Analysis of Alpha Architect ETPs
analysis, exchange-traded product, factor investing

A recent article in The Wall Street Journal profiles the CEO of Alpha Architect LLC, an upstart active investment manager. The firm currently advises five exchange-traded products (ETPs). Four of these ETPs have a sufficiently long history to be analyzed using Alpholio™’s patented methodology.

All of the following analyses employ the simplest variant of the methodology. For each analyzed ETP, the variant constructs a reference portfolio of up to six ETFs that most closely tracks periodic returns of the ETP. Both the membership and weights of ETFs in the reference portfolio are fixed over the entire analysis period.

Let’s start with the ValueShares U.S. Quantitative Value ETF (QVAL). Here is a chart of the cumulative RealAlpha™ for this ETP (to learn more about this and other performance measures, please visit our FAQ):

Cumulative RealAlpha™ for ValueShares U.S. Quantitative Value ETF (QVAL)

The ETP produced a significantly lower cumulative return than that of its reference ETF portfolio. It also had a higher volatility due to a relatively small number of deep-value holdings. This was also reflected in a considerably elevated RealBeta™, assessed against a broad-based domestic equity ETF.

The following chart with statistics shows the fixed composition of the reference ETF portfolio for QVAL:

Reference Weights for ValueShares U.S. Quantitative Value ETF (QVAL)

The ETP had equivalent positions in the First Trust Large Cap Value AlphaDEX® Fund (FTA), SPDR® S&P® Retail ETF (XRT), PowerShares S&P SmallCap Information Technology Portfolio (PSCT), iShares North American Tech-Multimedia Networking ETF (IGN), First Trust Industrials/Producer Durables AlphaDEX® Fund (FXR), and iShares U.S. Oil Equipment & Services ETF (IEZ). These ETFs correspond to average exposures QVAL generated over the evaluation period.

Let’s move on to the ValueShares International Quantitative Value ETF (IVAL). Here is a chart of cumulative RealAlpha™ with statistics for this ETP:

Cumulative RealAlpha™ for ValueShares International Quantitative Value ETF (IVAL)

The ETP added significantly more value than its reference ETF portfolio, but only beginning in the second half of last year. This is why the article singles out a recent outperformance of just this product:

…value-focused fund of overseas stocks is beating all its rivals over the past year.

The ETP produced this excess return at the expense of a substantially higher volatility than that of its reference ETF portfolio.

The following chart with associated statistics illustrates the static composition of the reference ETF portfolio for IVAL:

Reference Weights for ValueShares International Quantitative Value ETF (IVAL)

The ETP had equivalent positions in the WisdomTree Japan Hedged Equity Fund (DXJ), Guggenheim CurrencyShares® Australian Dollar Trust (FXA), iShares MSCI South Korea Capped ETF (EWY), iShares MSCI Spain Capped ETF (EWP), WisdomTree Japan SmallCap Dividend Fund (DFJ), and iShares MSCI Germany ETF (EWG).

Next, let’s take a look at the MomentumShares U.S. Quantitative Momentum ETF (QMOM). Here is a chart of the cumulative RealAlpha™ with statistics for this ETP:

Cumulative RealAlpha™ for MomentumShares U.S. Quantitative Momentum ETF (QMOM)

The ETP failed to outperform its reference ETF portfolio of somewhat lower volatility.

The following chart with related statistics depicts the constant composition of the reference ETF portfolio for QMOM:

Reference Weights for MomentumShares U.S. Quantitative Momentum ETF (QMOM)

The ETP had equivalent positions in the PowerShares DWA Industrials Momentum Portfolio (PRN), Global X Social Media ETF (SOCL), aforementioned DFJ, PowerShares NASDAQ Internet Portfolio (PNQI), PowerShares Dynamic Leisure and Entertainment Portfolio (PEJ), and PowerShares DWA SmallCap Momentum Portfolio (DWAS).

