Analysis of Cambria ETFs (Part II)
analysis, exchange-traded fund

The previous post in this two-part series covered the three actively-managed products out of the five Cambria ETFs with a sufficiently long history. This post will focus on the remaining two index ETFs.

Let’s start with the analysis of the Cambria Foreign Shareholder Yield ETF (FYLD). According to the sponsor, the fund follows a proprietary index that

…consists of stocks with high cash distribution characteristics. The initial screening universe for this Index includes stocks in foreign developed countries with marketing capitalizations over $200 million. The Index is comprised of the 100 companies with the best combined rank of dividend payments and net stock buybacks, which are the key components of shareholder yield. The Index also screens for value and quality factors, including low financial leverage.

As in the case of actively-managed Cambria ETFs, the evaluation with begin in the first full calendar month since the fund’s inception and end in July 2016. Here is a chart with related statistics of the cumulative RealAlpha™ for the fund:

Cumulative RealAlpha™ for Cambria Foreign Shareholder Yield ETF (FYLD)

Similarly to its predecessors, the fund failed to outperform its reference ETF portfolio which had a slightly smaller volatility, measured as the standard deviation of monthly returns. The fund’s RealBeta™ was moderately higher than that of a broad-based domestic equity ETF.

The following chart and corresponding statistics show the constant composition of the reference ETF portfolio for the fund over the same period:

Reference Weights for Cambria Foreign Shareholder Yield ETF (FYLD)

The fund had major equivalent positions in the Schwab International Small-Cap Equity ETF (SCHC), WisdomTree International SmallCap Dividend Fund (DLS), First Trust Dow Jones Global Select Dividend Index Fund (FGD), iShares MSCI United Kingdom ETF (EWU), PowerShares DWA Industrials Momentum Portfolio (PRN), and Vanguard FTSE Europe ETF (VGK). The Other component in the chart collectively represents additional five foreign-stock ETFs covering the New Zealand, Japan, Australia, Spain and Mexico markets. The reference weights indicate a significant foreign small-cap equity tilt of the fund.

Lastly, we will evaluate the Cambria Global Value ETF (GVAL). The issuer states that this product implements a proprietary index which

…consists of stocks with strong value characteristics. The Index begins with a universe of 45 countries located in developed and emerging markets. […] The Index next separates the top 25% of these countries as measured by Cambria’s proprietary long term valuation metrics. The Index then screens stocks with market capitalizations over $200 million. The Index is comprised of approximately 100 companies.

The following chart and associated statistics depict the cumulative RealAlpha™ for the fund:

Cumulative RealAlpha™ for Cambria Global Value ETF (GVAL)

Compared to its reference ETF portfolio, the fund added a modest amount of value (mostly in the last four months of the analysis period), although the portfolio had a slightly lower volatility. The RealBeta™ of the fund was substantially higher than that of a broad-based U.S. stock ETF.

The following chart and statistics demonstrate the fixed membership and weights of the reference ETF portfolio for the fund:

Reference Weights for Cambria Global Value ETF (GVAL)

The fund had main equivalent positions in the iShares MSCI Italy Capped ETF (EWI), WisdomTree Europe SmallCap Dividend Fund (DFE), Guggenheim CurrencyShares® Euro Trust (FXE), iShares MSCI Poland Capped ETF (EPOL), iShares Latin America 40 ETF (ILF), and Global X MSCI Greece ETF (GREK). The remaining six ETFs in the above table, spanning the Spain, Brazil and Germany equities as well as international-corporate and emerging-markets bonds, collectively constitute the Other item in the above chart.

Conclusion

One of our previous posts outlined the benefits of similar analyses of iShares smart beta ETFs, which we will not repeat here for brevity. This evaluation of Cambria ETFs provides investors with similar insights.

Just like any other composite investment vehicles, Cambria ETFs change their holdings over time. Therefore, a question arises about the value of an analysis in which a static ETF portfolio is calculated from long-term data. The answer is to use a more advanced variant of Alpholio™ patented methodology, in which the membership of the reference ETF portfolio is still fixed but weights can fluctuate. Such a dynamic portfolio tends to more accurately track the analyzed fund over time.

For example, here is a chart with accompanying statistics of a reference ETF portfolio determined in that manner for the Cambria Shareholder Yield ETF (SYLD):

Reference Weights for Cambria Shareholder Yield ETF (SYLD) - Fine Fit

This gives a more accurate view of the fund’s recent average exposures.

