Alpholio™ App for Android – Mutual Fund Service
app, mutual fund

In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the seventh and final one in a series covering the app’s services in more detail.

The Mutual Fund service analyzes mutual fund performance using Alpholio™’s patented methodology. For a detailed explanation of the methodology, please visit our FAQ. To see how the methodology is applied in practice, please review our blog posts that analyze various mutual funds.

To analyze a mutual fund, Alpholio™ finds a reference portfolio of exchange-traded funds (ETFs) that most closely tracks the fund’s performance over time. In general, there are three ways such a reference portfolio can be constructed with respect to its ETF membership and weights (percentages of each ETF’s value relative to the overall portfolio value):

  • Fixed membership and fixed weights (we call it a “regular fit”)
  • Fixed membership and variable weights (“fine fit”)
  • Variable membership and variable weights (“detailed fit”)

(The fourth alternative, variable membership and fixed weights, makes little sense.) More variability results in a more accurate fit, but it also causes more changes in the reference portfolio. A detailed fit, which selects ETFs from a large pool of candidates multiple times, is also much more computationally intensive than the other two.

To access the service, start the app, open the navigation drawer and tap the Mutual Fund item:

Alpholio™ App for Android - Services

This will open a new screen, on which you can configure the analysis:

Alpholio™ App for Android - Mutual Fund Input

By default, the app analyzes FAIRX (The Fairholme Fund). To change the fund’s ticker, tap the corresponding field and use the pop-up keyboard to edit it. (If you need to find the ticker based on other information, use the Security Lookup service of the app.)

To modify either the From or To date, tap its corresponding button. This will pop up a standard date selection dialog. The From date must chronologically precede the To date.

To select a different fit type, tap the corresponding radio button. (The Detailed fit is disabled in the beta release of the app.)

After you specify all parameters, tap the Analyze Mutual Fund button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to use the service, your device must be connected to the Internet.

After the app obtains and processes the data, you should see the following screen:

Alpholio™ App for Android - Mutual Fund - Output - Total Return

This is a chart of the total returns of the analyzed fund and its reference ETF portfolios. To learn about the difference between the regular reference (Ref in the chart) and lag reference (Lag Ref) portfolios, please consult the FAQ.

To select a different analysis screen, tap the spinner on the action bar and then tap a corresponding item in the dropdown menu:

Alpholio™ App for Android - Mutual Fund - Output - Spinner

Here is the cumulative RealAlpha™ chart for FAIRX:

Alpholio™ App for Android - Mutual Fund - Output - RealAlpha

Below the chart, there is a Statistics section that you can collapse and expand by tapping on its header. The section contains annualized standard deviation for the fund and reference portfolio, as well as annualized discounted RealAlpha™ and RealBeta™ measures for the the regular and lag reference portfolios.

Here is the reference weights chart for FAIRX:

Alpholio™ App for Android - Mutual Fund - Output - Reference Weights - Fine Fit

The Statistics section below the chart contains statics for each ETF in the reference portfolio. As you can see, the fund had a largest equivalent position in VFH (Vanguard Financials ETF). This was a reflection of large holdings in Fannie Mae and Freddie Mac, which recently caused the fund to lose 9.6% in a single day as a result of an unfavorable court ruling.

The Smooth Buy-Sell and EMA Buy-Sell charts provide hypothetical buy-sell signals derived from the cumulative RealAlpha™. The difference between the two stems from the smoothing method: The former uses forecasting (which has less latency but can cause more frequent buy-sell transitions), while the latter employs an exponential moving average (EMA) approach that has opposite characteristics. Here is a sample chart based on the first method:

Alpholio™ App for Android - Mutual Fund - Output - Smooth Buy-Sell

Press or tap the Back button and change the fit type to Regular to see how this affects the membership and weights in the reference ETF portfolio for the fund. Give the app a try today:

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Alpholio™ App for Android – Efficient Frontier Service
app

In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the sixth one in a series covering the app’s services in more detail.

The Efficient Frontier service produces efficient frontier charts for a portfolio in the specified time frame. A full explanation of the efficient frontier and modern portfolio theory (MPT) is beyond the scope of this post. Among other places, you can find a good coverage of these concepts here and here.

