performance sources for the top and bottom portfolios.

Chart 2: Universe, Funds’, and Benchmark Average Asset Loadings

-          The overall stability of the top funds and peer group returns provide R-Squared values that are sufficient to describe the funds past exposures. The R-Squared for the peer group is 64.60%, 71.63% for the top 5%, 89.78% for the bottom 5% and 99.64% for the benchmark; providing credibility to the statistical exposures identified in this analysis.

-          The Style return represents what a manager following a passive (ie. Beta) strategy would have generated. When the Style return is lower than the manager’s return, meaning positive selection returns, one can infer that the manager’s active return is positive. Selection tends to be higher when funds are highly concentrated, such as the bottom 5%, which are mostly exposed to sovereigns. As a group, the top 5% display positive selection (2.88%) and timing (8.37%) skills, whereas the bottom 5% show negative selection (-4.12%) and timing (-0.11). Selection and timing returns represent components of excess benchmark performance.

-          Style attribution analysis can clarify if over- and under-exposures to different styles (versus the benchmark) aided or hindered the funds. Overall, the over- and under-weight exposures suggest that if the bottom funds pursued a passive investment approach (with holdings in the same proportions as their style exposures) they should have underperformed the benchmark by approximately 24bps (not the given 3.88%). As depicted by Chart 3, the top funds’ hedged exposure to the USD and overexposure to US and EUR High Yield allowed them to outperform their peers. Having an overexposure to Cash impaired the top funds. On the other hand the bottom funds were consistently overexposed to Cash by slightly over 20%, which helped the group generate some excess return over the benchmark. The bottom funds’ lack of hedging did not protect them from a depreciating USD.

Chart 3: Excess Return Contribution

-          Although average Corporate 1-3 Yrs exposures for both groups of funds are similar, in reality the top funds were overexposed to this factor during the first 6 months of the period analyzed, whereas the bottom funds were exposed over the last 6 months. The same is true for the top funds exposure to cash, to which they were exposed to from February 2011 onwards. This provides for the differing contributions of this factor to the performance of each group. The dynamic exposures are shown in chart 4 below:

Chart 4: Dynamic Asset Loadings

-          As evident from the chart above, the dynamic USD hedging (negative values below the X-axis) for the top funds follows the same pattern as the long exposure to USD denominated assets. This observation is in line with the underlying funds’ holdings of US High Yield securities and USD denominated corporate and sovereign bonds. This further enforces our conclusion that the top funds hedged their exposure to currency risk which had a positive impact on their performance.

Conclusions

Funds within the Bond Global universe illustrate large dispersions in performance which can be explained by the managers’ specific style bets and use of derivatives to limit currency risks. The hedged exposure of the funds in the peer group ranges from funds with no hedged exposure to funds hedging close to 82%. The best performing funds exhibited a larger hedged USD exposure than the worst performing funds. During this period, the USD depreciated against the EUR which allowed the best performing funds to avoid a partial drop in performance of their USD denominated holdings, especially US High Yield. The best performing funds had very large exposures (close to 65%) to high yield instruments, whereas the worst performing funds had an exposure of close to 4.5%. Exposures to sovereign bonds  negatively impacted the funds’ performance.

UNIVERSE DEFINITIONS & ASSUMPTIONS

• Database provider: Lipper, a Thomson Reuters Company
• Registered for sale countries: Austria, France, Germany, Italy, Netherlands, Offshore, Spain, Sweden, Switzerland, and the UK
• Filters: Primary share class, at least 1 year of performance history, Asset Type: Bond, Lipper Global Category: Bond Global, AUM: minimum EUR 10 Million, Denominated in EUR.
• Number of funds analyzed: 268
• Date interval: Last 52 weeks starting on August 2, 2010 and ending on July 29, 2011
• RBSA Model: Locally Weighted Regression
• Currency: EUR
• Analysis frequency: Weekly (with compounded daily data)
• Cash proxy (Risk Free Rate): EONIA Index
• Benchmark: Barclays Capital Global Aggregate Bond Total Return Index
• Style factors: Bank of America Merrill Lynch Broad Market Corporate 1-3 Yrs, 3-5 Yrs, 5-7 Yrs, and 7-10 Yrs; Bank of America Merrill Lynch Global Sovereign Broad Market Plus Index 1-3 Yrs, 3-5 Yrs, 5-7 Yrs, and 7-10 Yrs; Bank of America Merrill Lynch Euro High Yield Total Return Index, and Bank of America Merrill Lynch US High Yield Master II Total Return. The USD is used as a Hedge Factor.
• Analysis performed with mpi Stylus Pro™

Style Return: Return of the Best Fit Portfolio for the Manager Series, where the holdings of the portfolio are the Style Indices.

Selection Return: Calculated as the Manager’s Return subtracted by the Style Return. This is an indication of the Manager’s Selection or Stock Picking abilities.

Timing Return: Calculated as the Manager’s Style Return subtracted by the Benchmark’s Style Return. This indicates whether the Manager’s decisions, to over or under weight the style holdings, as compared to the benchmark, added to the portfolio’s return or not.