Finally, let’s evaluate the MomentumShares International Quantitative Momentum ETF (IMOM). Here is the cumulative RealAlpha™ chart with statistics for this ETP:

Cumulative RealAlpha™ for MomentumShares International Quantitative Momentum ETF (IMOM)

The ETP significantly underperformed its reference ETF portfolio in terms of both the cumulative return and volatility. However, its RealBeta™ was well below that of the market.

The following chart with accompanying statistics shows the invariant composition of the reference ETF portfolio for IMOM:

Reference Weights for MomentumShares International Quantitative Momentum ETF (IMOM)

The ETP had equivalent positions in the iShares Mortgage Real Estate Capped ETF (REM), VanEck Vectors Vietnam ETF (VNM), iShares U.S. Medical Devices ETF (IHI), aforementioned FXA, Guggenheim CurrencyShares® Japanese Yen Trust (FXY), and aforementioned SOCL.

It should be noted that all of the above ETPs except for QVAL have traded at a considerable premium to their net asset value (NAV). For example, as of this writing, IMOM’s one-year price return was 8.50% compared to a 3.10% NAV return. Such pricing discrepancies could partially explain the presence of REM (a domestic real-estate fund) and IHI (a domestic medical device fund), in the reference ETF portfolio for IMOM.

In sum, the majority of Alpha Architect ETPs have so far delivered unimpressive results after a comprehensive adjustment for volatility and exposures. Since the oldest product has less than three years of history, only time will tell whether the performance of these ETPs vs. their reference ETF portfolios will eventually improve. The challenge of any factor investing, including value and momentum, is not only the cyclical variation of performance but also the selection of individual securities to implement the factor.

To learn more about the Alpha Architect and other ETPs, please register on our website.


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Do iShares Smart Beta ETFs Outperform? (Part II)
analysis, exchange-traded fund, factor investing

In the first part of this post, we analyzed a couple of iShares smart beta ETFs, the iShares Edge MSCI USA Size Factor ETF (SIZE) and the iShares Edge MSCI USA Value Factor ETF (VLUE).

Let’s start the second part with the evaluation of the iShares Edge MSCI USA Momentum Factor ETF (MTUM). Its issuer states that this ETF generates

Exposure to large- and mid-cap U.S. stocks exhibiting relatively higher price momentum

As before, the analysis will start in the first full month of the ETF’s existence and end in July 2016. Here is the cumulative RealAlpha™ chart with related statistics for the ETF:

Cumulative RealAlpha™ for iShares Edge MSCI USA Momentum Factor ETF (MTUM)

The ETF produced a return comparable to that of its reference portfolio, which had a lower volatility. The RealBeta™ of the ETF was considerably below than that of a broad-based equity market ETF.

The following chart and associated statistics show the constant composition of the reference ETF portfolio for the iShares Edge MSCI USA Momentum Factor ETF:

Reference Weights for iShares Edge MSCI USA Momentum Factor ETF (MTUM)

The ETF had major equivalent positions in the Consumer Staples Select Sector SPDR® Fund (XLP), First Trust Large Cap Growth AlphaDEX® Fund (FTC), Health Care Select Sector SPDR® Fund (XLV), PowerShares Dynamic Large Cap Growth Portfolio (PWB), First Trust Dow Jones Internet Index Fund (FDN), and PowerShares NASDAQ Internet Portfolio (PNQI). (The Other component in the chart collectively represents additional two ETFs with smaller weights.)

Not surpringly, the ETF had a strong tilt toward large-cap growth stocks, especially in the consumer staples and healthcare sectors, as well as the Internet industry. Unlike with the previous iShares smart beta ETFs, no single position was clearly dominant in its reference portfolio. It can also be reasonably expected that in the future, the ETF’s exposure to specific sectors and industries will change along with price momentum shifts. Therefore, for a further performance comparison, a similar smart beta equivalent position should be chosen.

