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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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

We examine duration 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. Our analysis suggests that the top and bottom funds, on average, had exposures to different duration factors that 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 286 funds that are classified under Lipper Global: Bond Eurozone, with AUM of at least EUR 10 million and denominated in EUR. Our analysis takes into account the performance of the Primary Share Class, as defined by Lipper.

-          Using MPI’s Locally Weighted Regression (LWR) algorithm, we run Returns Based Style Analysis (RBSA) using mpi Stylus Pro to estimate the average exposures using weekly observations for the period from May 3, 2010 ending on April 29, 2011. We use Markit iBoxx Euro Sovereigns Eurozone indices as Style Factors.

-          The average RBSA style loading shows that the peer universe is diversified with exposures across all maturities as well as an overall average exposure to Euribor 3 Month Index of close to 25%.

Selection of Top/Bottom Fund Groups

-          Based on the universe of 286 funds, the total annualized performance is calculated during the last 52 weeks to rank the funds. Using the top 5% (13 funds) and bottom 5% (15 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 bottom 5%. The top 5% group returns approximately 2.8% above the Markit iBoxx Euro Sovereigns Eurozone Index while the bottom 5% group returns 2.9% below the index. The overall market sell-off of peripheral European Sovereign Debt[2] during Q4 2010 appears to have had stronger effects on the performance and volatility of the bottom funds. During Q3 2010, the bottom funds had a short period of strong overperformance, which turned into underperformance within a matter of weeks, eventually dropping 7.8% by the end of 2010 from the peak reached in late August.

-          The top funds display a stable performance, with very low volatility throughout this period. It seems that the sovereign bond sell-off had little impact on the performance of the top funds’ portfolio.

Chart 1: Cumulative Performance Chart

Returns-Based Style Analysis Highlights

-          Using sovereign fixed income indices over various maturities as factors, our RBSA analysis, as depicted by Chart 2, demonstrates that both top and bottom funds  are very concentrated but with different style exposures. The top funds’ portfolio shows an exposure of over 70% to short-term instruments, represented by the Euribor 3 Month Index while the bottom funds’ portfolio appears to be predominantly exposed to long-term instruments. Given that prices of long duration bonds are more sensitive to interest rate changes than prices of short duration bonds, the bottom fund returns turn volatile when renewed sovereign debt fears move market interest rates.

-          A brief analysis into the top 10 holdings of the funds, within the top 5% portfolio, show large exposure to very short term debt instruments and to cash or cash equivalents. The same analysis into the bottom 5% portfolio shows that these funds are exposed to long term debt instruments. This brief holdings based analysis further confirms the results of our returns based style analysis.

-          As expected, the benchmark displays no exposure to cash or cash equivalents, proxied by the Euribor 3 Month Index. Comparing the portfolios’ and benchmark’s exposures helps us understand the sources of excess performance for the top and bottom portfolios.

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

-          The results of a dynamic RBSA analysis can provide insight on where the difference in volatility patterns lies; is it a result of market movements or can be attributed to abrupt changes in style exposures? As shown in Chart 3, the dynamic style exposures are very stable. The top funds’ exposure to the Euribor 3 Month Index was consistently over 70% throughout the period analyzed; while the bottom funds’ exposure to Sovereigns 10Yr+ was consistently over 80%. This allows us to conclude that the volatility of the bottom funds is a result of market movements, particularly of the price movements of long duration bonds, and not of variations in the style exposures.

Chart 3: Universe, Funds’, and Benchmark Dynamic Asset Loadings – Maturity factors

-          Style attribution analysis can clarify if over- and under-exposures to different styles (versus the benchmark) aided or hindered the funds. As depicted by Chart 4, the top funds’ overexposure to Euribor 3M and underexposure to the 5Yr+ factors added to their performance, while underexposure to short term sovereigns appears to have deducted from their overall performance. The bottom funds’ under- and over-weight exposures to all factors seem to have hindered their performance with overexposure to Sovereigns 10Yr+ being the largest contributor to underperformance.

Chart 4: Excess Return Contribution

-          The top funds display positive selection and timing returns of 1.60% and 1.14%, respectively. The bottom funds show the opposite, with both measures equal to -1.56% and -1.39%, for selection and timing, respectively.

-          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 71% for the top 5%; 97% bottom 5%; 67% for the peer group average; and 99.99% for the benchmark, giving credibility to the statistical exposures identified in this analysis.