If you would like to use our ETP Analysis Service to investigate similar products, please register on our website.


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Analysis of Cambria ETFs (Part I)
active management, analysis, exchange-traded fund

Cambria currently offers eight ETFs. Of those, five have a history longer than twelve calendar months, which is a minimum Alpholio™ requires to conduct an initial analysis. Of these remaining five products, three are actively-managed and two follow proprietary Cambria indices. This post, the first in a two-part series, focuses on the actively-managed Cambria ETFs. The second part will cover index-based funds.

We will evaluate each fund from the first full month since its inception through July 2016 using the simplest variant of the Alpholio™ patented methodology. This approach constructs a reference ETF portfolio with both fixed membership and weights that most closely tracks the returns of the analyzed fund. In essence, the reference ETF portfolio embodies average core exposures of the analyzed fund to various factors, indices and strategies over the analysis period. Since it constitutes a potential static substitute for the analyzed fund, i.e. it is an investment alternative, it also serves as a relevant performance benchmark for the fund. (Unlike with pure indices that are not investable, this real benchmark accounts for actual implementation costs.)

Let’s start with the oldest product, the Cambria Shareholder Yield ETF (SYLD). According to the firm, this actively-managed fund

…invests in 100 [U.S. listed] stocks with market caps greater than $200 million that rank among the highest in (a) paying cash dividends, (b) engaging in net share repurchases, and (c) paying down debt on their balance sheets.

Here is the resulting chart with statistics of the cumulative RealAlpha™ for the fund (to learn more about this and other performance measures, please visit our FAQ):

Cumulative RealAlpha™ for Cambria Shareholder Yield ETF (SYLD)

The fund did not not add value when compared to a reference ETF portfolio, which had a slightly lower volatility. The RealBeta™ of the fund was slightly higher than that of a broad-based domestic equity ETF.

The following chart with related statistics shows the constant composition of the reference ETF portfolio for the fund over the same evaluation period:

Reference Weights for Cambria Shareholder Yield ETF (SYLD)

The fund had major equivalent positions in the PowerShares BuyBack Achievers Portfolio (PKW; an index-based ETF), WisdomTree MidCap Dividend Fund (DON), Guggenheim S&P 500® Equal Weight Technology ETF (RYT), FlexShares Quality Dividend Index Fund (QDF), PowerShares Dynamic Market Portfolio (PWC), and First Trust Large Cap Value AlphaDEX® Fund (FTA). The Other component in the chart collectively represents additional six ETFs with smaller weights, listed in the above table.

It should be noted that one of the well-known investment analytics firms classifies SYLD into the mid-cap value category. While this may be based on the assessment of the fund’s individual holdings, our analysis shows that the fund had primarily large-cap exposures. As a matter of fact, the fund’s prospectus states that

Although the Fund generally expects to invest in companies with larger market capitalizations, the Fund may invest in small- and mid-capitalization companies.

Next, we will analyze the Cambria Global Momentum ETF (GMOM). According to its issuer, the fund

…intends to target investing in the top 33% of a target universe of approximately 50 ETFs based on measures of trailing momentum and trend. The portfolio begins with a universe of assets consisting of domestic and foreign stocks, bonds, real estate, commodities and currencies.

The following chart with corresponding statistics illustrates the cumulative RealAlpha™ for the fund:

Cumulative RealAlpha™ for Cambria Global Momentum ETF (GMOM)

This fund also failed to add value compared to its reference ETF portfolio of a somewhat lower volatility. However, its RealBeta™ was only about one-third that of the broad-based stock market.

The following chart with associated statistics depicts the fixed composition of the reference ETF portfolio for the fund over the same analysis period:

Reference Weights for Cambria Global Momentum ETF (GMOM)

The fund had major equivalent positions in the PowerShares Build America Bond Portfolio (BAB; an index-based ETF), SPDR® Nuveen S&P High Yield Municipal Bond ETF (HYMB), iShares Edge MSCI USA Quality Factor ETF (QUAL), iShares U.S. Utilities ETF (IDU), PowerShares Dynamic Food & Beverage Portfolio (PBJ), and iShares Global Healthcare ETF (IXJ). As in the previous analysis, the Other item in the chart collectively represents additional six ETFs with smaller weights, listed in the above table.

