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—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.

With AXA Rosenberg’s recent admission of a 2009 coding error in its portfolio risk management programs during a year in which the firm’s equity mutual funds lagged their benchmarks by a large margin, the investment community must reflect on how we monitor investments. Specifically, investors (and their advisers) must ask if we have the right tools and protocols to identify unusual risk and performance patterns in a timely manner.

According to the company’s statement in an April 15th letter to investors, the error was discovered in June 2009 and fixed between September and mid-November. Because the “coding error” apparently impacted risk controls, we examined two basic risk measures that are routinely used by both fund managers and investors to evaluate and monitor investment products: Beta and Tracking Error. The chart below shows Beta of the Laudus Rosenberg US Large Capitalization Fund in 2009 with a sample of its peers computed using daily fund NAVs¹.

The fund’s Beta appears to be very different from a sample of other quantitative large cap US equity funds² –the Laudus Rosenberg fund had a sharp drop in market beta right from the start of 2009. It’s plausible that having a beta substantially below 1 during the market rally in March-June of 2009 may have impacted the fund’s performance. What’s intriguing is that another risk measure—the 3-month rolling tracking error to Russell 1000 (as shown in chart below) was unusually high throughout the entire 2009 and was several times higher than that of any of the other quant managers in the group.

Using daily data to monitor Laudus Rosenberg’s beta and tracking error, could we have raised a red flag in June 2009?

It is worth stressing that such an apparent aberration in the fund’s risk profile could be most clearly seen using daily data. Unfortunately, investors typically use monthly data even though daily returns are now easily available from data providers (e.g. Lipper), public sources (Yahoo, Google, etc.), funds and custodians. When using monthly data, a longer history needs to be used to have sufficient observations to estimate the regression. This longer history may cloud, or, as in the case of the Laudus Rosenberg fund, completely transform the picture. The chart below uses a rolling 24 month window to calculate the fund’s beta.

When used in more granular returns-based analysis (RBSA), daily fund NAVs could provide answers to specific questions about attribution of the fund’s gains or losses: what bets were made, when and for how long and with what impact on both performance and risk. Thus, in one of our recent research papers on Oppenheimer Core Bond Fund, we demonstrated the importance of daily data in analysis and monitoring leverage of complex fixed-income portfolios having derivative exposure.

Related links:
http://online.wsj.com/article/SB10001424052748704388304575202501743719416.html
http://www.pionline.com/article/20100419/PRINTSUB/304199980
http://www.pionline.com/article/20100518/DAILYREG/100519849
http://news.morningstar.com/articlenet/article.aspx?id=335669
http://online.wsj.com/article/BT-CO-20100510-714684.html

_____________________________________

1. The daily fund beta is calculated vs. Russell 1000 Index using rolling 66-day (approximately 3 calendar months) calculations. Russell 1000 is the stated benchmark of the Laudus fund.
2. Details on the other quant equity funds used in the study:
   1. The Vanguard Growth & Income fund is sub-advised by Mellon and uses the S&P 500 as a benchmark.
   2. The Russell US Quant Equity fund is a fund of quantitative funds and currently has 6 sub-advisers: Aronson + Johnson + Ortiz, Goldman Sachs Asset Management, INTECH, Jacobs Levy, Numeric and Russell Investment Management Company
   3. The Vanguard Structured Large-Cap Equity Fund (VSLIX) employs a quantitative strategy and is managed by the Vanguard Quantitative Equity Group. Benchmark: S&P 500 Index
   4. The Goldman Sachs Structured US Equity fund is managed by Goldman Sachs Asset Management and uses the S&P 500 as its benchmark.
   

“…is a slow trading fund, investing in commodities, currencies, bonds, and stock indices, and is designed to deliver an attractive return at relatively low volatility. …RIFF, started 13 months ago, did well during its first nine months but has been challenged by the turbulence of this fall, during which its returns were disappointing“.