Conclusions

During a period of turmoil in the Eurozone’s sovereign debt market, a small group of funds managed to generate positive, albeit small, excess performance. An RBSA analysis of the best managers illustrates that the top performers were exposed to very short term debt, proxied by the Euribor 3M Index. This exposure helped the top funds avoid the market volatility and losses stemmed from holding long duration instruments (which suffered the worst losses from the broad market sell-off). On the other hand, the bottom funds had the wildest swings in performance. Being exposed to long term sovereign securities, they incurred losses when the quality of their holdings deteriorated along with the creditworthiness of the issuing countries.

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: share class, at least 1 year of performance history, Asset Type: Bond, Geographical Focus: Eurozone, Lipper Global Category: Bond Eurozone, AUM: minimum EUR 10 Million, Denominated in EUR.
• Number of funds analyzed: 286
• Date interval: Last 52 weeks starting on May 3, 2010 and ending on April 29, 2011
• RBSA Model: Locally Weighted Regression
• Currency: EUR
• Analysis frequency: Weekly (with compounded daily data)
• Cash proxy (Risk Free Rate): Euribor 3 Month Index
• Benchmark: Markit iBoxx Euro Sovereigns Eurozone TR
• Style factors: Markit iBoxx Euro Sovereigns Eurozone factors – 1-3 Year, 3-5 Year, 5-7Year, 7-10 Year, 10+ Year
• 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.

——————————————————————————————————————————-

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

Japan equity funds’ performance ranges from -21.04% to 3.64% over the last 52 weeks (ending March 31, 2011), in JPY terms. The best 5% of the funds outperform the market (pegged to the Daiwa DSI-1 Total Index) by approximately 9.2% and the worst 5% underperform by approximately 8.5%; however, both groups of funds’ cumulative performances are negative during this period. Given that the effects of the March 11 earthquake on performance are included in this period, we decided to analyze how the top and bottom performers’ style allocations change after the events of March 11. Click here to download the PDF.

We examine industry sector 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. Our analysis suggests that the top and bottom funds, on average, had focused on very different industry sectors prior to March 11, but in the following weeks, the bottom funds’ style exposures changed and appeared to be closer to those of the top funds. 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 289 funds that are classified under Lipper Global: Equity Japan, with AUM of at least EUR 10 million and denominated in JPY, EUR, GBP, CHF, SEK and USD. Our analysis takes into account the performance of the Primary Share Class, as defined by Lipper.

- Using MPI’s proprietary and patented Dynamic Style Analysis (DSA) engine, we run Returns Based Style Analysis (RBSA) using mpi Stylus Pro to estimate the average exposures using weekly observations for the period from April 2, 2010 ending on March 31, 2011. We use Daiwa DSI-1 Industry Indices as the Style Factors.

- The average DSA style loadings show that the peer universe is diversified with exposures across all industries as well as an overall average Cash or Cash equivalents (which we refer to as Cash for convenience) exposure of close to 10%. The funds in the peer group are mostly exposed to Manufacturing and Financials, which make up close to 55% of the total exposures.

Selection of Top/Bottom Fund Groups

- Based on the universe of 289 funds, the total annualized performance is calculated during the last 52 weeks to rank the funds. Using the top 5% (15 funds) and bottom 5% (18 funds) equally weighted, daily-rebalanced portfolios are created to try to identify why, on average, one group performed better than the other in terms of style exposures.

- Unsurprisingly, the top 5% of funds outperform their peers, benchmark and the bottom 5%. Over the analysis period, the top 5% group returns approximately 9.2% above the Daiwa DSI-1 Total Index while the bottom 5% group returns 8.5% below the index. The negative effects of the earthquake can be clearly seen, with the funds and the benchmark dropping over 15% in a week before recovering partial ground by the end of the month.

Chart 1: Cumulative Performance Chart

Returns-Based Style Analysis Highlights

- Given the large impact of the March 11^th, 2011 events on the Japanese financial markets, we decided to first conduct our performance analysis on the period ending March 10^th, 2011. Using industry sector indices as factors, our DSA analysis demonstrates that the top funds are more diversified, with exposures to all sectors, including Cash. The top funds outperformed through being overexposed to Cash and Materials, while being underexposed to Financials and Transportation & Utilities.