Finally, we will examine the Cambria Global Asset Allocation ETF (GAA). According to its issuer, the fund

…targets investing in approximately 29 ETFs that reflect the global universe of assets consisting of domestic and foreign stocks, bonds, real estate, commodities and currencies.

The following chart with accompanying statistics demonstrates the cumulative RealAlpha™ for the fund:

Cumulative RealAlpha™ for Cambria Global Asset Allocation ETF (GAA)

The fund moderately underperformed its reference ETF portfolio that had a slightly smaller standard deviation. The RealBeta™ of the fund was approximately the same as that of a traditional 60% stock / 40% bonds balanced portfolio.

The following chart and statistics show the composition of the reference ETF portfolio for the fund over the same period:

Reference Weights for Cambria Global Asset Allocation ETF (GAA)

The fund had major equivalent positions in the iShares MSCI Kokusai ETF (TOK), PowerShares DB Commodity Index Tracking Fund (DBC), FlexShares iBoxx 5-Year Target Duration TIPS Index Fund (TDTF), iShares U.S. Real Estate ETF (IYR), VanEck Vectors Emerging Markets High Yield Bond ETF (HYEM), and SPDR® Barclays Investment Grade Floating Rate ETF (FLRN). The remaining ETFs in the above table constitute the Other element in the chart.

It has to be noted that GMOM and GAA are relatively new products with only about 18 months of available history as of this writing. Typically, Alpholio™ uses at least 36 months of data for a more accurate analysis, which was the case with SYLD.

The second part of this series will review the Cambria Foreign Shareholder Yield ETF (FYLD) and the Cambria Global Value ETF (GVAL).

If you would like to use our ETP Analysis Service to examine similar products, 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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Introducing ETP Analysis Service
active management, analysis, exchange-traded fund, exchange-traded product, portfolio

Alpholio™ has recently added the ETP Analysis Service to its platform. The exchange-traded product (ETP) is an exchange-traded fund (ETF), exchange-traded note (ETN), NextShares ETMF®, or other exchange-traded financial instrument.

The main motivation behind the new service is the availability of ETPs that do not track market-cap weighted indices. In particular, this includes “smart beta” (a.k.a. “strategic beta“) strategies that blend active and passive management. Due to the former aspect, smart-beta ETPs resemble traditional actively-managed mutual funds. Consequently, they can be analyzed with Alpholio™’s patented methodology, which constructs a custom reference portfolio of ETFs for each analyzed fund.

This leads to an apparent paradox: an analyzed ETP (which may be an ETF) is to be replicated by a portfolio of ETFs. Why do this at all? Just as with a traditional mutual fund, for several main reasons:

  • To determine whether active management aspect of the ETP adds value on a truly risk-adjusted basis
  • To understand the exposure of the analyzed ETP to various factors. This helps eliminate excessive exposures in the overall investment portfolio.
  • To replicate the ETP’s performance with other ETFs that may have preferable characteristics, such as lower fees, smaller trading premia or spreads, accessibility, etc. Conversely, to simplify a portfolio by substituting multiple ETFs with a single ETP.
  • To discern periods of underperformance and outperformance of the ETP after adjustment for its exposures.

Let’s demonstrate the new ETP Analysis Service in action. First, we will analyze the PowerShares FTSE RAFI US 1000 Portfolio (PRF). This ETP tracks the FTSE RAFI US 1000 Index, which

…is designed to track the performance of the largest US equities, selected based on the following four fundamental measures of firm size: book value, cash flow, sales and dividends. The 1,000 equities with the highest fundamental strength are weighted by their fundamental scores.

To conduct the analysis, we will use the simplest variant of Alpholio™’s methodology, which builds a reference ETF portfolio with both fixed membership and weights. The following chart and related statistics show the cumulative RealAlpha™ for the ETP (to learn more about this and other performance measures, please visit our FAQ):

Cumulative RealAlpha™ for PowerShares FTSE RAFI US 1000 Portfolio (PRF)

Over the five years through July 2016, the ETP added a small amount of value vs. its reference ETF portfolio of comparable volatility. The RealBeta™ of the ETF was the same as that of a broad-based equity market ETF.

