Analysis of Neuberger Berman Multi-Cap Opportunities Fund
March 15, 2014
Analysis of Matthews Japan Fund
Today’s mutual fund profile in Barron’s features the Neuberger Berman Multi-Cap Opportunities Fund (NMUAX; Class A shares). This $2.2 billion fund has a hefty front load of 5.75% along with a more reasonable expense ratio of 1.18%. It sports a low 11% turnover ratio charged for the management of a fairly concentrated portfolio of 30 to 40 stocks, of which top-ten holdings currently constitute 33%. Morningstar® classifies this fund as Large Blend, although it invests “across styles and capitalizations.”
According to the article
“The market offers an objective measurement of performance,” he says. “You get a very crisp answer.” That answer for investors in Multi-Cap Opportunities, which Nackenson has run since December 2009, has been an average annual return of 17.6% over the past three years, better than 97% of all large-blend funds. In 2013, the fund was up 43%, at the very top of its category and with very little index overlap.
Let’s analyze the fund’s performance from the Alpholio™ perspective. (Please note that this analysis does not take into account the substantial sales charge of the fund, which would significantly degrade the results.) Here is the cumulative RealAlpha™ chart for the fund, spanning the last four years under the current manager:
The fund’s cumulative RealAlpha™ has been flat to negative from early 2010 through 2012. While it strongly rebounded in 2013, the trend in the most recent couple of months has been negative. Compared to a reference portfolio of ETFs, the fund generated a modest amount (fraction of a percentage point) of annualized RealAlpha™ at a similar level of volatility.
The following chart shows the ETF weights in the reference portfolio over the same analysis period:
The fund’s top equivalent positions were in the iShares Russell 1000 Growth ETF (IWF; average weight of 28.3%), Vanguard Consumer Staples ETF (VDC; 15.6%), iShares S&P Mid-Cap 400 Value ETF (IJJ; 12.6%), Vanguard Industrials ETF (VIS; 10.1%), Vanguard Energy ETF (VDE; 8.5%), and Vanguard Financials ETF (VFH; 7.4%). The Other component of the chart includes six additional ETFs with smaller average weights.
The final chart provides a hypothetical buy-sell signal for the fund, derived from the smoothed cumulative RealAlpha™:
As can be seen, by following this signal a prospective investor would largely avoid the long period of underperformance and timely capitalize on the recent period of outperformance of the fund.
In sum, the overall performance of Neuberger Berman Multi-Cap Opportunities in the last four years has been unimpressive on a truly risk-adjusted basis. Although the fund produced good results last year, there are early indications that this outperformance may not continue in the near future.
To learn more about Neuberger Berman Multi-Cap Opportunities and other mutual funds, please register on our website.
March 8, 2014
Inaccuracy of Average Investor Return
Today’s article in Barron’s profiles the Matthews Japan Fund (MJFOX; investor share class). This $452 million fund has a sensible 1.20% expense ratio, no 12b-1 fees and a 23% turnover (although this figure, reported on the fund’s website, is calculated differently than the 49% turnover published by Morningstar®).
The article indicates that the fund has recently performed quite well, mostly as a result of Japan government’s stimulatory policy:
Over the past 12 months, Matthews Japan rose 22%, compared with 15.3% for the Japan-only funds category, according to Morningstar… For investors, much depends on whether Abenomics, the economic policies of Prime Minister Shinzo Abe, will be able to jolt Japan’s economy out of its seemingly endless slump… [Fund managers] see welcome signs in the positive turn in core inflation last year, as companies began to raise prices for the first time in years.
The fund invests mostly in large-cap stocks, although compared to its primary benchmark, it is tilted toward mid- and small-caps:
The 63-stock portfolio is a mix of big names—including auto makers Toyota Motor and Honda Motor and financial firms ORIX and Mitsubishi UFJ Financial Group —and mid- and small-company stocks that often fly under the radar. At year end, more than 40% of assets were invested in companies with a market value of $5 billion or less, versus just 12% for the MSCI Japan index.
The article compares the annualized one-, three- and five-year returns of Matthews Japan to those of the MSCI EAFE index, which is not appropriate for this single-country fund. A better measure of performance would be the Sharpe ratio, which is the simplest form of adjustment for risk. According to that measure, the fund beat a practical implementation of its primary benchmark, the iShares MSCI Japan ETF (EWJ) in all of three-, five-, ten- and fifteen-year periods through February 2014. The fund also generated better annualized returns than the ETF in those periods.
