Stockpicker’s Delight
active management, active share, analysis, correlation, mutual fund

A recent piece in Barron’s proposes an investment into seven actively-managed mutual funds. This recommendation is motivated by the following observation:

A long, humiliating period for professional stockpickers might be giving way to something different. Stocks that have moved in near unison in recent years are beginning to chart more distinct paths. Data points that haven’t mattered in a decade, like the relationship between prices and fundamental measures of value, are starting to have more sway on returns. The divide between cheap stocks and expensive ones remains exceptionally wide, which could mean last year’s shift in favor of value investing is just the beginning.

Supposedly, were on the verge of entering the “stockpicker’s market,” as shown in this chart:

Average Pair-Wise Correlation of All S&P Stock Combinations

The myth that low correlations between stock returns lead to active manager’s outperformance has long been debunked. Similarly, a high active share is cited as one of the reasons actively-managed funds will outperform their passive peers. Please refer to our earlier post for a discussion of this topic.

So, this post will instead focus on the long-term performance of the funds featured in the article:

Time for Proactive Investing

The following charts with related statistics show the cumulative RealAlpha™ for each fund that has at least ten years of history through 2016 (to learn more about this and other patent-based performance measures Alpholio™ uses, please consult our FAQ). In all analyses, the number of ETFs in the reference portfolio was limited to no more than seven. The ETF membership and weights in each reference portfolio were constant throughout the entire evaluation period.

Here is a chart with statistics for the AllianzGI NFJ Dividend Value Fund (PNEAX; Class A shares):

Cumulative RealAlpha™ for AllianzGI NFJ Dividend Value Fund (PNEAX) over 10 Years

The fund cumulatively returned over 20.5% less than its reference ETF portfolio of lower volatility.

Here is a chart with statistics for the DFA US Large Cap Value Portfolio (DFLVX; Class I shares):

Cumulative RealAlpha™ for DFA US Large Cap Value Portfolio (DFLVX) over 10 Years

The fund cumulatively returned about 8.5% more than its reference ETF portfolio of lower volatility.

Here is a chart for the Dodge & Cox Stock Fund (DODGX):

Cumulative RealAlpha™ for Dodge & Cox Stock Fund (DODGX) over 10 Years

While the fund produced a 14% higher cumulative return than its reference ETF portfolio, by early 2016 it also lost virtually all of its cumulative RealAlpha™ generated since 2007.

The following chart is for the Sound Shore Fund (SSHFX):

Cumulative RealAlpha™ for Sound Shore Fund (SSHFX) over 10 Years

On a cumulative return basis, the fund underperformed its reference ETF portfolio by over 7.7%; most of that loss occurred over the past two years.

This chart is for the T. Rowe Price Equity Income Fund (PRFDX):

Cumulative RealAlpha™ for T. Rowe Price Equity Income Fund (PRFDX) over 10 Years

The fund’s cumulative return was over 23.3% lower than that of its reference ETF portfolio of a slightly higher volatility.

The final chart is for the Vanguard U.S. Value Fund (VUVLX; Investor Class shares):

Cumulative RealAlpha™ for Vanguard U.S. Value Fund (VUVLX) over 10 Years

The fund cumulatively returned about 9.1% more than its reference ETF portfolio of a slightly lower volatility. However, as recently as at the end of October 2016, the cumulative RealAlpha™ was only 4.4%.

In conclusion, only three out of the six funds analyzed above added some value when compared to their respective reference ETF portfolios. The rest of the funds underperformed, and in some cases quite significantly. It remains to be seen whether a combination of the expected low stock correlations in the market and a high active share of these funds leads to their significant outperformance in 2017.

To learn more about these and other mutual funds, incl. the composition of their reference ETF portfolios, please register on our website.

To learn more about the these and other mutual funds, including the composition of their reference ETF portfolios, please register on our website.


