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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Analysis of Harding Loevner Global Equity Fund
analysis, mutual fund

A recent profile in Barron’s features the Harding Loevner Global Equity fund (HLMGX; Advisor Class shares). This $870 million no-load fund has a reasonable 1.18% expense ratio and 21% annual turnover (a five-year average as of the second quarter of 2016). According to the article

The payoff for investors has been 6.6% average annual returns over the past decade, versus 5.0% for the average world stock fund tracked by Morningstar.

The primary benchmark for the fund is the MSCI All Country World Index (ACWI). One of the efficient implementations of this index is the iShares MSCI ACWI ETF (ACWI). According to Alpholio™ calculations, from April 2008 through July 2016 the fund returned more than the ETF in approximately 49% of all rolling 36-month periods, 62% of 24-month periods and 66% of 12-month periods.

The fund’s average cumulative (not annualized) outperformance over a rolling 36-month period was 1.5%. However, the median was minus 0.15%, which indicates that the fund significantly outperformed the ETF in a relatively small number of rolling periods. (A rolling period of 36 months tries to approximate an actual average holding interval. Many investments in a fund do not start precisely at the turn of a calendar year.)

To take the fund’s volatility and exposures into account, let’s employ the simplest variant of Alpholio™’s patented methodology. As other variants, this one also constructs a reference portfolio of ETFs that most closely tracks periodic returns of the analyzed fund. The reference portfolio has a fixed ETF membership and weights. Here is the resulting chart with statistics of the cumulative RealAlpha™ for the Harding Loevner Global Equity (to learn more about this and other performance measures, please visit our FAQ):

Cumulative RealAlpha™ for Harding Loevner Global Equity Fund (HLMGX) over 10 Years

Over the ten years through July 2016, the fund underperfomed its reference ETF portfolio. The volatility of the fund, measured as the standard deviation of monthly returns, was slightly higher than that of the reference portfolio. The fund’s RealBeta™ was close to that of a broad-based domestic equity ETF.

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

Reference Weights for Harding Loevner Global Equity Fund (HLMGX) over 10 Years

The fund had major equivalent positions in the iShares MSCI EAFE Growth ETF (EFG), Guggenheim S&P 500® Top 50 ETF (XLG), SPDR® Morgan Stanley Technology ETF (MTK), iShares U.S. Medical Devices ETF (IHI), and iShares MSCI Singapore ETF (EWS). The iShares TIPS Bond ETF (TIP) represented fixed-income holdings of the fund. The Other component in the above chart collectively embodies additional six ETF with smaller weights (see table).

A similar analysis of the fund over the five years through July 2016 yields even worse results:

Cumulative RealAlpha™ for Harding Loevner Global Equity Fund (HLMGX) over 5 Years

Over this shorter evaluation period, the fund generated a negative annualized discounted RealAlpha™. Its cumulative return was almost 15% lower than that of the reference ETF portfolio, whose composition is depicted in the following chart and accompanying statistics:

Reference Weights for Harding Loevner Global Equity Fund (HLMGX) over 5 Years

This reference portfolio indicates that the fund had a considerable exposure to financials (IXG), U.S. energy (IYE), Japan equity (EWJ), and U.S. technology (MTK and IGV).

In terms of cumulative RealAlpha™, the fund’s performance over the three years through July 2016 was similar to that over ten years (its annualized discounted RealAlpha™ was slightly negative).

In sum, over the most recent three-, five- and ten-year periods, the Harding Loevner Global Equity fund failed to add value when compared to the respective reference ETF portfolios. Despite a sensible turnover, in 2014 and 2015 the fund had long-term capital gain distributions around 2.5-4% of the NAV, which made it less suitable for taxable accounts.

To learn more about the Harding Loevner Global Equity and other mutual funds, 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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