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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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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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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Introducing Dynamic Portfolio Analysis (Part III)
active management, analysis, portfolio

Alpholio™ has recently added the Dynamic Portfolio Analysis (DPA) service to its platform. The first post in this three-part series described how the DPA can be used with OFX/QFX files. The second post covered the use of DPA with Transaction CSV files. This final installment focuses on using the DPA with Return CSV files.

Let’s start with the analysis of a simple buy-and-hold equity portfolio that contained only one ETF, the SPDR® S&P 500® ETF (SPY), with all distributions reinvested. Here is how the Return CSV file for such a portfolio looks like:

Return CSV for Single-ETF Portfolio

As in the Transaction CSV, the first line starting with the # character is a comment that is ignored during processing of the file. The CSV has only two columns: the date of a trading day and the numerical return of the analyzed portfolio on that date. The return figure, which is typically a fraction, may also be expressed as percentage by appending the % sign. The majority of lines in this sample CSV were replaced by a single … line for visual brevity. The sample returns start on the first trading day in 2005.

The following chart with related statistics shows the cumulative RealAlpha™ for the portfolio analyzed with a regular fit:

Cumulative RealAlpha™ for Single-ETF Portfolio

The From date in the chart is the last trading day of the first full month of the portfolio’s lifespan. The To date is determined by the availability of historical data, through June 2016 as of this writing.

Not surprisingly, Alpholio™ determined that the portfolio had virtually no RealAlpha™; any non-zero values resulted from the limit of computational precision. The RealBeta™ of the portfolio was slightly lower than one because Alpholio™ uses a broad-based equity ETF, which includes mid- and small-cap stocks, as a proxy for the equity market.

The following chart and statistics illustrate the constant membership and weights of the reference ETF portfolio:

Reference Weights for Single-ETF Portfolio

As could be expected, the reference ETF portfolio consisted of just one ETF (SPY), the same that constituted the entire analyzed portfolio. Please note that Alpholio™ constructed this reference portfolio only based on periodic returns, i.e. without any knowledge of the actual investment strategy, trades, positions or dollar amounts. This way, the confidentiality of investments was fully preserved.

For the second example, let’s use a diversified buy-and-hold balanced portfolio that contained multiple ETFs:

In the portfolio, each ETF had its distributions reinvested. At the end of each month, the portfolio was rebalanced to the original ETF weights. Here is how the abbreviated Return CSV looks like for the portfolio:

Return CSV for Multiple-ETF Portfolio

As in the previous example, the first line of the file is a comment, the second line is the CSV header, and subsequent lines contain trading dates and numerical returns of the portfolio.

The following chart and statistics show the cumulative RealAlpha™ for the analyzed portfolio:

Cumulative RealAlpha™ for Multiple-ETF Portfolio

The portfolio also had a negligibly small amount of RealAlpha™. Thanks to a broader asset allocation and periodic rebalancing, the portfolio’s RealBeta™ was slightly lower than the 0.6 of a traditional 60/40 portfolio.

The following chart depicts the constant composition of the reference ETF portfolio:

Reference Weights for Multiple-ETF Portfolio

As anticipated, the reference ETF portfolio contained exactly the same positions and weights as the analyzed portfolio did. Again, the reference portfolio was built without any knowledge of the original individual investments. This proves the correctness and viability of this analytical approach. Of course, the DPA can evaluate any investment portfolio, not just one that solely contained ETFs.

If you would like to apply the new Dynamic Portfolio Analysis service to your investment portfolio, please register on our website.


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Introducing Dynamic Portfolio Analysis (Part II)
active management, analysis, portfolio

Alpholio™ has recently added the Dynamic Portfolio Analysis (DPA) service to its platform. The previous post in this three-part series described how the DPA can be used with OFX/QFX files. This post focuses on using the DPA with Transaction CSV files.

Let’s start with the simplest input file that contains just two transactions in a hypothetical investment portfolio: a deposit of cash into the investment account and a purchase of a single mutual fund, both on the same date. Here is how this text file looks like:

Transaction CSV for BSTSX Portfolio

The first line in the file, starting with the # character, is a comment that is ignored during processing of the CSV. The BlackRock Science & Technology Opportunities Fund (BSTSX; Service Class shares) was specifically selected for this example because it had a sufficiently long history and also did not have any distributions over the entire analysis period (which means no reinvestment of distributions was possible).

The purchase date was purposely chosen to be the last trading day of 2005. This way, the first daily return of the investment account was the first trading day in 2006, and 10.5 years of subsequent history was available.

The cash amount deposited into the account equaled the amount paid for the fund’s shares, so that the initial net cash balance was zero. The ending cash balance was also zero because the fund had no distributions. The price of the unit of cash was $1 (e.g. one share of a typical money-market fund) and the price per share of the fund was equal to the actual net asset value (NAV) of the fund on the transaction date.