The following chart with accompanying statistics presents the fixed composition of the reference ETF portfolio for the analyzed ETP:

Reference Weights for PowerShares FTSE RAFI US 1000 Portfolio (PRF)

The ETP had major equivalent positions in the iShares Russell 1000 Value ETF (IWD), Vanguard Value ETF (VTV), iShares Core S&P Total U.S. Stock Market ETF (ITOT), SPDR® S&P® 500 Value ETF (SPYV), PowerShares BuyBack Achievers Portfolio (PKW), and Guggenheim S&P 500® Pure Value ETF (RPV). Clearly, this ETP had a very strong exposure to the large-cap value factors represented by reference ETFs. (The Other component in the chart collectively depicts additional six ETFs with smaller weights, some of which were effectively zero.)

In the second example, let’s analyze the Guggenheim S&P 500® Equal Weight ETF (RSP). This ETP

Seeks to replicate as closely as possible the performance of the S&P 500 Equal Weight Index, before fees and expenses, on a daily basis.

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

Cumulative RealAlpha™ for Guggenheim S&P 500® Equal Weight ETF (RSP)

Over the five years through July 2016, this ETP also added little value vs. its reference ETF portfolio. Its RealBeta™ was above that of a broad-based stock market ETF.

The final chart and statistics show the static composition of the reference ETF portfolio for the ETP:

Reference Weights for Guggenheim S&P 500® Equal Weight ETF (RSP)

The ETP had major equivalent positions in the First Trust Large Cap Core AlphaDEX® Fund (FEX), iShares Russell Mid-Cap Value ETF (IWS), PowerShares S&P 500 Quality Portfolio (SPHQ), PowerShares S&P 500® High Beta Portfolio (SPHB), iShares Russell Mid-Cap Growth ETF (IWP), and Consumer Discretionary Select Sector SPDR® Fund (XLY). The Other component in the chart collectively represents additional six ETFs with smaller constant weights, one of which was effectively zero.

As could be expected, due to equal-weighting of its positions this large-cap ETP had a significant tilt toward mid-cap stocks, especially of value characteristics. In addition, the ETP had considerable exposure to economic sectors such as consumer discretionary, financials, technology, and industrials.

If you would like to take advantage of the new ETP Analysis Service, please register on our website.


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Sharpe Ratio of Smart Beta
exchange-traded fund

A post in Barron’s and an article in GlobeAdvisor cover a report by strategists at Pavilion Global Markets on historical performance of “smart beta” strategies.

The report analyzed a number of monthly-rebalanced portfolios consisting of equities in the S&P 500® index since 1991. The conclusion stated in the post was that

All the methods beat the no-frills S&P 500. But the group found that only one strategy — screening for stocks exhibiting low volatility over three months — beat the index with reduced risk.

In particular, the article says that

One strategy draws from the same index, but weights stocks equally rather than by market capitalization. Since 1991, this approach turned a $100 investment into $892, or about 70 per cent more than the benchmark index. The divergence between the two approaches picked up noticeably after 2001.

An index that weighted stocks based on sales outperformed the benchmark by 53 per cent, an index that weighted stocks based on earnings outperformed by 77 per cent and an index that weighted stocks based on return-on-equity outperformed by 114 per cent – an astounding difference when you consider that it still draws from the same 500 stocks as the benchmark index.

This is further illustrated in the following chart:

Annualized Return and Risk of 'Smart Beta' Strategies

Since “smart beta” strategies exhibit both higher returns and elevated volatility compared to the index, naturally a question arises: What is the incremental return per unit of risk of these strategies compared to that of the index? This is where the ex-post Sharpe ratio (SR) comes in.

To estimate the SR for each strategy and the index, we can

  • Read the annualized return and volatility figures from the chart. While the annualized (geometric average) return is different from the arithmetic average required in the SR calculation, it should suffice as a rough equivalent.
  • Obtain an average risk-free rate (RF) in the analysis period. As a proxy for the risk-free rate, many SR calculations use a three-month Treasury bill rate; because each strategy was rebalanced monthly, we could also use a four-week bill rate.
  • Assume that the volatility of Treasury bill returns is negligibly small compared to that of the strategy. Further assume that the correlation of these returns to strategy returns is close to zero. This means that the denominator in the SR effectively becomes the risk of the strategy.