With that, let’s take a deeper look at the fund’s performance using Alpholio™’s methodology. For this analysis, instead of a diverse pool of reference ETFs, we chose only Japan-oriented ETFs, as well as one domestic ETF to model the fixed-income holdings of the fund. Since some of these reference ETFs only have a limited history, the overall analysis period has been reduced by a couple of years compared to our standard approach. Nevertheless, the results are still informative.
Here is the cumulative RealAlpha™ chart for the fund:
From early 2008 through 2012, the fund generated hardly any RealAlpha™. The cumulative RealAlpha™ started to increase dramatically only in the second quarter of 2013, but subsequently flattened out in the fourth quarter.
The lag cumulative RealAlpha™ curve was generally below the regular one. This indicates that in most analysis sub-periods, investors would be better off by sticking to the reference ETF portfolio rather than adjusting the positions to match the fund’s returns. In other words, not all new investment ideas of the fund panned out. (To learn more about the regular and lag RealAlpha™, please see our FAQ.)
Finally, at over 18%, the fund’s annualized volatility in the entire analysis period was higher by more than 2% than that of the reference portfolio.
The following chart shows the ETF weights in the reference portfolio for Matthews Japan in the same analysis period:
The fund had top equivalent equity positions in the iShares MSCI Japan ETF (EWJ; average weight of 38.6%), iShares Japan Large-Cap ETF (ITF; 14.2%), iShares MSCI Japan Small-Cap ETF (SCJ; 9.7%), SPDR® Russell/Nomura Small Cap™ Japan ETF (JSC; 8.6%), and WisdomTree Japan SmallCap Dividend Fund (DFJ; 8.5%).
The cash and short-term investment position of the fund was modeled by the iShares TIPS Bond ETF (TIP; average weight of 15.9%). The Other component of the chart includes two additional Japan stock ETFs with smaller average weights.
In summary, although the Matthews Japan Fund beat its primary benchmark on a risk-adjusted basis, it generated a mostly flat cumulative RealAlpha™ in the last six years. While the fund significantly outperformed its reference ETF portfolio in the two middle quarters of 2013, there is no guarantee that this will continue in the future. As a matter of fact, the cumulative RealAlpha™ of the fund has been slightly declining since last September. Therefore, investors may want to consider using a dynamic portfolio of ETFs instead.
To learn more about Matthews Japan and other mutual funds, please register on our website.
March 7, 2014
Analysis of Buffalo Discovery Fund
Morningstar® yet again paints a gloomy picture of the so-called “average investor returns.” The thesis is that these returns are generally lower than mutual fund returns because of investors’ poor timing: they make contributions before market downturns and withdrawals before rebounds, similarly to what they do with ETFs. Morningstar’s annual findings have been subsequently propagated by articles in The New York Times and MarketWatch.
To calculate the investor return, Morningstar takes into account the initial value of the fund’s assets, all inflows and outflows for the fund, and the end asset value, all obtained from the fund’s filings in a given period. This calculation is similar to that of the internal rate of return (IRR).
However, there is one major issue with this methodology: the IRR calculated from the fund’s aggregate cash flows is not the same as the average of IRRs realized by all investors in the fund. To determine the latter, Morningstar would have to take a representative sample of investors in the fund, calculate their individual IRRs, and then take an average of those IRRs.
Such a representative sample would need to have at least 20-30 investors, which, if multiplied by over 23,000 funds (all separate share classes), and applied on a regular basis, would be impractical. Hence the convenient shortcut of using only the overall cash flows of the fund and attributing the resulting IRR to a “typical investor.”
This approach not only results in inaccurate figures but also assumes that there exists such a hypothetical average investor whose cash flows into and out of the fund precisely mimicked (in proportion) the composite cash flows of the fund. As Alpholio™ stated in previous posts, it is highly unlikely that such an investor exists.
To illustrate the point, Alpholio™ constructed a simple Microsoft® Excel® simulation of a mutual fund (spreadsheet is available upon request). The simulation spans a period of 12 months and assumes that the fund had 30 investors; in this case, the sample size is the entire population. The simulation has six parameters governing its outcomes (all random variables have normal distributions):
- The amount and standard deviation of the initial investment in the fund. For example, $10,000 and 10%, meaning that 99.7% of the investors initially invested between $7,000 and $13,000 in the fund.
- The annualized return and standard deviation of return of the fund. For example, 10% and 15%, respectively, which models the typical attributes of the S&P 500® index.
- Inflow/outflow base amount and corresponding standard deviation. For example, $10,000 and 10%. This results in random contributions to and withdrawals from the fund each investor would make monthly. (The limit, of course, is that an investor cannot withdraw more than he/she has left in the fund.)