Chart 2: Universe, Funds, and Benchmark Average Asset Loadings – Industry factors

- Style attribution analysis can clarify if over- and under-exposures to different styles (versus the benchmark) aided or hindered the funds. It is worth noting that although the exposure averages may show that the top and bottom funds were over- or under-weighting the same sectors, their dynamic and rolling historical style exposure may have been significantly different. The top funds overexposure to Cash was consistently over 10% throughout the period analyzed, increasing to approximately 15% in the weeks leading to March 11, which helped them avoid a worse drop in their performance after this date. On the other hand, the bottom funds had an increased exposure to Cash during the last 10 weeks of 2010, with no exposure in the weeks leading to March 11, suggesting that they were fully invested when the earthquake hit, which hurt their performance.

- The top funds’ overexposure to Materials and underexposure to Transportation & Utilities and Financials also added to their performance. The bottom funds’ under- and over-weight exposures to each sector, except to Materials, seem to have hindered their performance. The conflicting results for the exposure to Materials between the top and bottom funds can be explained by the dynamics of their rolling historical style behaviour over the period analyzed, similar to what happens with the exposure to Cash.

Chart 3: Excess Return Contribution

- The top funds display positive selection and timing returns of 1.92% and 4.99%, respectively. The bottom funds show the opposite, with both measures equal to -8.17% and -1.79%, for selection and timing, respectively.

- In order to gain insight as to the changes in style exposures that were triggered by the effects of events of March 11, we run an additional analysis that takes into account the observations up to March 31 and compare the exposures from the periods before and after March 11. Following this date, the top funds display a smaller exposure to Cash and Manufacturing together with an increased exposure to Materials, Services, and Financials.

- On the other hand, the bottom funds’ exposures show a marked change after March 11. Prior to this date, the funds were mostly exposed to Manufacturing, Financials, and Transportation & Utilities. Post-March 11, the funds suddenly display exposures to Materials, larger exposure to Financials, and markedly reduced exposures to Manufacturing and Transportation & Utilities.

Chart 4: Funds’ Average Asset Loadings Pre and Post March 11 – Industry factors

- Post March 11, the bottom funds appear to have shifted their portfolio in favour of the same industries as those of the top funds, with the exception of Services. Whether this will mean that their performance going forward will be similar, we cannot know. What we can infer is that the top and bottom funds, on average, seem to be betting on industries that are expected to experience an increase in demand as Japan begins to reconstruct, such as Materials.

- 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 90% for the top 5%, and 89% for the bottom 5%, for the analysis run until March 10; when including the three additional weekly observations until March 31, the R-Squared increases to close to 94% for the top funds, and 92% for the bottom funds, giving high credibility to the statistical exposures identified in these analysis.

Conclusions

Prior to the earthquake of March 11, 2011, the exposures and style behaviour of the top and bottom funds were very different. The top funds displayed diversified exposures and generated excess returns by managing their exposures to Cash, Materials, Financials, and Transportation & Utilities. The March 11 earthquake did impact their performance and also changed their style exposures, mostly by reducing their Cash exposure and increasing their exposures to Services and Materials. However, their style was stable overall. The bottom funds’ style loadings show a marked change from before to after the earthquake, with funds increasing their exposures to industries that might see an increased demand for their products, such as Materials, while dropping industries that could take longer to recover, such as Manufacturing.

The shockwaves of the earthquake on the financial markets distort the RBSA results based on Sharpe’s original methodology. In this article we used MPI’s proprietary and patented Dynamic Style Analysis (DSA) model, which allows us to estimate meaningful exposures during times of shocks by minimizing the distorting effects of a single unsystematic event.

• Database provider: Lipper, a Thomson Reuters Company
• Registered for sale countries: Austria, France, Germany, Italy, Netherlands, Offshore, Spain, Sweden, Switzerland, and the UK
• Filters: share class, at least 1 year of performance history, Asset Type: Equity, Geographical Focus: Japan, Lipper Global Category: Equity Japan, AUM: minimum EUR 10 Million, Denominated in in JPY, EUR, GBP, CHF, SEK and USD
• Number of funds analyzed: 289
• Date interval: Last 52 weeks starting on April 2, 2010 and ending on March 31, 2011
• RBSA Model: MPI’s patented Dynamic Style Analysis DSA engine
• Currency: JPY
• Analysis frequency: Weekly (with compounded daily data)
• Cash proxy (Risk Free Rate): Bank of Japan Short term Money Market Rate/Call Rates, Uncollateralized Overnight (Daily)
• Benchmark: Daiwa DSI-1 Total Index
• Style factors: Daiwa DSI-1 Industry factors – Materials, Manufacturing, Miscellaneous Manufacturing, Transportation & Utilities, Services, Financials, and Miscellaneous non-manufacturing
• 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.

