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.
   

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

In the February 25, 2009 SEC complaint, Paul Greenwood and Stephen Walsh were charged with defrauding investors of $554 million through WG Trading Hedge Fund (“WG”). WG employed an index arbitrage strategy and charged 1% management fees and 20% performance fees. When we evaluated WG’s monthly return series’ statistical properties, we found them to be very similar to the average money market fund.

Over the past 5- and 10-years WG produced annual returns of only 50bp higher than some money market funds. In addition, the correlation between WG and money market funds is around 90% and for some funds is as high as 98%! Lesson learned: a simple statistical profile, in this case correlation analysis, can provide a useful sanity check before committing millions of dollars to a particular investment strategy.

Robert Stanford’s SIB marketed stable high-yielding CDs to investors as a part of his alleged fraud. Returns were in the 6-10% range¹ and were significantly higher than CDs offered by commercial banks but very similar to the returns on the investment product that many of us have in our respective 401(k) and IRAs – Stable Value funds (SVF).

Using data provided in the SEC complaint we compared statistical properties of SIB returns paid to investors with a large group of stable value products and found them to be almost identical (based on 15 annual return numbers) both in risk and return. Moreover, the correlation of some of these funds to Stanford was as high as 94%.

In all recent cases of alleged fraud including Madoff, Stanford and WG Trading, investors were looking for stable positive returns and were willing to take risks and invest in investment products with no protection and no transparency. Stable Value funds, while providing very similar smooth positive returns, are legitimate registered products² but investors have to understand that they carry certain risks. Unlike money market funds, SVF invest in longer-term high grade bonds and, therefore, are exposed to interest rate and credit risk. At the same time, these risks are not visible to investors because SVF are priced at book value rather than market value which allows them to report such stable positive returns. Principal and accrued interest are guaranteed by a number of insurance-wrapper contracts with multiple insurers AIG included. As of today, almost 20% of all DC assets or over $400B is invested in SVF.

There are several links below that go into more detail on the risks of SVF and the call for more fiduciary rigor in their assessment.

David Merkel’s Blog
A very good insight into structure and risks of stable value products from an industry insider where he’s pointing to interest-rate, credit and asset default risks.

Stable Value Investment Association
Describes challenges facing these funds in the current market environment. In short, investors should request better transparency, especially on MV/BV ratio and wrapper agreement terms.

J.P.Morgan Insight > Retirement Plans
Examines the procedures plan fiduciaries use in evaluating their stable value fund options. Has a fund performed well if the stabilized returns are positive and stable? You can only assume. No plan sponsor can clearly determine what risks were taken and whether the manager’s returns reflect a prudent tradeoff of risks and rewards by simply looking at the book value returns.

1. Over the period 1992-2006. See chart on p.28 of the complaint
2. With an important caveat: stable value funds are registered with DOL, not SEC.

In the January 3^rd 2009 article “Mutual Fund Fought Off Bears but Now Is Clawed” The Wall Street Journal reporter Eleanor Laise attempts to find an explanation of the Fairholme fund’s subpar performance in the last quarter of 2008. She interviews fund manager Bruce Berkowitz as well as industry experts, analyses fund holdings, and looks at the most recent stock picks. Her conclusion: the fund made all the right sector moves (e.g., got rid of energy and financials) and its otherwise exceptional performance in 2008 has been marred by just a series of poor stock bets. The article concludes with assurances from the fund manager that the strategy is sound and that all the bets will pay off. We also learn that Mr. Berkowitz now has almost 100% of his net worth invested with his fund. The latter is indeed a good sign but we believe that a sophisticated investor deserves better insight on what the future might hold for this outstanding fund.

By performing an analysis of the Fairholme return record using MPI Stylus we tried to connect the dots to understand the sources of the fund’s past performance and provide some guidance in what to expect in the nearest future.

“Ignore the Crowd”

Fairholme fund indeed has an exceptional track record. It outperformed the S&P 500 index every single year since its launch at the end of 1999 except for 2003. Even last year, which was the focus of the WSJ article, the fund outperformed the index by 7.3%.