All initial investments are assumed to be made at the end of the month preceding the start of the simulation (e.g. December 31, 2012). All additional contributions to and withdrawals from the fund are assumed to be made on the first day of each month (e.g. from January to December 2013). The return of the fund randomly varies monthly. The spreadsheet can be recalculated by pressing the F9 key, upon which a new series of random scenarios is generated and charted.
The simulation calculates individual IRRs of all 30 investors based on each investor’s random cash flows. It also calculates the overall IRR based on the aggregate cash flows of the fund. The difference between the latter and the former is the IRR error.
It turns out that across many simulation runs, the the average IRR error is relatively small, i.e. around +0.1%. This means that, on average, the fund flow IRR overestimates the true average investor IRR by that small amount. However, the standard deviation of the error is relatively large, i.e. about 0.6%. This means that the error is typically distributed in the -1.7% to +1.9% range. This puts into question the accuracy of 10-year “return gaps” cited by Morningstar in the negative 1.66% to 3.14% range for various mutual fund categories.
Here is a sample distribution of the error in 1000 simulations:
While the interpolation line is somewhat jagged, a normal-like distribution shape clearly emerges.
In conclusion, while this simple simulation is by no means perfect or exhaustive, it does demonstrate that the calculation of a “typical investor return” based solely on the composite flows of the fund can be quite inaccurate. Due to the non-linear, iterative nature of the IRR calculation, the IRR of the aggregate cash flows is not the same as the average IRR of individual investor cash flows. Hence, both investors and the media should interpret the “average investor return” figures with caution.
March 2, 2014
A recent story in Barron’s profiles the Buffalo Discovery Fund (BUFTX). This $642 million no-load fund, formerly known as Buffalo Science and Technology, sports a reasonable expense ratio of 1.01% and portfolio turnover of 53%.
According to the article, the fund’s manager
Carlsen attributes his fund’s success—it has returned 28% annually over the past five years, beating 93% of other mid-cap growth funds, and 11% over the past 10, beating 84%—to his go-anywhere approach. Though the portfolio mainly consists of midsize companies (with an average market value of $7.3 billion, smaller than the category’s average of $8.4 billion), Buffalo Discovery defines itself as an all-sizes fund specializing in innovation.
The primary benchmark for Buffalo Discovery is the Russell 3000® Growth index. The fund returned more than the practical implementation of its benchmark, the iShares Russell 3000 Growth ETF (IWZ), in all but two of the last ten years. However, this may not be a proper index benchmark for the fund, which has 41% in technology and 26% in technology-oriented healthcare stocks, according to the article. Indeed, according to the fund’s description, it
Invests in companies which create value through the commercial application of innovative products, services, or intellectual property [and is] not benchmark-driven.
The majority of the fund’s holdings are classified as mid-cap, which is why Morningstar applies a Mid-Cap Growth category to the fund.
Let’s assess the fund’s performance using Alpholio™’s methodology. Here is the cumulative RealAlpha™ chart for the fund:
From early 2005 through 2011, the cumulative lag RealAlpha™ for the fund was roughly flat. In 2012, the fund generated some RealAlpha™ only to lose it all by the end of that year. The fund’s RealAlpha™ strongly rebounded in the second half of 2013. As a result, in the overall analysis period, the fund generated only a slightly positive (fraction of a percentage point) annualized lag RealAlpha™.
The chart also shows that the lag cumulative RealAlpha™ was generally lower than the regular one. This is an indication that in many sub-periods, new investment ideas did not produce desired outcomes. To put it another way, investors would have been better off by sticking to the lag reference portfolio. (To learn more about the relationship between the regular and lag RealAlpha™, please visit our FAQ.)
The following chart depicts ETF weights in the reference portfolio for the fund in the same analysis period:
The fund’s top equivalent positions were in the following ETFs:
The Other component in the above chart includes six additional ETFs, such as the iShares Nasdaq Biotechnology ETF (IBB) or iShares North American Tech-Software ETF (IGV). Combined, all these equivalent positions prove that the fund was predominantly of a technology and healthcare, and not just general mid-cap growth, nature at its core.
In conclusion, the above analysis demonstrated that Buffalo Discovery could be effectively substituted by a relatively small number of technology and healthcare ETFs, which formed a portfolio of a similar volatility. While the fund has lately generated positive RealAlpha™, history shows that this outperformance streak may not continue for a long time.
To get more information about Buffalo Discovery and other mutual funds, please register on our website.