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Substituting Liquid Alternative Funds
alternatives, analysis, asset allocation, exchange-traded fund, hedge fund, mutual fund, portfolio

A recent cover story in Barron’s features liquid alternative funds from AQR. According to the article

The liquid-alt pitch is that individuals can access the same types of investments as university endowments and other big institutions, to diversify equity-heavy portfolios, typically with a 10% to 20% allocation to liquid alts… The advantage of the [AQR Managed Futures] strategy […] is that it is uncorrelated with other asset classes, and “has the most consistently strong performance in equity bear markets.” That is when diversification matters most, as was the case in the third quarter of last year and the early part of this year.

Ideally, returns of a liquid-alt fund should not only be uncorrelated with those of both stocks and bonds but also significantly positive over a long evaluation period. Let’s take a look at the performance of three AQR funds with a sufficiently long history.

The following chart shows rolling return correlation of the AQR Managed Futures Strategy Fund (AQMIX) with the Vanguard Total Stock Market ETF (VTI) and the Vanguard Total Bond Market ETF (BND):

Correlation of Rolling 36 Monthly Returns for VTI and BND with AQMIX

Please note that AQMIX had the first full month of returns in February 2010. Consequently, the first rolling 36-month return became available at the end of January 2013. As could be expected, the fund had lower correlation to stocks than to fixed income, although both coefficients were quite low (generally, correlation below 0.6 provides diversification benefits).

Here is a similar chart with related statistics for the AQR Multi-Strategy Alternative Fund (ASAIX):

Correlation of Rolling 36 Monthly Returns for VTI and BND with ASAIX

Compared to AQMIX, this strategy had a higher correlation to bonds.

Here is a similar chart with statistics for the AQR Diversified Arbitrage Fund (ADAIX):

Correlation of Rolling 36 Monthly Returns for VTI and BND with ADAIX

In contrast to AQMIX and ASAIX, this strategy had a higher correlation to equities than bonds; however, both coefficients were still pretty low.

The problem with any of these strategies is the lack of accessibility for most individual investors:

AQR’s approach can be hard to understand. Because of this—and to deter hot money—the firm sells its liquid-alt funds almost entirely through financial advisors. Retail buyers can access the funds directly through fund supermarkets like Fidelity, but direct investments involve a minimum of $1 million. Investments through advisors and 401(k) plans have no minimum.

Is there a way to substitute these liquid-alt funds with readily available ETFs? Let’s explore this possibility using Alpholio™’s patent-based analysis service for mutual funds. One variant of this methodology constructs a reference portfolio of ETFs with fixed both membership and weights. Here is the resulting cumulative RealAlpha™ chart for the AQR Managed Futures Strategy Fund (to learn more about this and other performance measures, please visit our FAQ):

Cumulative RealAlpha™ and Statistics for AQR Managed Futures Strategy Fund (AQMIX)

As the statistics section below the chart shows, since its inception the fund had a smaller return and a much higher volatility (measured by standard deviation) than those of the reference portfolio. The following chart illustrates the constant composition of the reference ETF portfolio in this analysis:

Reference Weights for AQR Managed Futures Strategy Fund (AQMIX)

The major positions in the reference portfolio were the PowerShares DB US Dollar Index Bullish Fund (UUP; fixed weight of 38.1%), iShares 20+ Year Treasury Bond ETF (TLT; 22.9%), iShares MSCI Netherlands ETF (EWN; 9.3%), Guggenheim CurrencyShares® Swiss Franc Trust (FXF; 6.0%), Consumer Staples Select Sector SPDR® Fund (XLP; 5.5%), and Utilities Select Sector SPDR® Fund (XLU; 4.7%). The Other component in the chart collectively represents addition five ETFs with smaller fixed weights.

The return correlation of the reference ETF portfolio over the entire evaluation period was 0.16 with VTI and 0.58 with BND. Given that these figures for AQMIX were approximately -0.07 and 0.21, respectively, the reference portfolio was not as good a diversifier for stocks and bonds as the fund was. However, the reference portfolio only had long positions in non-leveraged ETFs. It also returned about 8% more than the fund on a cumulative basis and with a 59% lower volatility. Similar analyses can be conducted for ASAIX and ADAIX. In the end, it is up to the investor to weigh the pros and cons of using reference ETF portfolios as substitutes for these funds in the context of the overall portfolio.