Over the same analysis period, MTUM outperformed FTC and PWB in terms of the annualized return and Sortino ratio, and had an equal or higher Sharpe ratio:

Total Return of iShares Edge MSCI USA Momentum Factor ETF (MTUM), First Trust Large Cap Growth AlphaDEX® Fund (FTC) and PowerShares Dynamic Large Cap Growth Portfolio (PWB)

At 0.15%, the expense ratio of MTUM was much lower than the 0.62% of FTC and 0.57% of PWB, which improved relative returns of MTUM. The average correlation between rolling 24-month returns was 0.95 and 0.96 for MTUM with FTC and MTUM with PWB, respectively.

Finally, we will evaluate the iShares Edge MSCI USA Quality Factor ETF (QUAL). According to the issuer, this ETF produces

Exposure to large- and mid-cap U.S. stocks exhibiting positive fundamentals (high return on equity, stable year-over-year earnings growth and low financial leverage)

Since QUAL’s inception date was in July 2013, the analysis begins in August 2013. Here is a chart with accompanying statistics of the cumulative RealAlpha™ for the ETF:

Cumulative RealAlpha™ for iShares Edge MSCI USA Quality Factor ETF (QUAL)

The ETF moderately outperformed its reference portfolio, which had a slightly higher volatility. The ETF’s RealBeta™ was lower than that of a broad-based equity market ETF.

The following chart with accompanying statistics depicts the composition of the reference portfolio for the iShares Edge MSCI USA Quality Factor ETF:

Reference Weights for iShares Edge MSCI USA Quality Factor ETF (QUAL)

The ETF had major equivalent positions in the iShares Russell Top 200 Growth ETF (IWY), SPDR® Dow Jones® Industrial Average ETF (DIA), PowerShares S&P 500 Quality Portfolio (SPHQ), Vanguard Dividend Appreciation ETF (VIG), PowerShares NASDAQ Internet Portfolio (PNQI), and iShares U.S. Energy ETF (IYE). Clearly, this ETF had a strong tilt toward mega-cap stocks, especially of the growth classification.

Over the same analysis period, QUAL had a significantly lower return as well as slightly smaller Sharpe and Sortino ratios than those of IWY:

Total Return of iShares Edge MSCI USA Quality Factor ETF (QUAL) and iShares Russell Top 200 Growth ETF (IWY)

The average correlation between rolling 24-month returns of the two ETFs was 0.98.

Conclusion

The above analyses uncovered reference portfolios for select iShares smart beta ETFs. While a wholesale substitution of an ETF with its multi-member reference portfolio may not always be practical, each of these portfolios

  • Built a foundation for assessment of the true risk-adjusted performance of a smart beta ETF.
  • Captured exposures of a smart beta ETF to various stock market styles, sectors and industries (paradoxically, these are exposures of the analyzed factor ETF to various other factors). This may help investors avoid an undesirable overlap with other positions in their overall investment portfolios.
  • Identified a predominant exposure of a smart beta ETF to a single factor. This may help investors substitute a smart beta ETF with another product that implements a traditional market-cap index or with a similar strategic beta strategy.

If you would like to use the ETP Analysis Service to examine other smart beta products, please register on our website.


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Do iShares Smart Beta ETFs Outperform? (Part I)
analysis, exchange-traded fund, factor investing

To better demonstrate the new Alpholio™ ETP Analysis Service in action, let’s analyze several of the iShares smart beta ETFs. The oldest of these products were introduced in mid-April 2013, so by now more than three years of performance data are available. According to their issuer

Smart beta ETFs can help investors achieve goals like reducing risk, generating income, or potentially enhancing returns. These funds primarily focus on factors – broad, persistent drivers of returns across equities and other asset classes. New technologies have made it easier to target factor exposures, which investors can access with iShares Edge ETFs.