There’s also generic information available at www.wsj.com obtained by WSJ from RIFF marketing materials:

“RIFF is a modestly-leveraged, slow-trading, global futures fund designed to provide substantial risk-adjusted returns, uncorrelated to US and global equity markets and with medium to low correlation to other asset classes… Targets holding times between nine and 12 months… The RIFF system is completely automated, with the exception of part of actual trade execution. Proprietary algorithms evaluate investment opportunities regularly in an effort to improve the portfolio“.

All of the above makes a good case for a dynamic factor analysis of RIFF returns.

We used fund monthly returns from inception through Dec. 2009 to measure its exposure to several dozen of various market, currency and commodity indices. The system identified the following index combination as the most credible representation of the funds historical factor exposures:

Although we don’t really know what positions the fund actually held, the result represents a straightforward directional strategy: short Credit and lately Energy, long Gold and Emg Mkts. The leverage is not very high; 100% at most. Notable deleveraging of exposures occurred during market turbulence in Aug-Oct 2008. It is possible that some significant restructuring occurred during that time as RIFF suffered significant losses. We show that, despite this, the major exposures remain the same (Gold, Credit, EM) with a slight reversal of other exposures (Equity and Commod).

The credibility of the analysis is very high and the portfolio of systematic exposures (“style” line in chart below) tracks the fund (“total”) very well.

There’s a reason why we performed our analysis through Dec 2009. Once we expanded the date range and added the first two months of 2010, the analysis has shown major exposure shifts and the quality of the analysis started deteriorating very quickly. This could indicate many things including potential rapid restructuring of the fund in the first days of 2010. Such exposure shifts could be related to the recent news that Renaissance management is mulling a shut-down of both RIEF and RIFF.

The question many investors are now asking is: Does BRK.B actually belong in the Financial sector? If BRK.B behaves like a Financial, for all practical purposes (including portfolio construction and risk management) it should be treated as a Financial. However, our analysis of BRK.B’s historical returns shows it behaves more like a Consumer Staple stock than a Financial.

BRK.B’s sector classification is of particular importance to both long-only and long-short portfolio managers attempting to manage sector exposure. For example, consider a long-short portfolio manager attempting to construct a sector neutral portfolio. If the portfolio manager is long BRK.B and it behaves like a Consumer Staple and not a Financial, this portfolio will have unintended, potentially large sector tilts towards Consumer Staples and away from Financials.

Based on monthly return correlations, it appears that, in recent years, BRK.B behaves more like a Consumer Staple than a Financial.

As the chart below clearly shows, not only are BRK.B’s returns more closely correlated with Consumer Staples, but the magnitudes of its returns are more similar to Consumer Staples than Financials.

The results from this simple correlation analysis were further supported by more rigorous, quantitative analysis done using MPI’s proprietary Dynamic Style Analysis (DSA) model. When using a multi-factor model comprising of the S&P 500 sectors, Consumer Staples is the dominating factor driving BRK.B’s returns. Since 2001, the exposure of BRK.B’s returns to Consumer Staples is much larger than its exposure to Industrials and Financials combined.

Why does the market treat BRK.B as a Consumer Staple instead of a Financial when, according to Berkshire itself, their primary business is in the insurance and reinsurance sector? We posit that three different considerations, when viewed together, provide a plausible explanation. First, a large chunk of Berkshire’s insurance businesses are viewed as Consumer Staples not Financials. Second, Berkshire’s reinsurance businesses impact BRK.B’s idiosyncratic returns, but do not have a significant impact on its systematic returns. Finally, and most obviously, Berkshire has large public holdings in companies like Coca-Cola and P&G.

Interestingly, looking at the exposure chart above, prior to 2001, BRK.B traded more like a Financial than a Consumer Staple. What explains this apparent shift in how the market perceives Berkshire Hathaway? We will continue our investigation into BRK.B and Berkshire Hathaway in future blog posts.