We hope that our Investment Toolkit™ will provide useful services for investors who want to construct well-diversified portfolios. If you would like to use it, please register on our website.


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Increasing Correlations of Asset Classes
analysis, asset allocation, correlation, portfolio

A recent column from Bloomberg Gadfly discusses increasing correlations of asset classes. The correlation coefficient of periodic returns is a measure of the extent to which these returns move in the same direction. Contrary to a common misconception, a high correlation does not imply that the two assets or classes are identical. Ideally, to diversify a portfolio, long-term correlations among portfolio components should low.

This correlation shift has a major impact on portfolio construction following the Modern Portfolio Theory. The article uses ten-trailing-year correlations of various indices:

Ten-year trailing correlations of asset classes in 1997

Ten-year trailing correlations of asset classes today

While a point-in-time analysis of ten-year correlations between indices is instructive, it is of little practical value to investors. Luckily, Alpholio™ has just introduced a new Multi-Correlation service, which provides an interactive analysis of rolling correlations.

In a typical analysis, monthly returns are used because they are less “noisy” than weekly or daily returns. A span of 36 months (three years) of returns is usually sufficient to approximate the long-term correlation and, at the same time, to nimbly react to rapid correlation changes. A rolling-period approach provides insights on how the correlation coefficient evolved over time. It can also facilitate calculation of useful statistics. Finally, instead of artificial indices that cannot be bought or sold, Alpholio™ uses ETFs.

To demonstrate the Multi-Correlation service in action, here is a chart of rolling correlations between each of several analyzed ETFs and one reference ETF:

Correlation of Rolling 36 Monthly Returns for IVV, EFA, EEM, ICF with GSG

In the above chart, the analyzed ETFs are the iShares Core S&P 500 ETF (IVV), iShares MSCI EAFE ETF (EFA), iShares MSCI Emerging Markets ETF (EEM), and iShares Cohen & Steers REIT ETF (ICF). The reference ETF is the iShares S&P GSCI Commodity-Indexed Trust (GSG).

Please note that the youngest of these ETFs (GSG) determines the common time frame of this analysis: the first full month of GSG returns was August 2006, so the first 36-month rolling return became available at the end of July 2009. The Multi-Correlation service determines the longest possible analysis interval automatically.

The following table contains statics of rolling correlations between each analyzed ETF and the reference ETF:

Statistics of Correlation of Rolling 36 Monthly Returns for IVV, EFA, EEM, ICF with GSG

By default, the statistics are ordered in the ascending order of median value, but can be reordered in any ascending/descending order by clicking on the header of the respective column. In conjunction with the chart, these statistics show that each of the equity ETFs had a substantial (above 0.6) correlation to commodities, while the REIT ETF had the lowest correlation. The correlation of all four analyzed ETFs to the reference ETF declined from the second half of 2014 onward, with the REIT ETF showing the strongest decoupling. The Forecast statistic is the expected value of the rolling correlation one month forward, or in February 2016 in this example.

We hope that the Multi-Correlation service will become a useful tool for investors who want to construct well-diversified portfolios. If you would like to use this and other Alpholio™ services, please register on our website.


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Merger Arbitrage Funds as Portfolio Diversifiers
alternatives, analysis, app, asset allocation, correlation, portfolio

A recent article in The Wall Street Journal’s Investing in Funds & ETFs report discusses merger arbitrage mutual funds. According to the article, such funds

…may offer an attractive way to diversify away from the risks of stocks or bonds …[but] can’t replace bonds, because their returns aren’t certain and come mostly through any price appreciation, not yield. But held in tandem with bonds, they can offer a way to hedge against interest-rate risk and might cushion part of a portfolio against stock-market volatility

Let’s take a closer look at these statements with the help of a recently introduced Alpholio™ App for Android, and specifically its Portfolio, Correlation, Total Return and Efficient Frontier services. For the purposes of this analysis, the base portfolio consists of 60% SPDR® S&P 500® ETF (SPY) and 40% of the iShares Core U.S. Aggregate Bond ETF (AGG), i.e. a traditional balanced mix of stocks and bonds. Here is the baseline chart with statistics generated from total monthly returns of both ETFs and quarterly rebalancing of the portfolio:

Portfolio 60% SPY + 40% AGG

The reason why the beta of this portfolio is not exactly 0.6 (i.e. equal to the 60% weight of the SPY) is threefold. Alpholio™ uses a broader definition of “the market” than just the S&P 500® index. Also, the correlation between the market and AGG is not zero. Finally, the portfolio is rebalanced quarterly, not monthly, which can lead to a temporary divergence of SPY/AGG weights from the original 60/40% level.