Due to the scope of analysis, this post will be divided into two parts. We will start with the iShares Edge MSCI USA Size Factor ETF (SIZE). According to its issuer, this ETF provides

Exposure to large- and mid-cap U.S. stocks with a tilt towards the smaller, lower risk stocks within that universe

Here is a chart with related statistics of the cumulative RealAlpha™ for this ETF from May 2013 (the first full month of returns since its inception) through July 2016:

Cumulative RealAlpha™ for iShares Edge MSCI USA Size Factor ETF (SIZE)

The ETF returned effectively as much as its reference ETF portfolio that had a slightly lower volatility. The ETF’s RealBeta™, measured against a broad-based US equity index ETF, was close to one.

The following chart shows the constant composition of the reference ETF portfolio for the iShares Edge MSCI USA Size Factor ETF:

Reference Weights for iShares Edge MSCI USA Size Factor ETF (SIZE)

The ETF had equivalent positions in the SPDR Russell 3000® ETF (THRK), SPDR® Dow Jones® REIT ETF (RWR), SPDR® S&P® Insurance ETF (KIE), iShares U.S. Medical Devices ETF (IHI), IQ Hedge Multi-Strategy Tracker ETF (QAI), and Utilities Select Sector SPDR® Fund (XLU).

Over the same analysis period, SIZE outperformed THRK, the dominant position in its reference portfolio, in terms of a slightly larger annualized return, as well as higher Sharpe and Sortino ratios:

Total Return of iShares Edge MSCI USA Size Factor ETF (SIZE) and SPDR Russell 3000® ETF (THRK)

The average correlation between rolling 24-month returns of the two ETFs was 0.96.

The second smart beta ETF we will evaluate is the iShares Edge MSCI USA Value Factor ETF (VLUE). According to the issuer, this ETF supplies

Exposure to large- and mid-cap U.S. stocks with lower valuations based on fundamentals

Here is a chart with related statistics of the cumulative RealAlpha™ for this ETF:

Cumulative RealAlpha™ for iShares Edge MSCI USA Value Factor ETF (VLUE)

Since late 2014, the ETF failed to add value over its reference portfolio that had a slightly lower volatility. The ETF’s RealBeta™ was higher than that of a broad-based stock market ETF.

The following chart shows the fixed reference ETF portfolio for the iShares Edge MSCI USA Value Factor ETF:

Reference Weights for iShares Edge MSCI USA Value Factor ETF (VLUE)

The ETF had major equivalent positions in the SPDR® S&P® 500 Value ETF (SPYV), SPDR® Morgan Stanley Technology ETF (MTK), iShares U.S. Broker-Dealers & Securities Exchanges ETF (IAI), First Trust Large Cap Value AlphaDEX® Fund (FTA), iShares U.S. Healthcare Providers ETF (IHF), and iShares Transportation Average ETF (IYT). The Other component in the chart collectively represents two additional ETFs with smaller weights.

Although VLUE had a slightly higher annualized return than SPYV (the prevailing ETF in its reference portfolio), it underperformed SPYV in terms of both Sharpe and Sortino ratios:

Total Return of iShares Edge MSCI USA Value Factor ETF (VLUE) and SPDR® S&P® 500 Value ETF (SPYV)

The average correlation between rolling 24-month returns of the two ETFs was 0.98.

The second part of this post will cover two other iShares smart beta ETFs, the iShares Edge MSCI USA Momentum Factor ETF (MTUM) and iShares Edge MSCI USA Quality Factor ETF (QUAL).


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Growth vs. Value
analysis, app, asset allocation, exchange-traded fund, factor investing

In one of the previous posts, Alpholio™ made the case for increasing the mid-cap stock holdings in the portfolio. As promised, in this follow-on post, we will examine the performance of growth vs. value equities.