To access the service, start the app, open the navigation drawer and tap the Efficient Frontier item:

Alpholio™ App for Android - Services

This will open a new screen, on which you can enter inputs for the chart. To expand the Dates and Return Frequency sections, simply tap each section header:

Alpholio™ App for Android - Efficient Frontier Input

The Positions, Dates and Return Frequency sections are identical to their counterparts in the Portfolio service described in the previous post. However, settings for the Efficient Frontier service are separate from those of the Portfolio service.

After you specify all parameters, tap the Get Efficient Frontier button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to generate the chart, your device must be connected to the Internet.

When the app obtains and processes the data, you should see the following screen:

Alpholio™ App for Android - Efficient Frontier Output

The first thing you may notice is that the chart begins in October 2003 and not January 2000 that was specified as the From date. That is because the inception date of AGG was in September 2003 and the first full month of returns for this ETF was the following month. The app automatically selected the largest possible date range for the analysis.

The efficient frontier (EF) is plotted in two sections: a small red one below the minimum-variance portfolio (MVP) and a large blue one above it. The capital allocation line (CAL) touches the upper EF section at the tangency portfolio (TP) point. Finally, the current portfolio (CP) is shown as a point inside the EF.

To zoom in on a portion of the chart, tap the + button or use a spread gesture. To scroll a zoomed-in chart horizontally or vertically, use a corresponding swipe gesture. To zoom out, tap the button or use a pinch gesture. To immediately restore the chart to its original view, tap the 1:1 button.

Below the chart, there is a Statistics section that can be collapsed and expanded by tapping its header. The section contains precise expected-return / standard-deviation coordinates for the MVP, TP and CP. It also provides the risk-free rate and the maximum Sharpe ratio (that of the TP).

Press or tap the Back button on the device to change the weights of positions in the portfolio and see how the CP location changes with respect to the efficient frontier. Give the app a try today:

Get It on Google Play

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Alpholio™ App for Android – Portfolio Service
app

In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the fifth one in a series covering the app’s services in more detail.

The Portfolio service produces charts of total returns for portfolios composed of multiple securities and rebalanced with a specified frequency. (To better understand the importance of using total returns as opposed to price returns, please refer to the description of the app’s Total Return service.)

To access the service, start the app, open the navigation drawer and tap the Portfolio item:

Alpholio™ App for Android - Services

This will open a new screen, on which you can enter inputs for the chart. To expand the Dates, Return Frequency and Rebalance Frequency sections, simply tap each section header:

Alpholio™ App for Android - Portfolio Input

You can enter up to 20 portfolio positions by specifying a ticker and percentage weight for each. The weight determines the value of the position relative to the total value of the portfolio. For example, if the portfolio is worth $10,000 and a position has a weight of 25%, then the position’s value is $2,500. Position weights in a portfolio always add up to 100%.

The default positions are VTI (Vanguard Total Stock Market ETF) at 40%, EFA (iShares MSCI EAFE ETF) at 20%, and AGG (iShares Core U.S. Aggregate Bond ETF) at 40%. This is effectively a balanced 60/40 portfolio with one-third (i.e. 20% out of 60%) of the equity part in foreign securities.

To change a position’s ticker, tap the corresponding field and use the pop-up keyboard to edit it. (If you need to find the ticker based on other information, use the Security Lookup service of the app.)

To change a position weight, tap and drag the thumb of the corresponding seek bar until you see the desired percentage displayed above the bar. When you finish, weights of all other positions in the portfolio will automatically recalculate to add up to 100% (due to the seek bar resolution, there may be a rounding error of up to 1%). If you do not want the weight of a particular position to change, tap a corresponding Fix check box. If only one position remains unfixed, its weight cannot be changed.

To delete a position, tap its Del button; you will not be able to remove the last remaining position. When a position with non-zero weight is removed, its weight is distributed among the remaining positions according to their weights. To add a position, tap the Add Position button at the bottom of the list, then enter the new position’s ticker and set its weight.

To modify either the From or To date, tap its corresponding button. This will pop up a standard date selection dialog. The From date must chronologically precede the To date.

To select a different return frequency, tap the corresponding radio button. Generally, monthly returns will provide a smoother return plot than weekly or daily ones.

To choose a rebalance frequency, expand the Rebalance Frequency section and tap the corresponding radio button:

Alpholio™ App for Android - Portfolio Input - Rebalance Frequency

Portfolio rebalancing involves adjusting positions to bring their weights to their original specification. The service assumes that trading costs are negligibly small compared to the position value. This is, for example, the case with no-transaction-fee ETFs at discount brokerages.