Fairholme is a no-load mutual fund with an expense ratio of 1%. Its manager is true to its motto “Ignore the Crowd” as prominently featured on the fund’s website. The fund makes focused sector and securities bets. It’s concentrated, holding about 22 stocks. The top ten holdings account for 64% of its assets (according to August 31, 2008 data.) We consider this degree of concentration to be especially high given the fund’s size of $7.2B. Annual turnover is only 14%.

Fairholme is classified as Large Blend by Morningstar and Mid-Cap Growth by Lipper. This seems a little odd, though it’s not surprising for fund research firms to disagree on such a concentrated portfolio. We performed a returns-based style analysis of the fund to better understand its style exposure. Then we undertook a second analysis with its focus on sector exposure to get at its underlying strategy and to determine the sources of its past and recent successes. This put us in a position to either confirm or refute conclusions presented in the reporter’s story.

In the chart below we show the fund’s cumulative performance since inception vs. the S&P 500 Index. A chart similar to this is prominently positioned in Fairholme’s semi-annual 2008 and annual 2007 reports. Note the accentuated 2008 loss—that’s how performance appeared to long-term Fariholme investors.

The depiction of the drop in 2008 seems a bit confusing given that Fairholme outperformed the index by 7.3% that year. For this very reason we find such “growth” charts to be very misleading because the compounding of a single-period return outlier gets multiplied and creates a distorted view of a fund’s performance. A simple cure for this problem is to employ a logarithmic scale on the y-axis as in the chart below. Doing so shows that most of the gains are attributed to years 2000-2002 while over the past several years the fund’s performance was more or less in sync with the S&P 500 index.

Fairholme could help itself either by replacing their growth chart in the 2008 annual report with this alternative form or by showing the fund’s annualized performance next to it. Such a chart will give the fund deserved credit for its outstanding track record despite recent losses.

Getting Under the Hood

In order to determine the drivers of the fund’s performance we needed to perform a returns-based style analysis (RBSA) of the fund. As a reminder, RBSA attempts to create a dynamic portfolio of generic asset indices that replicates the analyzed fund’s performance. For our analysis we use the nine years of Fairholme monthly returns¹ through the end of 2008, the six Russell style indices, and the MSCI Canada equity index to represent Fairholme’s significant exposure to Canadian stocks. The results of the analysis are presented in chart below.² Note that we didn’t use any holdings information to produce these results. All we needed was a stream of monthly return figures.

The fund behaved as if it held a significant amount of cash that it unloaded entirely in recent months. This could mean that the fund indeed held treasuries, but it is also possible that it included some financial or other interest rate sensitive stocks. SEC filings by Fairholme confirmed that the fund held 25% or more in treasuries, money market funds and bonds during this period. The rest of the cash exposure could be explained by the relative defensiveness or interest rate sensitivity of the specific stocks in the Fairholme portfolio vs. the generic style indices. In any event, this definitely contributed to the fund’s outstanding performance during 2000-2002. Note also defensive style of the fund – its lack of exposure to growth stocks. An exposure to Canadian equities built up during 2005-2007 diminished significantly by the end of 2008. No doubt long-time investors in the fund were expecting the fund manager to make a bold move out of the market to weather the financial storm the way it did back in 2000-2002. But let’s not forget that the size of the fund at that time was a mere $50M vs. $7B+ today!

The quality of the analysis is good with the R-squared equal to 79%, which is relatively high for such concentrated portfolio. It is worth noting that RBSA is especially valuable for studying concentrated portfolios such as Fairholme whereas an analysis based solely on holdings could sometimes produce a distorted view. For instance, an industry sector could be represented by a single stock, the performance of which may have little relationship to its sector’s performance, so provide misleading information about sector representation to an investor.³ Rather than using accounting information and what is sometimes a subjective industry classification, RBSA looks to the effect of a stock or, more properly, all of the stocks on portfolio behavior as viewed and valued by a consensus of market participants.

Another reason why RBSA is particularly helpful in the analysis of concentrated portfolios is that managers of funds with few holdings sometimes engage in “window dressing” to present their portfolio in an appealing way right before the reporting time. An analysis of the return record of a fund uncovers the real drivers (exposures) of its performance, so is not affected by such holdings manipulation.

