Of the top funds in our analysis a year ago, 7.69% remain in the top funds’ portfolio while 15.38% now make up the bottom funds’ portfolio this year; 23.08% of the funds in the portfolio either merged with another fund or were liquidated. Of the bottom funds a year ago, 13.3% remain at the bottom and 13.3% either merged with another fund or were liquidated. It must be noted that all funds have experienced a very marked increase in volatility on a rolling 12 week basis, roughly doubling since the start of July 2011.

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 industry 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 286 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 in terms of style exposures.

- The top 5% funds’ cumulative returns are approximately 5.13% higher than the S&P 500 Index while the returns of the bottom 5% are 15.73% lower. In the chart below we include the results for the top funds and bottom funds as of our original analysis of December 2010. It is worth noting that the market volatility over the last 4 months has increased, and that the best funds, by successfully managing their risk, avoided the steep drop in performance suffered by the rest of the peer group.

- It appears that there the top funds of December 2010 managed to consistently remain above the median for the peer group over the last 52 weeks. These funds underperformed the benchmark starting in late July 2011, while outperforming in weeks prior to that. It appears as if the bottom funds of December 2010 would underperform their peers over the period analyzed, however, having an overall lower volatility than the rest of the funds, they managed to limit their losses and ended the 52 weeks outperforming close to 45% of their peers.

Chart 1: Cumulative Performance Chart

Returns-Based Style Analysis Highlights

- Using style and capitalization factors, the first RBSA analysis suggests that, on average, the majority of the funds in the universe of 286 funds were exposed mostly to large caps, with a large number of them being exposed equally to growth and value. For those funds mostly exposed to small- and mid-caps, one can see that there was a bias towards growth. The style map shown in Chart 2 below also makes it clear that the top and bottom funds of this year and of 2010 were clearly exposed to different factors. The Top 5% fund behaves as a large cap blend, while the Top 5% 2010 was concentrated in mid growth. The Bottom 5% fund was biased to small caps with a heavy growth tilt, while the Bottom 5% 2010 had a mid-cap value bias.

Chart 2: Style Capitalization Factors – Style Map

- The second analysis performed using industry sectors (shown in Chart 3 below) as factors shows that the average RBSA style loadings for the peer universe is diversified with exposures across all industries. Cash and cash equivalent exposures make up a little over 6.5% and exposure to EAFE is close to 10%.

- When comparing these results to the results of the same analysis done last year (“North American Equity – USD Denominated Funds” December 2010), it becomes evident that the style exposures for the peer group are similar. On an aggregate basis, the peer group’s behaviour is consistent, with diversified exposures across all industry sectors, cash and cash equivalents, and MSCI EAFE.

- Using S&P 500 Industry Sector indices as well as the MSCI EAFE Index as factors, our RBSA analysis demonstrates that the top and bottom funds have exposures to different factors. As shown in Chart 3 below, the top funds had significant exposures to MSCI EAFE, and different S&P 500 sector indices: Industrials, Health Care, Consumer Staples, and Utilities. On the other hand, the bottom funds’ portfolio shows a limited exposure to MSCI EAFE, and large exposures to Financials and Materials sectors, which make up almost 54% of the total. The R Squared for each one of the regressions in our analysis is above 89.5%, which gives high credibility to our analysis.

- As expected, the benchmark displays no exposure to cash or cash equivalents, proxied by the Merrill Lynch 91 day T-Bill Actual Price Index. Comparing the exposures of the portfolio and benchmark helps us understand the excess performance sources for the top and bottom portfolios.

Chart 3: Universe, Funds’, and Benchmark Average Asset Loadings – Industry Factors

- Style attribution analysis shows that overweight exposures to the Industrials, Utilities, and Consumer Staples sectors together with underweight exposures to the Energy and Financials sectors are the main factors behind the top funds’ above average performance. Exposure to MSCI EAFE negatively impacted the top funds’ returns. On the other hand, all of the bottom funds’ excess exposures over the benchmark negatively impacted the overall performance. The most damaging overweight exposures were those to Materials and Financials.