The frequency of rebalancing cannot be higher than the frequency of returns. For example, with monthly returns, portfolio can be rebalanced monthly, quarterly or semi-annually (i.e. every six months), but not daily or weekly. If you make both frequencies the same then the portfolio weights will effectively be kept constant (disregarding weight fluctuations in between rebalancing events).

After you specify all parameters, tap the Analyze Portfolio button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to generate the chart, your device must be connected to the Internet.

When the app obtains and processes the data, you should see the following screen:

Alpholio™ App for Android - Portfolio Output

The first thing you may notice is that the chart begins in October 2003 and not January 2000 that was specified as the From date. That is because the inception date of AGG was in September 2003 and the first full month of returns for this ETF was the following month. The app automatically selected the largest possible date range for the analysis.

To zoom in on a portion of the chart, tap the + button or use a spread gesture. To scroll a zoomed-in chart horizontally or vertically, use a corresponding swipe gesture. To zoom out, tap the button or use a pinch gesture. To immediately restore the chart to its original view, tap the 1:1 button.

Below the chart, there is a Statistics section that can be collapsed and expanded by tapping its header. You can see that the portfolio had an annualized return of about 7.4% with an annualized standard deviation or returns of about 9.6%. The portfolio generated a modest amount of alpha but its beta was significantly lower than that of the market (by definition, equal to one). The Sharpe ratio of the portfolio was 0.64 and the maximum drawdown from the peak in October 2007 to the trough in March 2008 was about 34%.

Press or tap the Back button on your device and change some position weights or rebalancing frequency to see how that affects portfolio statistics. Give the app a try today:

Get It on Google Play

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Alpholio™ App for Android – Correlation Service
app

In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the fourth one in a series covering the app’s services in more detail.

The Correlation service produces charts of the correlation coefficient between returns of two securities over a specified period. The correlation coefficient ranges from -1 (perfect negative correlation; returns always moving in opposite directions) to +1 (perfect positive correlation; returns always moving in same direction). A correlation coefficient of 0 indicates that returns of the two securities are unrelated or random with respect to each other. A correlation coefficient of close to +1 does not imply that the two securities are virtually identical (see our detailed explanation).

Knowing which securities are highly correlated and which are not is useful in portfolio construction. Generally, a security with a correlation coefficient of 0.6 or less in relation to others is considered a good candidate for portfolio diversification. Variations in the correlation coefficient over time are also important, especially in periods when one of the analyzed securities severely underperforms (see below).

To access the service, start the app, open the navigation drawer and tap the Correlation item:

Alpholio™ App for Android - Services

This will open a new screen, on which you can enter inputs for the chart. To expand the Return Frequency and Span sections, simply tap each section header:

Alpholio™ App for Android - Correlation Input

By default, the app calculates correlation of monthly returns of VTI (Vanguard Total Stock Market ETF) and AGG (iShares Core U.S. Aggregate Bond ETF) using a rolling 24-month window.

To change either ticker, tap the corresponding field and use the pop-up keyboard to edit it. (If you need to find the ticker based on other information, use the Security Lookup service of the app.)

To modify either the From or To date, tap its corresponding button. This will pop up a standard date selection dialog. The From date must chronologically precede the To date.

To select a different return frequency, tap the corresponding radio button. Generally, monthly returns will provide a smoother correlation plot than weekly or daily ones.

To change the rolling correlation window, tap the Span field and use the pop-up keyboard to edit it. The span is expressed in the same units as the return frequency and has to be a whole number greater than one.

After you specify all parameters, tap the Get Correlation button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to generate the chart, your device must be connected to the Internet.

After the app obtains and processes the data, you should see the following screen:

Alpholio™ App for Android - Correlation Output

The first thing you may notice is that the chart begins in October 2005 and not January 2000 that was specified as the From date. That is because the inception date of AGG was in September 2003, and two years worth of monthly returns were required to calculate the first data point. The app automatically selected the largest possible date range for the analysis.

To zoom in on a portion of the chart, tap the + button or use a spread gesture. To scroll a zoomed-in chart horizontally or vertically, use a corresponding swipe gesture. To zoom out, tap the button or use a pinch gesture. To immediately restore the chart to its original view, tap the 1:1 button.

Below the chart, there is a Statistics section that can be collapsed and expanded by tapping its header. Here you can see that VTI and AGG returns were largely uncorrelated (both the mean and median correlation coefficient is very close to zero). However, at times this was not true, as indicated by the minimum and maximum values.