Style R Squared (R2): Measure of the model’s power in describing the Manager’s past behaviour in terms of style. The higher the Style R Squared value, the better the model’s explanatory power.

Predicted Style R Squared (PR2): Measure of the model’s power in predicting the Manager’s future behaviour in terms of style. The higher the Predicted Style R Squared value, the better the model’s predictive power.

Style Map: Graphic representation of the results of the Style Analysis. The series being analyzed are mapped unto a Cartesian plane, in which the X and Y axis represent exposures to different Styles and Sizes.

Asset Loadings: Weights of the Style Indices, as holdings, of the Style Portfolio, as calculated by mpi Stylus Pro.

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Markov Processes International, LLC (MPI) is a global provider of investment research and technology solutions. MPI’s analytical tools and methodologies are employed by the finest institutions and financial services organizations to enhance their investment research, reporting, data integration and content distribution. MPI offers the most advanced platform available to analyze hedge funds, mutual funds, portfolios and other investment products, as well as asset allocation and portfolio optimization tools.

MPI’s Stylus Pro software is utilized by alternative research groups, hedge fund of funds, family offices, institutional investors, consultants, private banks, asset managers, diversified financial services organizations as well as marketing, product development and IT departments around the world. MPI also offers solutions for investment advisors and private wealth professionals. For more information on past MPI research articles visithttp://markovprocesses.com/blog/

We examine factors describing the best and worst performing funds on an aggregate basis. When funds are aggregated in a group, their common factors crystallize and specific bets are diversified away, which provides the basis for such an analysis. The analysis suggests that the top and bottom funds, on average, were exposed to different regional factors which can help explain their very diverse performance. Please note that our conclusions may change if a different timeframe is used to select the best/worst funds.

Universe Overview – RBSA Analysis

-          The universe is comprised of 79 funds that are classified under Lipper Global: Bond Emerging Markets Global [1], with AUM of at least USD 10 million and denominated in EUR and USD. The analysis takes into account the performance of the Primary Share Class, as defined by Lipper.

-          Using MPI’s Locally Weighted Regression algorithm, we apply Returns Based Style Analysis (RBSA) using mpi Stylus Pro to estimate the average exposures using weekly observations for the period from July 5, 2010 ending on July 1, 2011. JP Morgan EMBI+ indices are used as Style Factors.

-          The average RBSA style loadings show that the peer universe is diversified with exposures across all regions; the highest exposure to JP Morgan EMBI+ Europe is approximately 41%. The peer average displays a nearly 20% hedged exposure to the US dollar which suggests that the average fund in the peer group did the same.

Selection of Top/Bottom Fund Groups

-          Based on the universe of 79 funds, the total annualized performance is calculated during the last 52 weeks to rank the funds. Using the top 5% (4 funds) and bottom 5% (5 funds) equally weighted, daily rebalanced portfolios are created to try to identify why, on average, one group performed better in terms of style exposures.

-          On a cumulative basis, over the period analyzed, the top 5% of funds outperform its peers, benchmark and the bottom 5%. Returns of the top 5% are approximately 15.08% above the JP Morgan EMBI Global Diversified Index while the returns of the bottom 5% are 6.26% below. The peer group’s performance indicates large variations over this period.

-          The top funds consistently outperformed their peers and the benchmark with an overall volatility, as defined by the annualized standard deviation, of 4.9%. This value is lower than that of the benchmark (12.04%) and bottom funds (12.05%). The Information Ratio is 1.24 for the top funds versus -1.66 for the bottom funds.

Chart 1: Cumulative Performance Chart

Returns-Based Style Analysis Highlights

-          Using emerging market bond indices for different regions as factors and the US Dollar as a hedge instrument, our RBSA analysis, demonstrates that the top and bottom funds have different style exposures. The top funds’ negative weight to the US Dollar indicates that these funds hedge up to 82% of their exposure to the US Dollar. This observation is reinforced after reviewing the funds’ descriptions and factsheets. On the other hand, the bottom funds do not hedge their exposure to the US dollar, which is also reinforced after reviewing the funds’ factsheets. Given that the USD depreciated by approximately 13% [2] against the EUR, over the period analyzed, the top funds’ hedges protected their performance while the bottom funds’ performance was negatively impacted.

-          We can verify that our returns based style analysis findings are in line with the holdings- based analysis. The top 10 holdings of the funds within the top 5% portfolio show that these funds are mostly exposed to debt from countries in emerging Europe (predominantly Russia, Hungary, Poland, Lithuania and Turkey) and in emerging Latin America (mostly Brazil, Mexico, Argentina, Peru, Colombia and Venezuela). A limited portion is invested in Africa (mainly South Africa) and Asia (mostly Malaysia, Philippines and Indonesia). The top funds are more diversified than the bottom, with exposures of 38% and 34% to Latin America and Europe, respectively, 16% to cash and cash equivalents, and the rest in Africa (8%) and Asia (4%). The bottom funds were mainly exposed to emerging Europe (68%) with the rest split evenly in Latin America, Asia and Africa.

-          As expected, the benchmark displays no exposure to cash or cash equivalents, proxied by the EONIA Index. Comparing the portfolio’s and benchmark’s exposures helps us understand the excess performance sources for the top and bottom portfolios.