Chart 4: Excess Return Contribution

Conclusions

Up until the first week of July 2011, North American Equity USD Denominated funds were performing very well, with only 3 of the 280 funds posting cumulative negative returns. After this date, North American Equity USD funds experienced a significant increase in volatility coupled with an overall drop in performance. The market’s volatility remained high until the first week of December; funds that successfully limited their volatility were able to reverse their fortunes and regain lost ground, with 170 funds in the peer group posting positive cumulative returns. The best performing economic sectors were Utilities (cumulative performance of 16.09%), Consumer Staples (13.03%) and Health Care (10.17%); these were favoured by the top funds which show overweight exposures and thus positive returns from these exposures. The bottom funds were underweight in these sectors but favoured financials and materials, which hurt their overall 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: Equity, Lipper Global Category: Equity North America, AUM: minimum USD 10 Million, Denominated in USD. Equity North America, as classified by Lipper, are “Funds with the primary objective to invest in Equity Markets of North America.”
• Number of funds analyzed: 286
• Date interval: Last 52 weeks starting on December 6, 2010 and ending on December 2, 2011
• RBSA Model: Locally Weighted Regression
• Currency: USD
• Analysis frequency: Weekly (with compounded daily data)
• Cash proxy (Risk Free Rate): Merrill Lynch 91 day T-Bill Actual Price Index
• Benchmark: S&P 500 Index
• Style factors: MSCI EAFE Index, S&P 500 Industry sector indices: Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology, Telecommunication Services, and Utilities.
• 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/blog

Of the top funds in our analysis a year ago, 18.75% remain in the top funds’ portfolio while the same percentage now make up the bottom funds’ portfolio this year; 18.75% of the funds in the portfolio were reclassified into different categories. Of the bottom funds a year ago, 12.5% remain at the bottom, 6.25% either merged with another fund or were liquidated, and 6.25% were reclassified. It must be noted that all funds have experienced a very marked increase in volatility on a rolling 12 week basis, roughly doubling over the last 3 months of the analysis.

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 emerging market segments 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 320 funds, the total annualized performance is calculated during the last 52 weeks to rank the funds. Using the top 5% (16 funds) and bottom 5% (20 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 7.95% higher than the MSCI Emerging Markets Index while the returns of the bottom 5% are 14.04% lower. In the chart below we include the results for the top funds and bottom funds as of our original analysis of November 2010. It is worth noting that the market volatility over the last 3 months has increased, and that the best funds, by successfully managing their risk, avoided the steep drop in performance suffered by the rest of the peer group.

- It appears that there was no performance persistence for the top funds of November 2010 over the last 52 weeks. These funds underperformed the benchmark and remained consistently below the median of the peer group. However, it appears as if the bottom funds of November 2010 made changes that allowed them to improve their performance and managed to outperform the top funds of 2010.

Chart 1: Cumulative Performance Chart

Returns-Based Style Analysis Highlights

- The average RBSA style loadings show that the peer universe, comprised of 320 funds, is diversified with exposures across all regions, with EM Asia making up close to 48%, EM Latin America close to 17%, and EM EMEA close to 20% of the exposures. Cash and cash equivalent exposures make up a little over 3% and EM Bonds make up 6%. The rest is approximately equally divided among exposures to the USA and Europe.

- When comparing these results to the results of the same analysis done last year (“Global Emerging Markets Equity” November 2010), it becomes evident that the style exposures for the peer group are similar. On an aggregate basis, the peer group’s behaviour is consistent, with exposures to EM Asia, Latin America, and EMEA making up close to 85% of the total exposures.