A recent article on this topic in The Wall Street Journal states that

Over the past year, the average U.S. large-cap growth fund has risen 18.2%, while the average U.S. large-cap value fund is up 10.4%… from 2003 through 2013, the average gap between the two styles of stock-picking for large-cap stocks was 0.75 percentage point… it’s a similar story among small-company stocks, where growth-stock funds […] are up 16% over the past year. Funds investing in small-cap value stocks […] are up 7.7%.

The trend of growth equities outperforming value equities is hardly a past-year phenomenon. Contrary to what might be expected, this trend is also not confined to the last seven years since the market’s trough during the financial crisis. The trend is best examined using specific ETFs as opposed to hypothetical and unspecified “average U.S. [mutual] funds.”

To start with, let’s use the Total Return service of the Alpholio™ App for Android to review the long-term performance of a couple of long-lived large-cap ETFs, the iShares S&P 500 Growth ETF (IVW) and iShares S&P 500 Value ETF (IVE), from their inception through March 2015, using monthly total returns:

Total Return of iShares S&P 500 Growth ETF (IVW) and iShares S&P 500 Value ETF (IVE) from 2000 to 2015

In that period, the large-cap value ETF handily outperformed its growth counterpart, albeit with a slightly higher standard deviation (a measure of volatility of returns). However, this only paints a part of the picture: in 2000, growth stocks significantly underperformed, following the deflation of the dot-com bubble. If the start of the analysis period is advanced to the beginning of 2001, growth slightly outperformed value:

Total Return of iShares S&P 500 Growth ETF (IVW) and iShares S&P 500 Value ETF (IVE) from 2001 to 2015

Through the market peak in October 2007, growth stocks did not advance as much as value ones did, but they suffered a much smaller drawdown (45.4% for growth vs. 56.7% for value, as calculated by the Portfolio service).

The growth outperformance becomes even more pronounced when the beginning of the analysis is moved to April 2005 for a 10-year evaluation period:

Total Return of iShares S&P 500 Growth ETF (IVW) and iShares S&P 500 Value ETF (IVE) from 2005 to 2015

Large-cap growth stocks returned about 2% more than their value counterparts, and did so with much smaller volatility. As shown by the Rolling Returns service, in the same period growth outperformed value in about 90% of all rolling 36-month intervals, 67% of 24-month intervals, and 63% of 12-month intervals:

Rolling Returns of iShares S&P 500 Growth ETF (IVW) and iShares S&P 500 Value ETF (IVE) from 2005 to 2015

The median difference of rolling 12-month returns over the last 10 years was over 2.6% in favor of growth.

For mid-cap stocks, let’s use the iShares S&P Mid-Cap 400 Growth ETF (IJK) and iShares S&P Mid-Cap 400 Value ETF (IJJ). As with large-caps, the 10-year performance of growth mid-caps was better than that of their value peers:

Total Return of iShares S&P Mid-Cap 400 Growth ETF (IJK) and iShares S&P Mid-Cap 400 Value ETF (IJJ) from 2005 to 2015

Finally, a similar chart for the iShares S&P Small-Cap 600 Growth ETF (IJT) and iShares S&P Small-Cap 600 Value ETF (IJS) also demonstrates the growth superiority over value:

Total Return of iShares S&P Small-Cap 600 Growth ETF (IJT) and iShares S&P Small-Cap 600 Value ETF (IJS) from 2005 to 2015

It is worth noting that the outperformance of growth stocks over value ones in this analysis period appears to directly contradict the value effect in the classic three-factor model. However, the latest research from Fama-French indicates that this factor is less important in the presence of the beta, size, profitability and investment factors.

To see all the Alpholio™ App for Android services in action, download the app from

Get It on Google Play

© 2015 Envarix Systems Inc. All Rights Reserved.

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Entering an Exclusive Dimension
analysis, factor investing, mutual fund, value investing

A cover story in Barron’s provides lots of interesting details about the history and operations of Dimensional Fund Advisors (DFA). Founded in 1981, DFA has recently reached $332 billion in assets under management (AUM).