For reference, in the same time frame a portfolio consisting of just the SPY would have an annualized return of 8.52% with a standard deviation of 14.25%, Sharpe ratio of 0.55 and maximum drawdown of 50.8%. Adding AGG to such an equity-only portfolio decreases its return but reduces its volatility even more, thus improving the Sharpe ratio. The maximum drawdown is also significantly diminished.

The article quotes two merger arbitrage funds with substantial assets: The Merger Fund® (MERFX) and The Arbitrage Fund (ARBFX). To effectively diversify the balanced portfolio, should either fund replace a portion of stocks, a portion of bonds, or a combination of both? What should be the extent of such a replacement?

To answer the first question, let’s take a look at the correlation between SPY, AGG and either fund using the Correlation service of the Alpholio™ app. Here is a chart of the rolling 12-month correlation coefficient for monthly returns of SPY and MERFX:

Correlation SPY - MERFX

The starting date of the chart stems from the earliest availability of AGG whose first full monthly return was in October 2003. The average correlation of 0.56 indicates that MERFX was a marginal diversifier for SPY (generally, a correlation of 0.6 or less is desirable). Here is a similar chart for AGG and MERFX:

Correlation AGG - MERFX

The average correlation of just below zero indicates that MERFX was a much better diversifier for AGG than SPY. Similarly, the average correlation between SPY and ARBFX was about 0.42 and virtually zero between AGG and ARBFX. Therefore, to effectively diversify the base portfolio, it should generally be better to allocate more of SPY rather than AGG to MERFX or ARBFX. However, this would also suppress portfolio returns — as the following total return chart shows, MERFX and ARBFX had steadier but smaller cumulative returns than SPY:

Total Return of SPY, MERFX and ARBFX

To answer the second question: a portfolio with the highest Sharpe ratio (i.e. the tangency portfolio) would be mostly composed of AGG and MERFX. Here is an efficient frontier chart in which the current portfolio, depicted by a standalone marker inside the frontier, had 80% in AGG and 20% in MERFX but no SPY and was very close to the tangency portfolio:

Efficient Frontier 0% SPY + 80% AGG + 20% MERFX

Adding MERFX at the expense of SPY decreased the portfolio volatility and increased its Sharpe ratio, but resulted in lower returns. To illustrate this further, here is a chart and statistics for a portfolio that consisted of 45% SPY, 40% AGG and 15% MERFX, rebalanced quarterly:

Portfolio 45% SPY + 40% AGG + 15% MERFX

Ultimately, it is up to the investor to trade off portfolio returns for risk — some may choose to optimize for the highest return per unit of risk, while others may strive for higher returns at the expense of a sub-optimal Sharpe ratio. The Alpholio™ app for Android provides a set of tools that facilitate the exploration of historical data and construction of desired portfolios, with the usual caveat that the past performance is not a guarantee of future results.


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Finding Star Fund Managers
active management, active share, correlation, performance persistence

With the new Fund Manager of the Year awards from Morningstar, a question of future performance of star fund managers inevitably arises. Apparently, the awards carry little short-term predictive value:

While our Fund Manager of the Year awards are recognition of past contributions rather than predictions of future results, we’re confident in each one’s long-term prospects because of their deep research resources and willingness to stick with their discipline in good times and bad. We wouldn’t expect repeat performances in 2014, as our winners and their rivals will wrestle with lofty equity valuations, policy-related volatility, a still-recovering economy, and the specter of rising rates.