- Using MSCI EM style and regional indices as well as the BarCap Global EM Bond Index 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 Global EM Bonds (proxied by the BarCap Global EM Bond index). The exposure to EM Bonds was constant, but shows an increase from an average of 20% to 30% over the past 6 months. On the other hand, the bottom funds’ portfolio show an exposure to EM Bonds that is half of that of the top funds; the biggest difference in exposures is within Asia EM, with the top funds favoring Large Caps, while the bottom funds favored Small and Medium Caps. The exposures to EM Latin America and EMEA Smid are similar for both groups of funds.

- 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 November 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 EM Bond and EM Asia Large Caps, while having a smaller exposure to EM Asia Smid. The bottom funds of 2011 underperformed the bottom funds of 2010 because they had a lower exposure to cash or cash equivalents, EM Bonds, and EM Asia Smid.

- It is interesting to note that the bottom funds of 2011, with exception of their exposure to EM Bonds, show similar exposures than those for the best funds of 2010. In this case, it appears that the bottom managers sought to improve performance by emulating the exposures of the best managers of the previous year. However, as markets have moved, the same strategy that proved to be a winner in the past did not hold over the past year, causing both groups of funds to lag the benchmark and their peers.

- Style attribution analysis shows that the constant overweight exposure to EM Bonds is the main factor behind the top funds’ above average performance. On the other hand, the bottom funds overweight exposure to emerging Asia Smid was the most damaging bet. Although both funds are overweight in EM Bonds, it is the dynamic of the exposures that causes for this excess exposure to contribute positively to the top funds’ performance, but negatively for the bottom funds’. The bottom funds exposure to EM bonds was not constant over time and ranged from almost 21% of the portfolio at the beginning of the period, to 0% around March 2011 and back to 10% by the end of October 2011.

Chart 3: Excess Return Contribution

Conclusions
During a turbulent year of uncertainty for world equity markets, only 8 out of 320 funds (i.e. 2.5%) managed to generate positive returns over the 52 weeks ending on October 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 3 basis points. It is important to understand how the best performing funds managed to stay on top and avoid the losses suffered by the rest of their peers. A sizable exposure to EM Bonds, which increased over the past 6 months, helped the top funds suffer the same losses suffered by the benchmark and the rest of the universe. On the other hand, the worst performing funds’ 44% exposure to Asia Smid, which lost almost 17% on a cumulative basis, hurt their overall 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: Equity, Geographical Focus: Global, Lipper Global Category: Equity Emerging Markets, AUM: minimum EUR 10 Million, Denominated in EUR, USD and GBP. Equity Emerging Markets, as classified by Lipper, are “Funds with the primary objective to invest in Equity Markets across the globe.”
• Number of funds analyzed: 320
• Date interval: Last 52 weeks starting on November 1, 2010 and ending on October 28, 2011
• RBSA Model: Locally Weighted Regression
• Currency: EUR
• Analysis frequency: Weekly (with compounded daily data)
• Cash proxy (Risk Free Rate): EONIA Index
• Benchmark: MSCI Emerging Markets Index
• Style factors: BarCap Global Emerging Markets Index, MSCI USA, Europe, EM ASIA Large and Smid, EM Latin America Large and Smid, and EM EMEA Large and Smid Indices.
• 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/blog

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.

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.

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

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 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.

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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

Given the large number of North American Equity funds registered for sale in Europe, the analysis is divided into USD denominated funds and all other remaining funds. This month we examine USD funds and attempt to rank the managers’ performance based solely on their skills, thereby stripping away any beneficial or hurtful effect of currency movements and improving the returns-based style analysis (RBSA) results. Next month, our asset class focus will be on North American Equity funds denominated in EUR. Click here to download the PDF.