Chart 2: Universe, Funds’, and Benchmark Average Asset Loadings – Regional Factors.

-          The diversification effects of blending a large number of funds together in an equally-weighted portfolio result in high explanatory power with R-Squared values of close to 91% for the top 5%; 85% for the bottom 5%; 79% for the peer group average and 99.06% for the benchmark; providing credibility to the statistical exposures identified in this analysis.

-          Style attribution analysis can clarify if over- and under-exposures to different styles (versus the benchmark) aided or hindered the funds. Overall, the over- and under-weight exposures suggest that if the bottom funds pursued a passive investment approach (with holdings in the same proportions as their style exposures) they should have underperformed the benchmark by approximately 15bps (not the given 6%). As depicted by Chart 3, the top funds’ hedged exposure to the USD allowed them to outperform their peers. Being overexposed to Cash helped the group generate some excess return over the benchmark. The bottom funds’ lack of hedging did not protect them from a depreciating USD.

-          The bottom funds underperformance is partly due to their underweight exposure to Latin America. However, it should be noted that not hedging for USD currency risk hurt their overall performance denominated in EUR.

-          As a group, the top 5% display positive selection (1.74%) and timing (13.59%) skills, whereas the bottom 5% show negative selection (-6.19%) and timing (-0.13). Selection and timing returns represent components of excess benchmark performance.

Chart 3: Excess Return Contribution

Conclusions

Funds within the Bond Global Emerging Markets universe illustrate large dispersions in performance. This dispersion can be explained by the specific style bets of the managers and use of derivatives to limit currency and/or regional risks. The hedged exposure of the funds in the peer group ranges from funds with no hedged exposure to funds hedging close to 90%. The best performing funds tend to have a higher hedged exposure. During this period, the USD depreciated against the EUR, hedging for this risk allowed the best performing funds to avoid a drop in performance of their USD denominated holdings. The use of hedges also helped limit the top funds’ volatility. The best performing funds did not deviate widely from the benchmark in terms of Style Exposures and still largely outperformed due to the use of hedges to limit the adverse effects of the depreciation in USD.

UNIVERSE DEFINITIONS & ASSUMPTIONS

• Database provider: Lipper, a Thomson Reuters Company
• Registered for sale countries: Austria, France, Germany, Italy, Netherlands, Offshore, Spain, Sweden, Switzerland, and the UK
• Filters: Primary share class, at least 1 year of performance history, Asset Type: Bond, Lipper Global Category: Bond Emerging Markets Global, AUM: minimum USD 10 Million, Denominated in EUR and USD.
• Number of funds analyzed: 79
• Date interval: Last 52 weeks starting on July 5, 2010 and ending on July 1, 2011
• RBSA Model: Locally Weighted Regression
• Currency: EUR
• Analysis frequency: Weekly (with compounded daily data)
• Cash proxy (Risk Free Rate): EONIA Index
• Benchmark: JP Morgan EMBI Global Diversified Index
• Style factors: JP Morgan EMBI+ Latin, JP Morgan EMBI+ Africa, JP Morgan EMBI+ Europe, and JP Morgan EMBI+ Asia. The USD is used as a Hedge Factor.
• Analysis performed with mpi Stylus Pro™

Style Return: Return of the Best Fit Portfolio for the Manager Series, where the holdings of the portfolio are the Style Indices.

Selection Return: Calculated as the Manager’s Return subtracted by the Style Return. This is an indication of the Manager’s Selection or Stock Picking abilities.

Timing Return: Calculated as the Manager’s Style Return subtracted by the Benchmark’s Style Return. This indicates whether the Manager’s decisions, to over or under weight the style holdings, as compared to the benchmark, added to the portfolio’s return or not.

Style R Squared (R2): Measure of the model’s power in describing the Manager’s past behaviour in terms of style. The higher the Style R Squared value, the better the model’s explanatory power.

Predicted Style R Squared (PR2): Measure of the model’s power in predicting the Manager’s future behaviour in terms of style. The higher the Predicted Style R Squared value, the better the model’s predictive power.

Style Map: Graphic representation of the results of the Style Analysis. The series being analyzed are mapped unto a Cartesian plane, in which the X and Y axis represent exposures to different Styles and Sizes.

Asset Loadings: Weights of the Style Indices, as holdings, of the Style Portfolio, as calculated by mpi Stylus Pro.

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Markov Processes International, LLC (MPI) is a global provider of investment research and technology solutions. MPI’s analytical tools and methodologies are employed by the finest institutions and financial services organizations to enhance their investment research, reporting, data integration and content distribution. MPI offers the most advanced platform available to analyze hedge funds, mutual funds, portfolios and other investment products, as well as asset allocation and portfolio optimization tools.

MPI’s Stylus Pro software is utilized by alternative research groups, hedge fund of funds, family offices, institutional investors, consultants, private banks, asset managers, diversified financial services organizations as well as marketing, product development and IT departments around the world. MPI also offers solutions for investment advisors and private wealth professionals. For more information on past MPI research articles visit http://markovprocesses.com/company/research.htm