First, we expanded the time period from our previous post. The chart below now includes excess return information for five full years from 2004 where we’ve highlighted three months: July ’07 and ’08, which have come under heavy scrutiny for alleged insider trading and July ’06, where outperformance was notably higher than both July ’07 and July’08. Over the five year period (2004-2008) these three months were the only months when the Galleon fund significantly outperformed its peers—an average technology hedge fund in the index.

Clearly, July performance numbers for each of the three years are deemed as statistical outliers (regardless of the legal connotation) which potentially could distort any further analysis of Galleon returns. In addition, the complaint mentions July 2007 $4M gain from trading Hilton—not a technology stock—which could also “contaminate” our analysis. Given the facts above we decided to remove all three July observaions as outliers.

Next, we proceed with a dynamic forensic analysis of the Galleon Technology fund’s returns similar to the one performed in our previous post using MPI Stylus™ and its DSA engine. The results of this analysis are presented in an exposure chart below. Note that this chart does not show actual holdings, but allocations to different factors that best explain the returns of the fund.

We note quite stable long exposure to several Dow Jones technology sectors. The exposure to foreign technology companies is represented by the MSCI All Country ex. US Tech index, indicating positions in ADRs, foreign stocks, or simply sensitivity to foreign markets through investing in U.S. stocks. Short exposure to PowerShares QQQ ETF is supported by the fund’s SEC filings according to which the fund maintained at times a significant position in QQQ put options. Both the cash exposure (about 60%, an indication of net 40% market exposure) and the size of the short position (30-40%) are similar to our earlier results. At the same time, these results are slightly different from the ones in the previous post, which is expected given that we removed three very large outliers.

It should be noted that because of the removal of outliers the statistical quality of the analysis improved significantly. The R-squared statistic determining the quality of fit is 89% and the Predicted R-squared, MPI’s proprietary cross-validation statistic, is 79%. Such a high quality regression is more typical for a large, diversified mutual fund. The quality of fit is also illustrated in the performance chart below where an exposure-weighted portfolio made of factor indexes (called a “Style” or “Tracking” portfolio) closely tracks the fund’s actual performance in-sample (“Total”).

Note that the tracking is exceptionally good through the middle of 2006 where the fund and the tracking portfolio lines begin to deviate slightly despite the removal of outliers. Nevertheless, both the pattern of performance and its magnitude are captured very well throughout the entire five-year history.

While the results of this analysis are very intriguing and somewhat unexpected, the study itself carries very important lessons for investors. First, it shows that analysis of hedge fund returns is a delicate, iterative process requiring careful examination of residuals. If outliers cannot be explained by any available portfolio information they could warrant removal or winzorisation. More importantly, removal of several large “alpha” outliers allowed us to show that in the remaining periods this quintessential high-turnover arbitrageur behaved more like a diversified mutual fund, with returns mimicked by a few long-term directional bets. And while massive computer-generated trading of Renaissance RIEF resulted in such an immediately apparent pattern, in the case of Galleon, the long-term directional pattern became visible only after identification and removal of several exceptional returns.

Daniel Li, PhD contributed to this research.

Daniel Li
Michael Markov

In the recent insider trading scandal involving the founder of Galleon Group, Raj Rajaratnam, the government used wiretaps to secretly record his phone conversations and those of his alleged accomplices. In the complaint, government prosecutors present an insider trading case against Rajaratnam and several other executives for illegally profiting from trading stocks and options of Hilton, Google, Akamai and others.

The full text of the complaint is available on the DOJ website and reads like a detective story.

There is little doubt that wiretapping is a highly effective investigative tool, but we decided to try some less intrusive methods. With $20M in profits allegedly pocketed in the scheme, we thought that there might be a possibility that the effect of these transactions could show up in Galleon’s hedge fund returns.

As a first step, we take a closer look at the actual numbers in the complaint. Below are the facts of the alleged profit from trading in 2007-2008. The rest of trading (e.g., AMD) hasn’t produced meaningful profit.