North America Equity funds’ performance ranges from about 10% to 45% over the last 52 weeks (ending October 29, 2010). This disparate performance allows for the best 5% of the funds to outperform the market (pegged to the S&P 500 Index) by approximately 15% and the worst 5% underperforming by approximately 12%.What role do favourable style allocations play? We take a closer look at common 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, invested in quite different industries which impacted their performance. Top-performing funds benefited from selection and timing, mostly impacted by overweighting their exposures to top and mid growth. The worst performers were negatively impacted by having high exposure to cash and international equity (proxied by the MSCI EAFE Index) while underweighting their exposure to growth. Using an attribution framework, we were able to quantify the impact of each bet on the overall 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 287 funds that are classified under Lipper Global: Equity North America, with AUM of at least USD $10 million and denominated in USD. Our analysis takes into account the performance of the Primary Share Class, as defined in Lipper Hindsight.

- RBSA analysis of the universe suggests that slightly over 72% of the funds have a cash exposure of 10% or less; however, the exposure to cash tends to be higher for the lowest ranking funds.

-  The best performing funds have a larger standard deviation than the worst performing funds; however, it is still in line with the majority of the mid-ranked funds in the universe.

- RBSA average exposures to different style/capitalization factors, over the past 52 weeks, the funds in the universe are mostly tilted towards Large Caps.

Selection of Top/Bottom Fund Groups

- Based on the universe of 287 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 understand why, on average, one group performed better in terms of style exposures.

- As shown below, not surprisingly, the top 5% of funds outperform its peers, benchmark and bottom 5%. Over the analysis period, the top 5% group returns approximately 16% above the S&P 500 Index while the bottom 5% group returns 12% below the index.

Returns-Based Style Analysis Highlights

- Using Style Capitalization factors, the first RBSA analysis demonstrates that the top 5% funds are fully invested, with significant style exposures to growth —a category dominated by technology firms. At the same time, the bottom funds’ behaved as if holding large amounts of cash with large exposure to large cap value and growth, as well as exposure to international equity.

- The second analysis, focused on Sector Analysis, shows that the top 5% of funds are exposed mostly to information technology and industrials, sectors in which they are overweight as compared to the benchmark. The top 5% overweight MSCI EAFE, IT, industrials, and materials and underweight the remaining sectors. On the contrary, the bottom 5% are mostly exposed to cash and MSCI EAFE, sectors in which they are overweight vs. the benchmark.

- As a group, the top 5% display strong selection and timing skills, whereas the bottom 5% show negative selection and timing. Selection and timing returns represent components of excess benchmark performance.

- Attribution analysis can clarify if decisions to over- and under-weigh different styles (vs. the benchmark) aided or hurt the funds. For the top 5%, the overexposure to top and mid growth and underexposure to top value benefit the funds. The bottom 5% decision to be exposed to cash and underweight in growth clearly hurt them. Although both top and bottom funds have positive exposures to international equity, which the benchmark does not, the effects are different. For the top funds, the exposure to MSCI EAFE is less than 10% and was positive for the first half of the analysis period. The bottom funds’ exposure has consistently been over 12%, going over 30% for the second half of the analysis. The different behaviours allow for the attribution to be negative for the top and positive for the bottom funds.

- As expected, the diversification effects of blending a large number of funds together in an equally-weighted portfolio results in very high explanatory power for both analysis with R-Squared values of slightly over 96% for the top 5% and 85% for the bottom 5%.

Rolling Risk/Return Analysis

- The chart below shows that over the analysis, the top 5% had periods, on a 12-week rolling basis, of strong over-performance (vs. the benchmark). During Q3 2010, the top 5% (slightly) underperformed the benchmark, and even the bottom 5% funds. Over the past 6-8 weeks, the top funds reclaimed some lost ground. On the other hand, the bottom 5% underperformed the benchmark for most of the period of analysis, although in the first few weeks of July 2010 they appeared to be tracking it very closely.

- The top 5% display higher risk than both the benchmark and the bottom 5%, as defined by the 12-week rolling standard deviation. The bottom 5% have much lower risk which reflects the overexposure to cash. The higher risk of the top 5% has clearly paid off, as they generated over 2 units of return per unit of risk, as measured by the Information Ratio of 2.61.

- On a risk-adjusted basis and comparing the funds with the Capital Market Line, the top 5% portfolio provides a higher return per unit of risk than the benchmark. The bottom 5% are clearly not producing an adequate level of return for the risk they are exposed to.