The following chart depicts the cumulative RealAlpha™ and related statistics for the analyzed portfolio:

Cumulative RealAlpha™ for BSTSX Portfolio

The results are identical to those of Alpholio™’s analysis of the same fund as a standalone investment, which indicates that the Transaction CSV was processed correctly. Note that the chart starts on the last trading day of January 2006 because it was the first full month of the analysis period. It ends on the last trading day in June 2016 because this was the last full month of data available as of this writing. (While the performance of the fund itself is less important in the context of this discussion, it can be noted that over the evaluation interval the fund added a modest amount of value on a risk-adjusted basis, and so did the hypothetical portfolio that contained it.)

The second example uses a hypothetical buy-and-hold portfolio with a focus on equity investments:

Transaction CSV for Ten-Stock Portfolio

On the last trading day of 2005, $100,000 was deposited into the account to purchase approximately equal dollar amounts of ten stocks, each of the largest-cap public company in its respective economic sector per GICS (note that at that time, the currently separate real-estate sector was part of Financials).

The share price for each position was chosen at an approximate mid-point of the actual low and high prices on the transaction date. For simplicity, trading costs were assumed to be negligibly small. All dividends subsequently paid by the stocks were not reinvested but instead deposited as cash into the account, so that the cash position gradually increased. However, any corporate spinoffs were assumed to be immediately sold, with proceeds reinvested into the primary shares. All share splits were automatically accounted for during analysis but the portfolio was not rebalanced.

The following chart shows the cumulative RealAlpha™ and related statistics for the analyzed portfolio:

Cumulative RealAlpha™ for Stock Portfolio

Since its inception, the portfolio added a substantial amount of value on a risk-adjusted basis. While the analyzed portfolio’s volatility, measured as the annualized standard deviation of returns, was slightly higher than that of the reference ETF portfolio, the RealBeta™ of the analyzed portfolio was significantly lower that that of the broad-based equity ETF.

The following chart and statistics show the constant composition of the reference ETF portfolio:

Reference Weights for Ten-Stock Portfolio

Consistently with the stock holdings of the analyzed portfolio, the reference portfolio comprised large-cap equity ETFs, such as the Guggenheim S&P 500® Top 50 ETF (XLG), PowerShares High Yield Equity Dividend Achievers Portfolio (PEY), PowerShares Dividend Achievers Portfolio (PFM), and iShares Morningstar Large-Cap Value ETF (JKF). The average cash portion of the analyzed portfolio was approximated by an equivalent position in the iShares 1-3 Year Treasury Bond ETF (SHY).

The final post in this series will demonstrate how the Dynamic Portfolio Analysis service can be used with Return CSV files.


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Introducing Dynamic Portfolio Analysis (Part I)
active management, analysis, portfolio

Alpholio™ has recently added the Dynamic Portfolio Analysis (DPA) service to its platform. Unlike the Basic Portfolio service in which the membership of the portfolio is predetermined and only subject to periodic rebalancing over the analysis period, the DPA allows for arbitrary changes in portfolio composition. This three-part post will cover the new service in more detail.

Th DPA has two main benefits: It determines whether active management of an investment portfolio has added value on a truly risk-adjusted basis. It also shows the exposure of the portfolio to various factors that may change over time.

To start the DPA, you can supply data files in one of three formats:

  • Open Financial Exchange (OFX) or the Quicken® proprietary version thereof (QFX). Although primarily used for interchange of banking transactions, OFX/QFX files are also available for brokerage accounts.
  • Transaction comma-separated values (CSV). This simple file format is proprietary to Alpholio™. The file can easily be composed from historical account records in any text editor or spreadsheet application. To improve confidentiality, stock and cash positions may be scaled by some constant factor.
  • Return comma-separated values (CSV). Also specific to Alpholio™, this file format is even simpler than the Transaction CSV. The file contains daily historical returns, expressed either as fractions or percentages, of the analyzed portfolio. The main advantage of this format is that, by not disclosing specific trades, positions or dollar amounts, it preserves confidentiality of the investment strategy.

To learn more about the Transaction CSV and Return CSV formats, please inquire through the Contact Us page. For security, Alpholio™ only uses all uploaded files for transient analysis and does not permanently retain them.

The following screenshot shows the upload of QFX files from a sample brokerage account that mostly contained domestic and foreign large-cap stocks paying significant dividends:

Dynamic Portfolio Analysis with QFX Files

The Sweep File input is optional and used in cases where the investment account, such as at Vanguard® Brokerage Services, has a separate sub-account for a money-market fund used to automatically invest cash. The Fit Type selects the mode of the analysis (to learn more, please visit the FAQ page). Both file inputs can be reset with the Clear button.