The rate on three-month Treasury bills since 1991 can be obtained from the FRED® service of the Federal Reserve Bank of St. Louis. (Data on four-week Treasury bill rates are only available from July 2001.) It turns out that the average annualized rate in that period was about 3%.

The following table shows the estimated SRs:

Strategy Return Return – RF Risk Sharpe Ratio
ROE Weighted 0.112 0.082 0.157 0.52
Low-Vol Weighted 0.098 0.068 0.144 0.47
Earnings Weighted 0.103 0.073 0.163 0.45
Equal Weighted 0.101 0.071 0.165 0.43
P/E Weighted 0.101 0.071 0.166 0.43
Profit Margins Weighted 0.100 0.070 0.164 0.43
Sales Weighted 0.096 0.066 0.155 0.43
High-Vol Weighted 0.099 0.069 0.197 0.35
Market-Cap Weighted 0.075 0.035 0.146 0.31

All of the fundamental indexing strategies exhibited a higher SR than that of the traditional market-cap index. In addition, the return-on-equity strategy beat the low-volatility strategy on a risk-adjusted basis. No wonder that, according to the article, fundamental indexing is gaining momentum:

Whatever you prefer to call them, there are now 326 U.S. ETFs that fit the description in one way or another, according to IndexUniverse, and this number doesn’t include leveraged and inverse strategies. These funds account for 40 per cent of all U.S.-listed ETFs and about 14 per cent of ETF assets. This year alone, nearly $46-billion (U.S.) has flowed in.

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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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Substituting Exclusivity
analysis, mutual fund

A recent article from InvestmentNews describes the popularity of Dimensional Fund Advisors (DFA) mutual funds with financial advisers:

For the third time in four years, Dimensional Fund Advisors tops the list of mutual fund companies best positioned to increase its share of assets with financial advisers… The Austin, Texas-based mutual fund company, best known for its factor-based investing philosophy and high barriers to entry (for the mutual fund world, at least), scored 25% higher than bond megashop Pacific Investment Management Co. LLC, which came in second. The Vanguard Group Inc., the world’s largest mutual fund company, finished third.

Why do advisers prefer DFA funds? Three reasons come to mind: exclusive access, smart beta, and superior performance. The first reason is the same as the “high barriers to entry” mentioned in the above quote — the DFA funds cannot be purchased directly by individual investors, but only through qualified advisers. This means advisers can position themselves between inexpensive index-like vehicles and investors’ assets, thus improving the justification for an advisory fee.

The second reason emphasizes characteristics that make DFA funds supposedly different from regular index funds. For example, DFA Core Equity funds skew towards small-cap and value stocks:

Increased exposure to small and value companies may be achieved by decreasing the allocation of the portfolio’s assets in large growth companies relative to their weight in the US universe. Securities are considered value stocks primarily because a company’s shares have a high book value in relation to their market value (BtM).

However, this tilt is well known through Fama-French research and can be achieved through other means, including specialized factor exchange-traded funds (ETFs).

The third reason requires more scrutiny. While it is true that most DFA funds earn above-average ratings in their respective categories according to Morningstar, how does the performance of these funds look like from the Alpholio™ perspective? Let’s analyze the first DFA fund on the US Core Equity list, the US Core Equity 1 Portfolio (DFEOX). Here is the cumulative RealAlpha™ chart for the fund:

Cumulative RealAlpha™ for DFEOX

The chart clearly shows that on a truly risk-adjusted basis, the fund did not generate any alpha in the past seven years. This is further supported by performance statistics:

DFEOX Statistics

Similar results are observed for all of the DFA US Core Equity funds since their inception:

Name Ticker Annualized Lag RealAlpha™
US Core Equity 1 Portfolio DFEOX -0.38%
US Core Equity 2 Portfolio DFQTX -0.27%
US Vector Equity Portfolio DFVEX -0.64%
US Social Core Equity 2 Portfolio DFUEX -0.02%
TA US Core Equity 2 Portfolio DFTCX -0.03%
US Sustainability Core 1 Portfolio DFSIX -0.13%

These data indicate that an individual investor would realize better risk-adjusted returns from substitute portfolios of ETFs than from these DFA funds, while in the process gaining intra-day liquidity and full control of investments. Alpholio™ provides composition of such substitute portfolios on an ongoing basis. Thus, the three reasons for preference of DFA funds, in particular the exclusive access, no longer hold.


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