Analysis of Alpha Architect ETPs
June 3, 2017
Do iShares Smart Beta ETFs Outperform? (Part II)
A recent article in The Wall Street Journal profiles the CEO of Alpha Architect LLC, an upstart active investment manager. The firm currently advises five exchange-traded products (ETPs). Four of these ETPs have a sufficiently long history to be analyzed using Alpholio™’s patented methodology.
All of the following analyses employ the simplest variant of the methodology. For each analyzed ETP, the variant constructs a reference portfolio of up to six ETFs that most closely tracks periodic returns of the ETP. Both the membership and weights of ETFs in the reference portfolio are fixed over the entire analysis period.
Let’s start with the ValueShares U.S. Quantitative Value ETF (QVAL). Here is a chart of the cumulative RealAlpha™ for this ETP (to learn more about this and other performance measures, please visit our FAQ):
The ETP produced a significantly lower cumulative return than that of its reference ETF portfolio. It also had a higher volatility due to a relatively small number of deep-value holdings. This was also reflected in a considerably elevated RealBeta™, assessed against a broad-based domestic equity ETF.
The following chart with statistics shows the fixed composition of the reference ETF portfolio for QVAL:
The ETP had equivalent positions in the First Trust Large Cap Value AlphaDEX® Fund (FTA), SPDR® S&P® Retail ETF (XRT), PowerShares S&P SmallCap Information Technology Portfolio (PSCT), iShares North American Tech-Multimedia Networking ETF (IGN), First Trust Industrials/Producer Durables AlphaDEX® Fund (FXR), and iShares U.S. Oil Equipment & Services ETF (IEZ). These ETFs correspond to average exposures QVAL generated over the evaluation period.
Let’s move on to the ValueShares International Quantitative Value ETF (IVAL). Here is a chart of cumulative RealAlpha™ with statistics for this ETP:
The ETP added significantly more value than its reference ETF portfolio, but only beginning in the second half of last year. This is why the article singles out a recent outperformance of just this product:
…value-focused fund of overseas stocks is beating all its rivals over the past year.
The ETP produced this excess return at the expense of a substantially higher volatility than that of its reference ETF portfolio.
The following chart with associated statistics illustrates the static composition of the reference ETF portfolio for IVAL:
The ETP had equivalent positions in the WisdomTree Japan Hedged Equity Fund (DXJ), Guggenheim CurrencyShares® Australian Dollar Trust (FXA), iShares MSCI South Korea Capped ETF (EWY), iShares MSCI Spain Capped ETF (EWP), WisdomTree Japan SmallCap Dividend Fund (DFJ), and iShares MSCI Germany ETF (EWG).
Next, let’s take a look at the MomentumShares U.S. Quantitative Momentum ETF (QMOM). Here is a chart of the cumulative RealAlpha™ with statistics for this ETP:
The ETP failed to outperform its reference ETF portfolio of somewhat lower volatility.
The following chart with related statistics depicts the constant composition of the reference ETF portfolio for QMOM:
The ETP had equivalent positions in the PowerShares DWA Industrials Momentum Portfolio (PRN), Global X Social Media ETF (SOCL), aforementioned DFJ, PowerShares NASDAQ Internet Portfolio (PNQI), PowerShares Dynamic Leisure and Entertainment Portfolio (PEJ), and PowerShares DWA SmallCap Momentum Portfolio (DWAS).
Finally, let’s evaluate the MomentumShares International Quantitative Momentum ETF (IMOM). Here is the cumulative RealAlpha™ chart with statistics for this ETP:
The ETP significantly underperformed its reference ETF portfolio in terms of both the cumulative return and volatility. However, its RealBeta™ was well below that of the market.
The following chart with accompanying statistics shows the invariant composition of the reference ETF portfolio for IMOM:
The ETP had equivalent positions in the iShares Mortgage Real Estate Capped ETF (REM), VanEck Vectors Vietnam ETF (VNM), iShares U.S. Medical Devices ETF (IHI), aforementioned FXA, Guggenheim CurrencyShares® Japanese Yen Trust (FXY), and aforementioned SOCL.
It should be noted that all of the above ETPs except for QVAL have traded at a considerable premium to their net asset value (NAV). For example, as of this writing, IMOM’s one-year price return was 8.50% compared to a 3.10% NAV return. Such pricing discrepancies could partially explain the presence of REM (a domestic real-estate fund) and IHI (a domestic medical device fund), in the reference ETF portfolio for IMOM.
In sum, the majority of Alpha Architect ETPs have so far delivered unimpressive results after a comprehensive adjustment for volatility and exposures. Since the oldest product has less than three years of history, only time will tell whether the performance of these ETPs vs. their reference ETF portfolios will eventually improve. The challenge of any factor investing, including value and momentum, is not only the cyclical variation of performance but also the selection of individual securities to implement the factor.
To learn more about the Alpha Architect and other ETPs, please register on our website.
September 12, 2016
Do iShares Smart Beta ETFs Outperform? (Part I)
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:
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:
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:
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:
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:
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:
The average correlation between rolling 24-month returns of the two ETFs was 0.98.
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.
September 10, 2016
Introducing ETP Analysis Service
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:
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:
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:
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:
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:
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:
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).
August 27, 2016
Introducing Dynamic Portfolio Analysis (Part III)
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):
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:
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:
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:
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.
July 25, 2016
Introducing Dynamic Portfolio Analysis (Part II)
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:
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:
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:
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:
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:
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:
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.
July 23, 2016
Introducing Dynamic Portfolio Analysis (Part I)
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:
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:
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:
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:
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:
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.
July 22, 2016
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:
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:
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:
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.