Once the Analyze button is clicked, Alpholio™ processes both QFX files by extracting all investment and cash transactions, building portfolio values and calculating periodic returns. If there are no errors in input files, it then proceeds to analyze the portfolio just like a mutual fund. Here is the cumulative RealAlpha™ chart with related statistics for the sample portfolio:

Cumulative RealAlpha™ for Sample Stock Portfolio

The data files span 18 months but one month of history is ignored due to the required date alignment. The analysis indicates that the reference ETF portfolio cumulatively returned 4.8% more than the analyzed portfolio (see chart) and produced 3.3% of annualized discounted RealAlpha™ (see statistics). An investor managing this portfolio would generally be better off investing in the reference ETF portfolio instead, at least over this evaluation interval.

The volatility of both portfolios, measured as annualized standard deviation of returns, was comparable. The analyzed portfolio had a RealBeta™ lower than that of the broad-based market ETF.

The following chart and related statistics illustrate the constant composition of the reference ETF portfolio:

Reference Weights for Sample Stock Portfolio

As expected, the reference portfolio predominantly consisted of large-cap, dividend-paying equity ETFs: the ProShares Large Cap Core Plus (CSM), WisdomTree Dividend ex-Financials Fund (DTN), Vanguard High Dividend Yield ETF (VYM), Vanguard Mega Cap Value ETF (MGV), and iShares International Developed Property ETF (WPS).

An equivalent position in the PowerShares Senior Loan Portfolio (BKLN) indicates the sample portfolio’s exposure to high-yield bonds. The equivalent position in the iShares U.S. Healthcare ETF (IYH) implies the portfolio’s exposure to a specific economic sector. Similarly, an equivalent position in the iShares MSCI Mexico Capped ETF (EWH) represents an exposure to the economy of a specific country. Finally, an equivalent position in the PowerShares DB US Dollar Index Bullish Fund (UUP) signals a positive exposure to the USD currency. (The above table shows the weights of the final two ETFs as zero due to rounding.)

The next post in this series will show how the Dynamic Portfolio Analysis service can be used with a Transaction CSV file. The final post will demonstrate how to analyze a portfolio with a Return CSV file.


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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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Searching for Consistent Outperformance
active management, mutual fund, performance persistence

Two studies from Vanguard underscore how difficult it is to identify actively-managed mutual funds that not only survive and but also consistently outperform their benchmarks.

The first study shows that the majority of funds across all asset classes failed to outperform their prospectus benchmarks over the past 15 years through 2012:

Vanguard - Funds Underperforming Prospectus Benchmark

The chart demonstrates that when assessing long-term performance, it is important to take into account liquidated and merged (“dead”) funds. Otherwise, statistics suffer from a “survivorship bias” that benefits funds still in existence. In addition, in most categories a median surviving fund exhibited a negative annualized excess return vs. the benchmark.

Statistics get worse when style benchmarks, assigned to fund categories, are used:

Vanguard - Funds Underperforming Style Benchmark

This is because many funds choose an inappropriate prospectus benchmark that does not reflect the fund’s actual investment style. In a majority of categories the survivors’ excess returns were even lower.

Finally, what are the chances that a fund ranked in the top quintile (20%) of U.S. actively-managed funds in terms of five-year returns through 2007 persisted in the same quintile in the next five years? About 15%, which is less than the 20% expected by chance:

Vanguard - Persistence of Ranking in Actively-Managed US Funds

As a matter of fact, about a quarter of such funds wound up in the lowest quintile, and about one-sixth disappeared altogether. Of all the funds available, only about 3% persisted in the top quintile in both five-year periods.

The second study shows that of the 1,540 U.S. domestic equity funds in the 15-year period through 2012, only about 18% survived and outperformed their respective style benchmarks:

Vanguard - Funds That Survived and Outperformed

However, almost all of these successful funds had five or more years of underperformance within the 15 years of analysis:

Vanguard - Periods of Successful Fund Underperformance

Moreover, about two-thirds of outperforming funds experienced at least three consecutive years of underperformance. In many cases, investors would have divested such funds and therefore not realize a full 15-year benefit.

All these findings underscore the need for a close monitoring of mutual fund performance. Outperforming funds are rare and do not persist in their winning streaks. Therefore, a dynamic analysis with a true adjustment for risk is required. Alpholio™ analyzes funds with a monthly frequency and provides buy-sell signals derived from the smoothed cumulative RealAlpha™ curves. These signals, among other inputs, can help investors make informed investment decisions. For more information about the Alpholio™ methodology, please visit our FAQ.