Indeed, statistics on award winners presented by an article in The Wall Street Journal are not too encouraging:

Subsequent Period Beating the Benchmark
1 year 58%
3 years 45%
5 years 56%
10 years 65%

Generally, the best long-term predictors of fund outperformance remain a low expense ratio (fees), minimal portfolio turnover (a proxy for trading costs), and divergence from benchmark index weightings. The latter measure, known as active share should be high:

Prof. Cremers says the best funds tend to have active-shares percentages that are at least 60%. Large-stock managers should ideally have an active share above 70%. Midcap managers should have active share above 85% and small-cap managers should exceed 90%, he says.

Unfortunately, the proportion of funds with such big active shares has been falling over the years, which gave rise to “closet indexing,” as a chart from a Lazard Research study demonstrates:

Rise in Closet Indexing

The active share in the aggregate portfolio of actively-managed U.S. stock funds has been also declining:

Active Share in Aggregate Portfolio of Active U.S. Stock Funds

However, active is not always a guarantee of strong performance, as shown in an earlier Alpholio™ post.

As for fund fees, an article in The New York Times points out that

The average total expense ratio, which encompasses management fees and operating expenses but not brokerage commissions and other trading costs, is 1.33 percent of assets a year for domestic stock funds and 0.97 percent for domestic bond funds, according to Morningstar.

Over time, these fees add up. According to a paper by William Sharpe, which estimated an average expense ratio of stock funds at 1.12% compared to 0.06% (now 0.05%) for the Vanguard Total Stock Market Index Fund (VTSAX, Admiral Shares):

Whether one is investing a lump-sum amount or a series of periodic amounts, the arithmetic of investment expenses is compelling… Under plausible conditions, a person saving for retirement who chooses low-cost investments could have a standard of living throughout retirement more than 20% higher than that of a comparable investor in high-cost investments.

However, as a Gerstein Fisher study found, the cheapest funds may not always provide the highest returns:

We found that the best performing quintile of funds was the second most expensive quintile (i.e., the 21-40% highest-cost ones), whether we equally weighted funds or asset-weighted them. The consistent results of the study: the cheapest quintile of funds was not the best performing, but the most expensive funds were the worst performing.

Similarly to a low correlation, a factor that is often quoted to aid stock pickers is a high degree of dispersion (measure of spreading) of stock returns. At first blush, it would seem that, just as with active share, an increased dispersion is beneficial. However, all it does is to enlarge the dispersion of active fund returns, without necessarily moving the average, as another article in The Wall Street Journal indicates:

A bigger spread between the best and worst stocks hasn’t helped active funds as a group, but it does tend to make good funds better—and bad funds worse… To put it another way: Markets that aren’t moving in lock step give active managers more rope with which to climb above the pack or to hang themselves.

While a low cost and small turnover coupled with a significant active share are generally good screening criteria, funds clearly have trouble with performance persistence. As our analyses have repeatedly demonstrated, even the star fund managers stumble, so outperformance is fleeting. This is where the Alpholio™ methodology helps by showing momentum in the smoothed cumulative RealAlpha™ for each analyzed fund, from which buy/sell signals can be derived. To learn more, please visit our FAQ.

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REIT Correlations with Stocks
alternatives, analysis, correlation, portfolio

Traditionally, real-estate investment trusts (REITs) provided a good diversification to other stocks in a portfolio. However, in the last several years, REIT returns have become highly correlated with returns of other equities. One theory, outlined in a Morningstar article, is that over the years REITs evolved from a small, illiquid and neglected to a mainstream and easility accessible asset class.

As an article in The Wall Street Journal indicates

From 1980 through 2006, stock performance of REITs moved in tandem with the broader market only 47% of the time, according to an analysis for The Wall Street Journal by Citi Private Bank in New York… Since then, as the bank’s research shows, REIT correlations have jumped to nearly 80%, erasing more than a quarter of a century in decoupling.

To illustrate that, Alpholio™ compiled the following chart of correlation between returns of the SPDR® S&P 500® ETF (SPY) and iShares U.S. Real Estate ETF (IYR):

SPY-IYR Correlation 3-4 Years

The chart shows rolling correlations in trailing three- and four-year periods using total monthly returns of both ETFs since mid-2000. (As expected, thanks to a larger number of data points the latter curve is a bit smoother but lags the former one.) Either curve is characterized by four distinct phases:

  • Through 2006, the correlation was indeed in the mid-40%
  • From 2007 through 2008, the correlation gradually increased to about 70% and abruptly jumped to over 80% at the onset of the financial crisis
  • From 2009 through mid-2013, the correlation stayed at about 85%
  • Afterwards, the correlation decreased started to decrease.