DFA's Three Decades of Growth

About 78% of these AUM are in stocks, and about 85% in low-cost mutual funds with an average expense ratio of 0.39%. The funds have a small-cap and value tilt, based on the Fama-French three-factor model. Lately, the firm started to augment its funds with a profitability factor.

The article states that

More than 75% of its funds have beaten their category benchmarks over the past 15 years, and 80% over five years, according to Morningstar — remarkable for what some investors wrongly dismiss as index investing.

To substantiate this, the article compares two similar funds from DFA and Vanguard:

For example, take the Vanguard Small Cap Value index fund (VISVX), which is based on the S&P 600 Small Cap Value index and is the counterpart to Dimensional’s DFA US Small Cap Value (DFSVX). The DFA fund has a much smaller tilt — its average market value is $1.1 billion, versus Vanguard’s $2.7 billion — and on all measures is much more value-oriented. So the Dimensional fund better captures the market-beating advantage of small and value stocks. In fact, a lot better: The DFA fund returned 42% in 2013, beating 88% of its peers in Morningstar’s small-cap value category, versus the Vanguard fund’s 36% return, which beat just 53%. Over 15 years, which includes periods that were less favorable to small and/or value stocks, DFA’s fund returned an average of 12% a year, beating 80% of peers. The Vanguard fund returned 10% on average, beating just 37% of peers. The Dimensional fund costs twice as much as Vanguard’s — 0.52% versus 0.24% — but the significant outperformance more than makes up for that difference.

That only tells a part of the story. According to Morningstar data, DFSVX had a lower Sharpe Ratio than VISVX in the 3-year (0.96 vs. 1.01) and 10-year (0.47 vs. 0.48) periods through 2013. This is also reflected in the generally higher volatility and upside and downside capture ratios for the DFA fund. As a result, the DFA fund produced lower returns than the Vanguard fund did in the down years of 2007, 2008 and 2011.

The article says that a deliberately paced trading as well as market making in the 14,000 stocks DFA owns both add to its outperformance. However, DFA faces an ongoing criticism: since its funds are sold exclusively through 1,900 rigorously screened and trained financial advisors, they are not easily accessible to individual investors, especially those with a small amount of investable assets, not willing to pay advisory fees or already having an unaffiliated advisor. This is what creates an “exclusive dimension” of DFA, which Alpholio™ can help investors enter. Following up on one of the previous posts, let’s analyze DFSVX in more detail.

The following chart shows the relative performance of the fund vs. its reference portfolio of ETFs:

Cumulative RealAlpha™ for DFSVX

An investor who committed to the fund in early 2005 would have gained only a modest amount of cumulative RealAlpha™ by late 2013. This was mostly caused by the fund’s underperformance in the three years mentioned above. In addition, at about 22.7% the annualized volatility of the fund was 2% higher than that of its reference portfolio in the overall analysis period.

The next chart illustrates ETF weights in the reference portfolio in the same period:

Reference Weights for DFSVX

The fund could effectively be emulated by a collection of just four related ETFs: iShares Russell 2000 Value (IWN; average weight of 34.9%), iShares S&P Small-Cap 600 Value (IJS; 30.1%), iShares Morningstar Small-Cap (JKJ; 18.5%), and iShares Morningstar Small-Cap Value (JKL; 13.7%). (The remaining two ETFs accounted for only 2.8% of the reference portfolio on average.)

The weighted expense ratio of these four ETFs is currently only 0.33% compared to the fund’s 0.52%. In addition, while an investor trading these ETFs might incur some commission, spread and premium/discount costs, he/she would not have to pay a recurring advisory fee of about 1% (or be forced to switch advisors) to gain benefits similar to those offered by DFA funds. Over time, dedicated factor ETFs will likely make such fund substitution even easier. Thus, entering an exclusive dimension of factor investing is no longer as hard as it has been.

To get a unique perspective on the DFA and other funds, please register on our website.


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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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