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: Equity, Geographical Focus: North America, Lipper Global Category: Equity North America, AUM: minimum USD 10 Million, Denominated in USD
• Number of funds analyzed: 287
• Date interval: Last 52 weeks starting on November 2, 2009 and ending on October 29, 2010
• Currency: USD
• Analysis Frequency: Weekly (with compounded daily data)
• Cash proxy (Risk Free Rate): Merrill Lynch 91 day T-Bill Actual Price Index
• Benchmark: S&P 500 Index
• Style factors: Russell 6 Indices: Top 200 Value and Growth, Mid Cap Value and Growth, and Russell 2000 Value and Growth; MSCI EAFE; S&P 500 Sector Indices – Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology, Telecommunication Services, and Utilities.
• 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

Returns-Based Style Analysis (RBSA) Approach

- Upon review of the strategy of a number of funds, a theme emerged: a large number claimed that they sought an absolute performance over time, regardless of the movement of the market by hedging their bets using derivatives or taking long/short positions. For this reason, we used a modified RBSA model to allow for sensing hedging.

- In terms of style and capitalization average exposure over the past 12 months, the funds in the universe cover the entire equity style space:

Selection of Top/Bottom Groups of Funds

- Based on the universe of 797 funds, we calculate the total annualized performance over the last 52 weeks and rank the funds from the best to the worst performers. Using the top 5% (39 funds) and bottom 5% (25 funds) we create equally weighted, daily rebalanced portfolios in order to understand why, on average, one group performed better than the other in terms of style exposures.

Analysis Highlights

- As evidenced by the chart, the top 5% outperforms its peers, benchmark, and the bottom 5%. Over the period of analysis, the top 5% return is approximately 15% on top of MSCI Europe, while the bottom 5% return is 15% below MSCI Europe.

- The style map and asset loadings give us some insight into how the top and bottom performers differ:

- The average style exposures suggest that the top 5% (in average) had over-weighted exposures to small value and large growth, whereas the bottom 5% had a significant large value bias. The negative exposure to MSCI Europe suggests that hedging decisions were made.

- As expected, the diversification effects of blending a large number of funds together in an equally-weighted portfolio results in very high explanatory power of the analysis with R-squared values in the 90s. As a group, the top 5% group displays strong selection and timing skill whereas the bottom 5% group displays the opposite. Both, selection and timing returns represent components of excess benchmark performance.

Rolling Risk/Return Analysis: Consistent Behaviours

- Over the period of analysis, one can see that the top (bottom) 5% consistently outperformed (underperformed) the benchmark on a 12-week rolling return basis. This over (under) performance account to the 14.82% (-14.86%) return in excess over (under) the benchmark during this period.

- Both portfolios display less risk than the benchmark, as defined by the 12-month rolling standard deviation. For the majority of the period, the top 5% has a higher risk than the bottom 5%, which once again confirms the adage that the higher the risk, the higher the return.

- On a risk-adjusted basis, the top 5% portfolio clearly dominates, providing a higher return per unit of risk than the benchmark and the bottom 5%.

UNIVERSE 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, Lipper Global Category: Equity Europe, Assets Under Management of at least EUR 10 Million
• Number of funds analyzed: 797
• Date interval: Last 52 weeks ending on July 30^th 2010
• Currency: Euro
• Analysis Frequency: Weekly (with compounded daily data)
• Cash proxy (Risk Free Rate): EONIA Index
• Benchmark: MSCI Europe
• Style factors: MSCI Europe Large Value, Large Growth, Mid Value, Mid Growth, Small Value, and Small Growth. MSCI Europe as a generic hedging instrument.
• Analysis performed with mpi Stylus Pro™

UNIVERSE DEFINITIONS

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

In our previous post below (referenced in a story from Jeff Sommer of The New York Times) we performed an analysis of the Laudus Rosenberg US Large Cap mutual fund (sub-advised by AXA Rosenberg) that indicated a significant change in the fund’s risk profile occurred as early as 2008. Our study showed a substantial increase in the daily tracking error of the fund to its benchmark – the Russell 1000 Index. The tracking error reached its peak in June 2009 and was several times higher than many of its quant Large Cap Core peers. So there was a symptom that could have alerted risk managers or investors to potential problems. Note that this symptom became apparent only on shorter-horizon (daily temperature charts) rather than two-year averages commonly used by many practitioners.