The last two phases were caused, at least in part, by the Federal Reserve’s interest rate policy: a strong coupling of rising returns stimulated by low rates, followed by an indication of decoupling when rates rose. A better economic outlook is also a factor:

Improving conditions in the broader economy usually lead to lower real-estate correlations… In fact, correlations between the S&P 500 and REITs have dropped by about 10% since late last year.

Let’s take a look at the last phase in more detail, this time using trailing 18- and 24-month returns:

SPY-IYR Correlation 18-24 Months

Here, thanks to shorter time windows the degree of decoupling in the last phase is more evident: the correlation reverted to about 50%. This would suggest that REITs might once again help with portfolio diversification. However, as the next chart shows, REIT returns are currently negatively correlated with the interest rate on a 10-year Treasury note:

IYR - 10-Year T-Note Rate Correlation

With the prospect of rising interest rates this year, REIT returns are likely to continue to be depressed. At the same time, many analysts forecast 5-10% returns of the overall equity market (for example, S&P just increased its 12-month target for the S&P 500® index from 1895 to 1940, which implies an approx. 7% total return). Therefore, until interest rates stabilize, it may be too early to declare a structural decrease in correlation of REIT returns to those of other stocks. A permanent return to pre-2007 correlation levels would certainly help with portfolio construction.

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Hedge Fund Stats
correlation, hedge fund

An article in Barron’s points out a disconnect between assets and returns of hedge funds:

As of Sept. 30, the industry managed a record $2.51 trillion in assets, according to the analysts at Hedge Fund Research. That’s also a huge recovery from the depths of the financial crisis, when the funds’ $1.87 trillion in assets fell by $400 billion.

The HFRI Fund Weighted Composite Index, which covers a wide range of strategies, was up only 5.5% from Jan. 1 to Sept. 30, while the S&P 500 rose 19.79%. The poor showing was no better than during the 10 years ended on Sept. 30, when the index, compiled by Hedge Fund Research, was up only 5.92% on an annualized basis.

Annualized returns for other periods to September 30 compiled by Hedge Fund Research (HFR) are also unimpressive:

Index 1-Year 3-Year 5-Year
HFRI Fund Weighted Composite Index 7.05% 3.85% 5.01%
HFRI Equity Hedge [EH] (Total) Index 11.08% 4.61% 5.22%
HFRI Event-Driven (Total) Index 12.32% 6.22% 6.85%
HFRI Macro (Total) Index -2.92% -0.60% 1.83%
HFRI Relative Value [RV] (Total) Index 7.18% 6.13% 7.70%
HFRI Emerging Markets (Total) Index 6.66% 0.22% 4.28%
HFRI Fund of Funds Composite Index 6.58% 2.50% 1.95%
HFRI EH: Short Bias Index [lowest] -17.06% -13.02% -12.79%
HFRI RV: Fixed Income-Asset Backed Index [highest] 10.19% 10.60% 12.01%

Not surprisingly, in the environment of low interest rates and modest economic recovery, the short-biased funds had the worst and the fixed income funds had the best performance in the past five years.

Meanwhile, the compensation of hedge fund personnel increased more in line with assets under management rather than performance. Per a Barron’s blog post, the 2014 Glocap Hedge Fund Compensation Report states the following figures:

A new study contends an entry-level analyst at a middling large hedge fund is taking home $353,000 this year. The figure, which includes salary and bonus, rises to $2.2 million for the average portfolio manager of a large fund. Overall, average compensation in the industry gained between 5% and 10% for the year.

This surely contributed to the huge increase in the number of hedge funds: from 2,392 in 1996 to 8,201 at present (567 more than before the financial crisis).