• July 3-5, 2007 trading in Hilton stock resulted in a profit of about $4M (pp.15-16 of the complaint)
• July 17-19, 2007 trading in Google stock and options resulted in profit of $8M (pp.17-19)
• July 31–Aug 6, 2008 trading in Akamai stock and options resulted in profit of $3.5M (pp.23-25) A detailed analysis of Akamai stock prices and positions listed in the complaint point to about $4.5M P&L gain booked on July 31, when Akamai stock dropped 25%. The realized gain could differ as positions were closed in early August when the stock price was steady.

The October 20th article in The New York Times puts the current assets of the Galleon Technology Fund at $350M. Assuming that the technology fund lost about 50-60% in assets over the past two years, and given the profits shown in the trades above, one should observe spikes of about 1-2% per month over the fund’s systematic, market driven return.

First, we examine the fund’s monthly returns over two years, 2007-2008, where we highlighted the periods of alleged fraud in 2007 and 2008.

One can observe a 5% return in July 2007 and 2% in July 2008 but there’s no conclusive pattern of anything questionable going on during these periods. Essentially, the pattern of the fund’s monthly returns seems random.

The next obvious step would be to distill or filter out systematic market or strategy-driven return. Once that is done, the remainder would more likely reflect security selection picks. A reasonable choice for such a procedure would be to subtract the monthly returns of an appropriate benchmark from the monthly returns of the Galleon Technology fund. In the chart below we show the return differential between the fund and the CISDM Technology Index (representing average returns of hedge funds in the Technology category). The results are striking: the only two periods when the Technology fund significantly outperformed the index are the two months mentioned in the complaint.

Of course, the magnitude of excess performance spikes is 2-3 times higher than what we expected to see and the results are completely dependent on the choice of index. Still, this qualifies as a good initial sanity check.

However, what if a fund had an obscure strategy and a reasonable benchmark wasn’t easily available? In such a case, a forensic analysis of the hedge fund returns could provide an answer. Using MPI Stylus™ and its Dynamic Style Analysis model (“DSA”) we attempted to reverse-engineer the Galleon Technology fund’s returns.¹ After an exhaustive search of thousands of potential combinations of factors and sector indices, the model selected only a handful of relevant factors that best mimic the return behavior of the fund over a two-year period from 2007-2008. The results of this analysis are presented in exposure chart below. Note that this chart does not show actual holdings, but allocations to different factors that best explain the returns of the fund.

The apparent exposure to the Tech and Biotech Dow Jones sector indices, as well as the amount of leverage determined by MPI’s DSA model, are supported by the scant information about the fund’s strategy. It is widely known that the fund made long and short bets on software and hardware companies and maintained a moderate short position of 30-40%. The latter is confirmed by our analysis showing average negative exposure of about 40%. The credibility of the above analysis is also supported by MPI’s proprietary cross-validation statistics. The cash position of roughly 70% could indicate either hedging or margin position. In more generic terms it tells us that the fund had about 30% net long exposure.

Note that the factor exposures are exceptionally stable and an exposure-weighted portfolio made of these indexes (called a “Style” or “Tracking” portfolio) closely tracks the fund’s actual performance in-sample as shown in the cumulative growth chart below.

It should be noted that even though the fund’s strategy was primarily short-term arbitrage, a significant portion of its returns could be explained by directional sector bets and easily replicated with a handful of ETFs if anyone wanted to.

Finally, we compute the return differential between the fund and its dynamic in-sample tracking portfolio (“Style”). These returns are shown in the chart below and are called “Selection” returns as they could be attributed to non-systematic factors such as skill, luck or… insider trading.

We readily observe that both July’07 and July’08 returns are outliers on the positive side. Their magnitude is in the range of what one would expect to see based on the information presented in the complaint. Interestingly, if these two months are taken out of consideration, our analysis shows that the fund manager had unimpressive stock selection results in 2008. Obviously, not every spike in selection return is a suspect of insider trading and the prosecution is yet to prove their case. At the same time, this case adds another important dimension to routine analysis of a portfolio manager’s selection skill: what if it’s neither luck nor skill but something else? An analysis like this one helps investors to frame their questions and precisely position them in time.