From the Style Map below we can conclude that Fairholme’s US equity position has been generating performance consistent with Russell Midcap Value Index and that its capitalization size has decreased in the recent years. This is quite different from both Morningstar’s and Lipper’s classifications placing the fund in Large Cap Blend and Mid Cap Growth categories, respectively, based on its portfolio information. As mentioned above, our mapping is not derived from holdings but is based on the fund’s performance relative to Russell style indices and depends on the Russell style classification, so we could expect some differences between both. But this difference between holdings-based and returns-based style is quite significant and should be noted as it puts us in a better position to assess the fund’s near-term risks and exposures.

Daily Analysis

Though an analysis based on monthly frequency return data is sufficient for a reasonable assessment of the fund’s long-term results, we need to resort to the use of daily frequency return data to get a better understanding of the fund’s recent behavior. By having more data points to enrich the same or a shorter return history, we are able to use more indices (explanatory variables), such as economic sectors and sub-sectors, to get a more detailed analysis. For the daily frequency analysis of the fund’s performance in the 4^th quarter of 2008, we used Fairholme’s daily return record as provided by Lipper/Reuters, along with daily returns of MSCI Canada and the S&P 1500 GICS sector indices for the same time period. Given the results of our monthly analysis, we thought that a broad equity index such as the S&P 1500 Index would better represent the fund’s positions than the large cap S&P 500. The results of daily RBSA covering the last quarter of 2008 are presented in chart below.

Although our results do not aim to represent the fund’s holdings, they could be defined as “implied holdings”, i.e., a sector combination that best explains the fund recent performance. In many cases such implied information agrees with the holdings report, especially for highly diversified funds holding hundreds of stocks. Here, the diminishing cash and foreign exposure is consistent both with the WSJ article and the monthly frequency analysis presented above. Negligible Energy and Financials exposures are also consistent with the reported data. One immediate observation from the chart is that the fund manager is making fairly concentrated sector bets and the fund’s performance is dominated by the two sectors: Health Care and Consumer Discretionary. While the significant Healthcare component was noted in the article, the sizable exposure to the Consumer Discretionary segment is rather surprising. Companies in this segment tend to be much riskier and more susceptible to market downturns than in the Consumer Staples segment. This could have contributed to the fund’s poor results in the 4^th quarter. The differences in sector exposures vs. the market index are better seen in the chart below where positive values indicate over-exposure while negative ones (below X-axis) suggest under-exposure. Note that the fund seems to be consistently over-exposed to the two above mentioned sectors and is under-exposed to the rest of the sectors.

Fairholme underperformed the S&P 1500 broad equity index –by 1.64% in the last quarter of 2008. Instead of trying to guess which of the sectors affected the fund’s results in the last quarter of 2008, we performed an attribution analysis to quantify the impact of each bet. We present results of this analysis in the “Excess Attribution” chart, which represents a breakdown of the 1.64% loss vs. the index. Each bar in this chart shows the impact of over/under weighting a sector in the portfolio vs. an “index-neutral” bet, i.e., passively investing in the index proportional to its weight within a broader index.

In a quarter when all sectors experienced losses, the most significant positive contribution came from the fund’s small cash position. Other notable positive bets were the overweighting of Health Care stocks and being out of Financials. The most important observation is that the “Selection” component accounted for a loss in the quarter of well over 5%. Selection return represents the difference between the fund’s performance and the performance of the dynamic portfolio of sector indices (shown in the Asset Loadings chart above) and is usually attributed to the manager’s within-sector security selection ability. In this particular quarter the portfolio suffered the most from specific stock picks within sectors rather than from exposure to any particular sector. So, in this regard our findings echo the WSJ article, which targeted specific stock underperformance.

In summary, fund investors should take away from our analysis the following: (a) sector bets overall proved positive during the quarter (although they should keep in mind the exposure to Consumer Discretionary stocks as we’re moving deeper into a recession) and (b) negative Selection bets in a single quarter may prove positive down the road as the fund manager is usually making long-term bets.

1. Fund returns are provided by Morningstar
2. The analysis is performed using MPI Stylus software in DSA mode. For interpretation of this and following charts please refer to the “Style Primer” and other white papers in the Research section of MPI’s website.
3. It’s a known fact that there are stocks in each economic sector that have higher correlation with sectors other than the ones they belong to.