We then performed some quick diagnostics to understand the nature of the problem and to eliminate false alarms such as data issues or a specific stock bet. We found none of those but the fund’s daily beta was on a path of steep descent in 2009, another worrying symptom but no diagnosis yet.

To get a better understanding of the nature of the problem we will perform a full multi-factor, “metabolic panel”, of the fund’s daily returns-bases style analysis (RBSA). When performed with the right tools that can filter the daily noise, such an analysis could provide key insights and potential answers to specific questions. Through this daily returns-based analysis, we seek to learn more about attribution of the fund’s gains or losses: what bets were made, when were they made and for how long – and with what impact on both performance and risk? It is important to remember that this analysis does not look at actual holdings. Instead, our analysis seeks to understand a fund’s return behavior by comparing its daily returns to those of various passive indices.

Using daily fund NAVs we performed returns-based style analysis in MPI’s Stylus Pro software of Laudus Rosenberg US Large Cap funds against the S&P 500 sectors. The results of the analysis are presented in the charts below where color bands represent portfolio exposures to S&P sectors and cash (exposure to US Treasury bills).

The change in sector exposure at the end of 2008 is immediately apparent with some significant shifts both before and after. Exposure to Energy, Health Care and Consumer Staples changed in a profound way. The quality of this analysis is very high with R-squared being in the 99% range. Again, please note that the analysis was performed using only fund’s NAV and without use of any holdings information and such a result may not directly imply that the fund indeed had such sector position at the time. The best way to further identify possible bets is to plot the fund’s exposures over the S&P 500 index.

In the chart, the bands above the zero line denote sectors where the fund’s sector exposures are greater than those of the S&P 500 index and the ones lower than zero indicate sector exposures less than the index. In 2009, fund exposure differentials peaked in the 2nd QTR and then started slowly normalizing by the end of the year. Our returns-based analysis shows that the fund may have been significantly overexposed to Health Care (the lowest beta sector) and underexposed to some of the highest beta sectors such as Financials. The increased cash position (green color) may indicate that the fund selected more defensive (i.e. lower beta) stocks within sectors. All of these factors could have contributed to the fund having very low beta behavior in 2009, as identified in our previous post.

Incidentally, this period was characterized by what many referred to as a “dash for trash”. Lower quality stocks that were priced for an Armageddon scenario rebounded strongly when the cycle turned in the second quarter of 2009. Consequently, higher quality names, in general, underperformed these lower quality stocks resulting in the underperformance of many fundamental (both quantitative and non-quantitative) alpha strategies. The impact of this phenomenon on a fund’s performance depended on the extent to which these alpha bets were reined in by risk controls.

And finally, let’s see what RBSA can tell us about fund liquidation. On May 2, the board of directors of Charles Schwab made a decision to liquidate four Laudus Rosenberg funds. The funds were immediately closed to new investors and the liquidation was scheduled for July 30th. The fund is still reporting daily NAVs which make it possible to understand how the liquidation is progressing in terms of the fund’s exposure to major sectors. The chart below, showing daily returns-based exposures of the US Large Cap fund , indicates that the fund is currently behaving as though it is about 65% cash with exposure to just a handful of other sectors.

Having such little exposure to the market over the past several weeks clearly boosted the fund’s performance for remaining investors! Again, we would like to make a disclaimer that this analysis is based only on the fund’s NAVs and may not reflect the fund’s actual positions.