What was presented in this quick analysis of a suspect fund obviously cannot be used as evidence in court. Clearly, wiretapping as a prosecutor’s tool wins here hands down. But, for investors with no access to their investments’ positions (forget about wiretapping!) and struggling to reconcile their funds’ performance with common sense and market moves (Performance is too good to be true? Why are the returns so smooth? Why the fund is up when everyone else is down? etc.) a quantitative forensic analysis remains the only viable option. Investors should be and can be smarter than wiretaps.

1. For those unfamiliar with our methodology we refer to the Research Center and other posts in this blog for details.

http://online.barrons.com/article/SB125513241806577275.html

In the article, the author notes that the manager of the flagship Legg Mason Value Trust fund is at the top of its relative peer group for 2009, delivering a 37% YTD return compared to 19% return for the S&P 500 index. The article attributes the fund’s poor performance in 2007-2008 to both sector bets in financials along with poor security selection in names such as Lehman Brothers and JPMorgan. This year, the author concludes, the manager must be doing something right.

Several years ago we performed a detailed analysis of Mr. Miller’s fund to better understand the sources of its well documented 15-year winning streak through 2006.

This time around however our goal was to take a very top level look at the performance drivers behind the fund’s turnaround in 2009. We wanted to identify whether Mr. Miller had “passively” benefited from the market rebound or actively employed new strategies to help lift the fund’s return (or a combination of both).

We started the analysis by analyzing the fund’s monthly returns from January 2007 through September of 2009. The chart below illustrates the fund’s returns-based style analysis factor exposures using S&P 500 sector indices.

There are two immediate observations: (a) Legg Mason Value Trust has had considerable overweights in financials and technology relative to their benchmark S&P 500 index; and (b) overweights in these sectors have remained relative stable throughout the period. From an RBSA perspective, there were virtually no notable structural changes in the fund and that Mr. Miller maintained the fund’s sector composition going into 2009.

Performance attribution analysis in the chart below decomposes the fund’s excess return into timing and selection components. Mr. Miller’s 17% excess return over the index in 2009 can be attributed to both “timing” and “selection” in almost equal proportion. Sector bets accounted for roughly fifty-percent of the fund’s “turnaround” with the other half attributed to security selection within those sectors.

The attribution chart above shows that timing and selection components had roughly equal share in the fund’s underperformance in 2007 and 2008 by 12% and 18% respectively. Mr. Miller’s overweight in financials and associated names such as Lehman Brothers created serious problems for the fund in 2008.

This analysis is very top level and we will post a more comprehensive Performance Attribution Report shortly.

Wall Street Journal’s print edition on October 5, 2009 also featured Mr. Cinnamond as placing second in the Winner’s Circle contest for the 12-month period through September 2009 (posting a 29% return).

We decided to take a closer look using daily data and returns-based style analysis (RBSA), and found Mr. Cinnamond’s selection skills over the past eighteen months have been strikingly strong relative to other small capitalization mutual funds.

We were surprised to find “timing contribution” was only incremental given Mr. Cinnamond’s sizeable moves into cash and other sectors over this period of high market volatility. Most of the excess performance as determined by the sector specific returns-based style analysis model was generated through “selection” as shown in the chart above. Together, the combination of both timing and selection skill led to dramatic excess outperformance of 23.0% over the Russell 2000 Index for the period March 2008 to September 2009.

MPI clients: For a more in-depth Performance Attribution report, please click here. To obtain the mpi Stylus Pro daily attribution template, please contact support@markovprocesses.com. Facilitated and interactive Case Study Webcasts on Intrepid Small Cap and other investment product are available to clients on an ongoing basis. Schedule and sign-up requests can be accessed here.