——————————————————————————————————————————-

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

Asia Pacific ex Japan equity funds’ performance ranges from -0.59% to 40.34% over the last 52 weeks (ending February 25, 2010), in USD terms. The best 5% of the funds outperform the market (pegged to the MSCI AC Asia Pacific ex Japan) by approximately 11.9% and the worst 5% underperform by approximately 14.4%. What role do favourable style allocations play? We examine style/capitalization and country factors describing the best and worst 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. Our analysis suggests that the top- and bottom-performing funds, on average, had focused on the same countries in the region, albeit with somewhat different levels of exposures. The top funds outperformed through selecting better performing stocks within those countries, which is reflected in their positive Selection return; on the other hand, the bottom group displayed negative Selection, implying that although they were invested in the same countries as the top funds, their intra-country stock selection hindered their performance. Both groups of funds are mostly exposed to Cash, Hong Kong and Singapore, loadings that represent close to 52% of the top funds exposure and 58% of the bottom funds exposure. Using an attribution framework, the impact of each bet on the overall performance is quantified. Please note that our conclusions may change if a different timeframe is used to select the best/worst funds. Click here to download the PDF.

Universe Overview – RBSA Analysis

- The universe is comprised of 201 funds that are classified under Lipper Global: Equity Asia Pacific ex Japan, with AUM of at least EUR 10 million and denominated in EUR and USD. Our analysis takes into account the performance of the Primary Share Class, as defined by Lipper.

- Using a 26 week centered, rolling window, we run Returns Based Style Analysis using mpi Stylus Pro to estimate the average exposures over the last 52 weeks ending on February 25, 2011.

- RBSA average exposures to the different countries that make up the MSCI AC Asia Pacific ex Japan index shows that the funds in the universe invest across all the countries in the region[1], together with an exposure to Cash of around 7.2%. The top 5% of funds display a higher Cash exposure, approximately equal to 17.8%, while the bottom 5% have an exposure of close to 13%. These estimated exposures to cash can be validated by reviewing the latest factsheets for the funds that make up each of the top and bottom portfolios.

- In terms of Style-Capitalization factors, one can see that the funds behave similarly, with the funds being exposed to a blend of mid caps with a value tilt, which is more pronounced for the bottom funds. It is worth noting that that the top and bottom funds behave very differently than the benchmark, which is a blend of large caps, as well as behaving differently than the majority of their peer group.

Chart 1: Style-Capitalization Map

Selection of Top/Bottom Fund Groups

-  Based on the universe of 201 funds, the total annualized performance is calculated during the last 52 weeks to rank the funds. Using the top 5% (11 funds) and bottom 5% (12 funds) equally weighted, daily rebalanced portfolios are created to try to identify why, on average, one group performed better in terms of style exposures. - Unsurprisingly, the top 5% of funds outperform its peers, benchmark and bottom 5%. Over the analysis period, the top 5% group returns approximately 11.9% above the MSCI AC Asia Pacific ex Japan Index while the bottom 5% group returns 14.42% below the index.

Chart 2: Cumulative Performance Chart

Returns-Based Style Analysis Highlights

- Using country market indices as factors, our RBSA analysis demonstrates that the top and bottom 5% funds’ are making more concentrated country bets since their top 5 exposures represent, respectively, 71% and 83% of the funds’ behaviour. In comparison, the Peer Group average is more diversified, with the 5 largest exposures representing about 61% of the peer group performance.

- 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 83% for the top 5% and 78% bottom 5%. This allows us to have a sufficient level of trust in the points and conclusions we have made in this analysis.

- The top funds’ performance is mostly explained by their exposure to (from largest to smallest): Singapore, Cash, Hong Kong, Malaysia, and Taiwan. The bottom funds’ are best explained by: Singapore, Hong Kong, Cash, China, and India.

- Cash exposures for both groups of funds are significant, 17.8% for the top funds and 13.3% for the bottom funds. This average exposure does not display the dynamics of the portfolio’s behaviour over the past year. Over the past 6 months there was a steady increase in Cash exposures, reaching their highest level (close to 30%) around early November 2010. The exposure has since then leveled off to show a year-to-date estimated value of 23.5% and 20.5% for the top and bottom funds, respectively. This helped both portfolios avoid larger losses stemming from year to date losses in the countries’ in which they invest. Losses in these markets range from 1% (Malaysia) to over 15% (India).  Among the worst performers that affected the top and bottom funds’ performance YTD are: Indonesia (-5.72), Singapore (-4.47), Thailand (-4.41), and China (-3.93).