The influx of money into hedge funds is caused by institutional investors that, according to the 2013 Glocap Report, account for 77% of capital compared to only 12% contributed by high-net-worth individuals and family offices. The main reason is that after the financial crisis institutions want to minimize losses during market downturns, while sacrificing the upside during market rebounds (in 2008, the HFRI composite index fell 19.03%, while the S&P 500® lost 37.0%).

Also, hedge fund returns are supposed to exhibit low correlation with those of the market, which leads to improvement of return-to-risk characteristics of the investment portfolio. However, as the following chart from the HFR presentation to the US Dept. of Labor shows, historical correlation of the HFRI composite index to the S&P 500® has been quite high:

HFRI Correlation to S&P 500® (12-Month Rolling Window)

Furthermore, the correlation of the equity hedge fund index to the S&P 500® has been on the rise for a long time, which makes it very difficult for such funds to beat that benchmark:

Correlation of HFRI Equity Hedge Index with S&P 500&reg (Rolling 60 Months) Jan 1990 - Oct 2011

In sum, while some, especially smaller, hedge funds have attractive characteristics, performance of the overall industry leaves a lot to be desired.

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Correlations of Factor ETFs
correlation, exchange-traded fund, factor investing

BlackRock has recently introduced a set of four iShares ETFs that follow factor indices. They are:

The first three of these ETFs debuted on April 16, 2013, while the fourth one three months later. Therefore, as of this writing, there are only 91 and 28 trading day data available for these ETFs, respectively. Traditionally, at least three years worth of data (a minimum of 36 monthly data points) are required to calculate a return correlation between two investments. However, it may be helpful to take an early look on how the return correlations among these ETFs and the iShares Core S&P 500 ETF (IVV) are shaping up so far:

ETF VLUE SIZE MTUM QUAL IVV
VLUE 1.00
SIZE 0.37 1.00
MTUM 0.48 0.66 1.00
QUAL 0.55 0.42 0.74 1.00
IVV 0.53 0.55 0.85 0.71 1.00

Since daily returns are assumed to contain a substantial amount of “noise,” and the observation period is very limited, the above figures certainly cannot be considered very reliable. However, there is an early indication that the majority of correlations are lower than 0.6, which should aid in portfolio diversification. A research paper from BlackRock shows that idealized zero-net-investment factor portfolios constructed using Fama-French approach* can have much lower long-term correlations:

BlackRock Factor Correlations

*MktRf = market, SMB = size, HML = value, CME = quality.

Only time will tell whether these new factor ETFs provide low inter-correlations and sufficient returns to truly benefit an investment portfolio. However, early signs are encouraging.

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On Factor Investing
factor investing

Quite a few of recent industry articles focus on factor investing.

Rick Ferri has a two-part article on the topic, with the first part covering the history of multi-factor models, and the second part delving into more practical considerations. According to the author, factor investing has the following benefits:

  • Outsized performance (returns) compared to a single-factor (market) portfolio, e.g. as historically observed for small-cap stocks
  • Combination of uncorrelated factors leads to a higher risk-adjusted performance of the portfolio
  • Intellectual enrichment and academic stimulation that stems from studying of multi-factor models.

The author also points out the disadvantages of factor investing:

  • Cost of factor vehicles [although the actual expense ratio of VTI is 0.05% and not the cited 0.15%]
  • Historical lack of risk premium persistence of factors such as size.

The author comes to the same conclusions about factor-based products from DFA as Alpholio™ already did in one of the previous posts. However, in his zeal to defend pure market-based factor investing, the author confuses terms:

Finally, tracking error is the name give [sic] to a strategy that falls short of a market benchmark. It could mean the downfall for many multifactor investors.

A tracking error of a factor investment vehicle is in reference to the theoretical index of this vehicle and not to the market benchmark. For example, a momentum factor does not purport to track the S&P 500® index.

The author goes on to combat the term “smart beta” in his post on InvestmentNews. Despite a more pragmatic approach from Arnott, the author quotes noted academics (Sharpe, Fama, and French) to instill the message of beta purity, :

I believe the original definitions are best left unchanged. Beta is non-diversifiable market risk, other return dimensions are defined as additional risk factors, and putting these risks together in a portfolio is multifactor investing.