Non-subscribers: For a more in-depth performance attribution report, please complete this online form and the analysis will be sent to you shortly.

Next day, Bloomberg reported that Putnam basically decided to add an overlay-type product to their target-date funds to hedge out potential risks and “will invest from 10 percent to about 50 percent of its target-date retirement accounts through its new absolute-return funds”
http://www.bloomberg.com/apps/news?pid=20601082&sid=amjAA8xrh9OU

The new absolute return fund series launched by Putnam in Jan this year are supposed to hedge out risks, similar to what CalPERS overlay was supposed to do. And although these funds invest in fixed income securities and derivatives, the idea is the same.

“Jon Goldstein, a spokesman for Putnam, said the firm’s managers can use assets such as commodities and options to hedge their bets.
“They’ve got the freedom to go anywhere,” Goldstein said in an interview.”

We are planning to post some analyses of these funds here on the blog shortly. Since the data quality is crucial for returns-based analysis, we first looked at NAVs of one of these funds, Putnam Absolute Return 100 A (PARTX), on Yahoo! Finance. Interestingly, we found that it had a large number of cases when NAVs remained the same for days and even weeks. This seems unusual given that the fund has 30% of assets invested in corporate bonds, CMBS, Govt bonds, High Yield bonds, etc. The chart below is showing daily fund NAVs from Yahoo!. Periods of constant NAVs could be identified by plateaus.

Unfortunately, the famed manager didn’t provide any hint on where the fund might be heading these days. When asked about the fund’s current cash position, Mr. Berkowitz referred to the data in the semi-annual report showing 17% as of the end of May. We did a quick analysis of the fund’s daily NAVs with a hope that it could shed some light on the most recent fund’s moves. For example, it would be interesting to see if the cash remained steady over the past several months or whether there’s any significant change in the health care sector weight—the most sizeable in the Fariholme. We used returns-bases style analysis and recent daily fund NAVs to deduce more recent portfolio information. It is important to note, however, that similar to fund’s beta this information is provided in terms “exposures” rather than holdings, i.e., telling us how the fund behaves rather what it holds.¹

We did analysis in MPI Stylus using S&P 500 equal-weighted sector indices to measure the exposure to US economic sectors. We also added Barclay’s High Yield Bond index and MSCI EAFE Index because the fund had a position in high yield bonds and foreign equities. We show the result of the analysis in Exposure Chart below.

Note that Cash+Bond exposure (red and green) in May is about 20% of the portfolio and is close to the number provided in the semi-annual report. Other sectors are very much in line with the report with the largest exposure to Health Care. Note that the only information used for this analysis were Fariholme’s daily NAVs through Monday, August 24.

The dynamics of exposures in the last month are of most interest. Cash+Bond exposure is increasing and is at the 30% level, the highest so far this year. The Health Care sector exposure is diminishing and has fallen to 20% which is more visible from the chart below. The rest of the sector exposures remain relatively steady.

In order to find funds with a similar tendency, we analyzed 100 largest equity funds (Source of data: Lipper/Reuters) and plotted our results in the two scatter diagrams below. One chart shows the change in Cash+Bond exposure since the end of June, the other chart provides similar information for Health Care sector. X-axis shows June number, Y-axis – the latest August exposure. Each point represents a fund in the group. The farther the point is from the diagonal, the more dramatic change has occurred over the 2-month period. Fairholme appears to be the only fund that significantly decreased Health Care exposure and increased cash exposure! Most funds don’t change at all or move in the opposite direction.

We would like to emphasize again that the exposure information above may not be equal to what positions the fund holds as it is not using holdings information. For example, in one of the charts we show Health Care exposure at about 38% at the end of May, while it was reported at 30% (incl. Pharmaceutical). At the same time, similar to market beta number, it shows what kind of performance is to be expected from the fund given performance of various market sectors.

1. for detailed explanation of returns-based analysis please refer to our website’s research section or to the previous post on Fairholme in January