- The bottom funds’ exposure to India increased over the past 52 weeks, reaching its highest of approximately 19% in the last 8 weeks ended on February 25^th. The bottom 5% overweight in India, versus the benchmark, during this period hurt them as this country index dropped by over 15%, year to date. The top funds’ were not as exposed to losses in this country, as their exposure was limited to about 4% of their overall loadings.

- It also worth highlighting the fact that neither the top nor bottom funds are exposed to Australia, which makes up for a large portion of the MSCI AC Asia Pacific ex Japan Index, as well as for the overall Peer Group average.

Chart 3: Average Asset Loadings – Country factors

- Although the top and bottom funds have average exposures to the same countries, albeit in different proportions, their performance is vastly different. This allows one to conclude that the top funds excelled in selecting better performing stocks within each country, with the bottom funds selecting poor performers. This can be translated as positive selection returns for the top funds and negative selection returns for the bottom funds.- Interestingly, the notable differences in country exposures of the top and bottom funds’ vs. the benchmark did not translate in significant positive timing returns. The effects of differentiation for the top funds had negligible positive effects on the portfolio’s overall performance, while it was markedly negative for the bottom funds. Although both groups of funds tend to over- and under-weight the same countries, their timing returns are completely different. This is because timing returns are the result of the dynamics of a fund’s exposure to the different Style indices over the total period of analysis. It takes into account the weights in excess of the benchmark at each point in time, which are different than the overall average displayed in the chart above. This means that the top (bottom) funds may have overweighted (underweighted) one country at the right (wrong) time, but in average their weights are estimated to be the same.

Chart 4: Timing and Selection Returns

- Style attribution analysis can explain which factors best describe the funds’ performance. The sum total of the factor returns is approximately equal to the funds’ style return, which represents the funds’ expected performance had the managers followed a passive approach. The difference between the managers’ actual return and the style return is the selection return, which can be explained as the active return generated by the managers’ intra-country stock selections, which can be positive or negative.

Chart 5: Style Attribution Analysis

- The top 5% style return is estimated to be 13.75%, which is below their total return of 32.52%. The difference of 18.77% is the selection return. The bottom 5% would have been better off following a passive approach, as their stylus return is estimated to be 15.81%, which is higher than the 6.2% actual return experienced by the fund. This provides for a negative 9.61% selection return.

Conclusions

Both groups of funds’ style exposures are similar, having many of their top country loadings in common. It is the managers’ abilities to select the better stocks within those countries that differentiate the top 5% funds from the bottom 5%. Over- and under-weight exposures to certain countries versus the benchmark did not generate significant timing returns for the top funds, but generated negative timing returns for the bottom funds. This stresses the need of selecting managers with proven knowledge of each of the countries in which they invest.

• Database provider: Lipper, a Thomson Reuters Company
• Registered for sale countries: Austria, France, Germany, Italy, Netherlands, Offshore, Spain, Sweden, Switzerland, and the UK
• Filters: share class, at least 1 year of performance history, Asset Type: Equity, Geographical Focus: Asia Pacific ex Japan, Lipper Global Category: Equity Asia Pacific ex Japan, AUM: minimum EUR 10 Million, Denominated in EUR and USD
• Number of funds analyzed: 201
• Date interval: Last 52 weeks starting on March 1, 2010 and ending on February 25, 2011
• Window selection for RBSA: 26 week rolling centered window
• Currency: USD
• Analysis frequency: Weekly (with compounded daily data)
• Cash proxy (Risk Free Rate): The BofA Merrill Lynch 91-day Tbill Actual Price Index
• Benchmark: MSCI AC Asia Pacific ex Japan Index
• Style factors: MSCI Country Indices – Australia, China,  Hong Kong, India, Indonesia, Korea, Malaysia, New Zealand, Philippines, Singapore, Taiwan, and Thailand
• 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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[1] The MSCI AC Asia Pacific ex Japan Index is made up equities in the following countries: Australia, China, Hong Kong, India, Indonesia, Korea, Malaysia, New Zealand, Philippines, Singapore, Taiwan, and Thailand.