In the end, does it really matter if factor coefficients in a multiple regression are labeled beta1, beta2, …, or beta, gamma, delta, …? Sure, “smart beta” may sound like a marketing gimmick from fund providers to peddle their latest products, but it is a simple way of conveying the difference of these factors from plain market-cap based ones.

Finally, an article in Morningstar focuses on the scientific background of multifactor investing. It also presents two points of view on factors: from the perspectives of efficient and not perfectly rational market. The author leans toward the second interpretation, which is supposedly supported by the following “evidence:”

It’s also hard to reconcile them all [factors] as representing risk because if you lump them all together, you get an eerily smooth return stream.

The reason for this smoothing is that excess returns of factors are generally un- or low-correlated, and thus tend to cancel “bumps” in portfolio returns. This does not mean that each factor does not represent risk.

The author then proceeds to cover the problem of redefinition of alpha, which results from the introduction of multiple factors, and concludes that from his perspective such an adjustment is inappropriate because it “redefines outperformance:”

From my perspective, the mountains of studies purporting to show that active equity managers can’t beat the market are really showing that much of their excess returns can be replicated by a handful of factor strategies.

Regardless of semantics and opinions, if a manager’s excess returns can be replicated by cheaper and readily accessible instruments (such as factor ETFs), then there is no need to pay the excess management fee.

From Alpholio™’s perspective, all of the above discussions are academic. Whether or not factors have persistent risk premia, market is efficient or not perfectly rational, what truly matters is whether or not active management adds value over a reference portfolio after all fees have been taken into account. Factor ETFs provide yet another set of potential explanatory variables that squeeze the alpha to its essence, the RealAlpha™.

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Alternatives vs. Bonds
alternatives, correlation

In light of a recent downturn in bonds caused by a perception of the Fed’s upcoming actions, a Barron’s blog post and a Morningstar article explore alternative investments with “bond-like” returns. However, it turns out that these alternatives behave mostly like stocks with poor return-to-risk characteristics, and thus do not provide diversification to a broader portfolio.

To illustrate, here are correlations to stocks and Sharpe Ratios derived from Morningstar’s statistics for mutual funds and ETFs mentioned in the post and article:

Fund Ticker Category Beta StDev Correlation Sharpe Ratio
S&P 500® SPX Stock Index 1.00 13.56 1.00 1.32
IQ Merger Arbitrage ETF MNA Market Neutral 0.26 5.08 0.69 0.37
Merger MERFX Market Neutral 0.12 2.63 0.62 1.07
Robeco Boston Partners L/S Rsrch BPRRX Long/Short N/A N/A N/A N/A
IQ Alpha Hedge Strategy IQHOX Multialternative 0.31 6.54 0.64 0.36

These three-year statistics indicate a high positive correlation to stocks coupled with sub-par risk-adjusted returns. This observation is corroborated by a new study from the Leuthold Group cited in The Wall Street Journal article that states:

“From 1994 through May, it found that hedge-fund correlations have slowly been inching up to 0.75, almost 36% higher than earlier levels. Since a measure of 1.00 represents lock-step movements, hedge fund returns are generally following the tendencies of stocks about three-quarters of the time… Funds with correlations to stocks of 0.6 or less are prized by investors since they can significantly reduce portfolio volatility and limit risks over full-market cycles.”

In the past month or so, these alternative funds held their value well relative to bond investments. This is supported by their negative or low positive three-year correlations to iShares Core Total U.S. Bond Market ETF (AGG), as estimated by Alpholio™:

Fund Ticker Correlation
IQ Merger Arbitrage ETF MNA -0.30
Merger MERFX -0.24
IQ Alpha Hedge Strategy IQHOX 0.27

For reference, the correlation of SPDR® S&P 500® ETF (SPY) to AGG over the same period is -0.33. Therefore, these alternatives do not provide a significant amount of diversification to a balanced equity-and-bond portfolio, but could be marginally helpful if the portfolio contains only bonds. However, even in the latter case they could be a drag on the risk-adjusted performance of the portfolio: at 1.31, the Sharpe Ratio of SPY is higher than that of any of the above funds.

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