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Exchange-Traded Product Statistics
exchange-traded fund, exchange-traded product

A paper from PwC provides interesting statistics on exchange-traded funds (ETFs) and products (ETPs). [Alpholio™ uses the latter term to encompass ETFs, exchange-traded notes (ETNs), and other similar investment vehicles.]

As of 3Q2013, about $2.2T was invested in almost 5,000 ETPs globally:

PwC Global ETF and ETP Totals

Thanks to a large single market, an average ETP had much more AUM in the US than elsewhere (however, this does not take into account the typical right skew of the AUM distribution, whereby a small number of funds hold the majority of assets):

PwC Global ETF and ETP per Region

Unlike elsewhere, in the US the majority of ETP AUM belong to retail investors:

PwC ETF Use by Region

The percentage of AUM in active ETFs, whose launch began only in 2008, is still small but growing:

PwC Assets in Active vs. Index ETFs

PwC Active ETF Launches

ETFs enable the shift from individual security selection to asset allocation, especially in liquid markets:

PwC ETFs Displacing Mutual Funds in US Equity Category

ETFs now cover a broad spectrum of asset classes:

PwC ETF Asset Class Coverage

All these findings strongly support the Alpholio™ thesis: the growing number, breadth and variety of ETPs enable more and more accurate assessment and substitution of actively-managed mutual funds and arbitrary investment portfolios for the benefit of investors.

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Is Index Investing Extinct?
active management, exchange-traded product

An article in Forbes laments a recent change of focus in ETF industry conferences from traditional “market-tracking” products to active investment strategies. By “market-tracking,” the author clearly means the classic market-cap weighted indexing that is prevalent in ETFs.

This brings up two questions. The first one: What really qualifies as active management? A one-time decision to invest in the entire stock market, such as through the Vanguard Total Stock Market ETF (VTI), is arguably an act of active management. So is a decision to split the investment portfolio between 60% VTI and 40% iShares Core Total U.S. Bond Market ETF (AGG). Likewise, a decision to adopt

“a fixed asset allocation to various asset classes based on an investor’s long-term needs.”

Periodic portfolio rebalancing to such a fixed allocation is also a form of active management, if not market timing, even if conducted on a fixed schedule. That is because one of its attributes is to “buy low, sell high,” i.e. lock in the gains in appreciated assets to cheaply purchase other assets in anticipation of a reversal to the mean.

Similarly, any modification of a “fixed” asset allocation in response to a change in the investor’s age or life circumstances also qualifies as active management.

Finally, it is worth noting that indices tracked by passive ETFs are also actively managed. Over time, the membership of securities in the index will change, and frequently so due to an arbitrary decision from a management committee rather than as a result of an explicit formula. A recent recomposition of the Dow Jones Industrial Average is one case in point. Another example is a recent switch of the Vanguard Emerging Markets ETF’s (VWO) underlying index from MSCI to FTSE, which caused all South Korean stocks to be removed from the fund.

Active management inevitably takes place at all stages of the investment process, even one based on passive instruments.

The second question that arises: Where is the ETF industry heading? The first wave of ETFs was about attaining economies of scale while implementing traditional market indices. It created a few dominant providers but resulted in a race to the bottom in management fees.

The second wave was about spreading horizontally to all niches of the market. Many of such exotic strategies failed to garner minimum assets of $50-100M that are typically required for an ETF to survive.

The third wave is about non-market-cap indexing, whether equal-weighted or fundamentally-weighted (“smart beta“). Such funds are a blasphemy to market-cap indexing purists who spend a lot of time poking holes in these strategies.

The next wave, pending regulatory approval of infrequent reporting of fund holdings, will be about active management. The ETF structure is attractive to actively-managed mutual fund vendors because it allows them to lower fees and survive the onslaught of cheap market-cap indexed ETFs.

All this makes traditional fee-based advisers nervous:

In the end, most advisers continue to do what’s in their clients’ best interest; they create a long-term asset allocation, buy low-cost index fund, and then stay the course!

The problem is that in many cases investors pay a recurring annual fee of anywhere from 0.2% to 1.5% of assets for a one-time setup of a portfolio pie-chart (frequently with small variations from the adviser’s “moderate” allocation template), followed by periodic rebalancing and reports. That enables a typical adviser to spend only about 11% of time on investment research, while devoting about 18% to client acquisition and prospecting, and 48% to client management.

In the end, the market will rightly decide what type of financial products survive and flourish. Marketing gimmicks aside, innovation in the ETF industry is a good thing because it gives investors more financial instruments to choose from at an ever-decreasing cost. Alpholio™ can use these new products to form reference ETF portfolios that better explain the performance of actively-managed mutual funds and arbitrary portfolios.

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