The forecast field projects the best estimate of correlation in the next time increment (in this example, October 2014). The estimate is calculated in a dynamic manner, using the entire data set of the chart.

In this example, you can see that the return correlation between the broad US equity and bond ETFs dramatically increased at the onset of the financial crisis in 2008 and did not subside until about three years later. One possible explanation: AGG is composed of Treasury, MBS and corporate investment-grade bonds, of which the last ones underperformed in 2008, similarly to their stock counterparts. After the equity market rebounded in early 2009, low interest rates caused a sustained positive correlation of bond returns with those of stocks.

We hope that you will find this service of help in finding and analyzing portfolio diversifiers. Give the app a try today:

Get It on Google Play

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Alpholio™ App for Android – Rolling Returns Service
app

In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the third one in a series covering the app’s services in more detail.

The Rolling Returns service compares returns of an analyzed security to those of a reference security in rolling intervals over a specified period. For example, consider a two-year period starting in January 2012 and ending in December 2013 with a rolling 12-month interval. The first comparison will be made in an interval from January 2012 through December 2012. Then the interval will move out (roll) by one month and span February 2012 through January 2013. The rolling will continue until the final interval covers January 2013 through December 2013. In total, there will be 13 comparisons between 12-month cumulative returns of the analyzed and reference security. Note that if the rolling interval has multiple time units (months in this example), successive intervals overlap in time.

Rolling returns are useful in determining the persistence of outperformance (or lack thereof) of an analyzed security vs. its reference. Unlike the typical one-, three-, five- and ten-year annualized returns, they are not anchored to a single point in time, frequently aligned to an artificial boundary of a calendar year. Instead, they cover various market conditions, especially those characterized by a high volatility. Finally, rolling returns more accurately reflect actual investment patterns.

To access the service, start the app, open the navigation drawer and tap the Rolling Returns item:

Alpholio™ App for Android - Services

This will open a new screen, on which you can enter inputs for the chart. To expand the Dates, Return Frequency and Span sections, simply tap on each section header:

Alpholio™ App for Android - Rolling Returns Input

By default, the app compares rolling returns of FMAGX (Fidelity® Magellan® Fund) with those of SPY (SPDR® S&P 500® ETF) from the end of 2004 using 12-month returns rolling by one month at a time.

To change the analyzed or reference ticker, tap the corresponding field and use the pop-up keyboard to edit it. (If you need to find the ticker based on other information, use the Security Lookup service of the app.)

To modify either the From or To date, tap its corresponding button. This will pop up a standard date selection dialog.

To select a different return frequency, tap the corresponding radio button. Generally, monthly returns will provide smoother results than weekly or daily ones.

To change the rolling interval, tap on the Span field and use the pop-up keyboard to edit it. The span is expressed in the same units as the return frequency and has to be a positive whole number.

After you specify all parameters, tap the Get Rolling Returns button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to generate the chart, your device must be connected to the Internet.

After the app obtains and processes the data, you should see the following screen:

Alpholio™ App for Android - Rolling Returns Output

The first thing you may notice is that the bar chart begins in December 2005 and not December 2004 that was specified as the From date. That is because the first full 12-month rolling interval ends on the former date.

To zoom in on a portion of the chart, tap the + button or use a spread gesture. To scroll a zoomed-in chart horizontally or vertically, use a corresponding swipe gesture. To zoom out, tap the button or use a pinch gesture. To immediately restore the chart to its original view, tap the 1:1 button.

Below the chart, there is a Statistics section that can be collapsed and expanded by tapping its header. The first part of the section contains statistics for rolling returns of the analyzed security. In this example, you can see that the average (mean) rolling return was lower than a median one, which indicates a left skew of the distribution. You can also see that the analyzed security had a very wide range of 12-month returns. Finally, you can see that the analyzed security returned more than the reference one in only 44% of all rolling intervals over the entire analysis period.

Scroll the Statistics section up to see the Difference part. As the name indicates, this part contains statistics for the differences of rolling returns. You can see that the median underperformance of the analyzed vs. reference security was 2% and that the differences of returns spanned quite a broad range.

We hope that this service will provide you with useful insights into performance of various investment vehicles. Give the app a try today:

Get It on Google Play

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Alpholio™ App for Android – Total Return Service
app

In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the second one in a series covering the app’s services in more detail.

The Total Return service produces charts of total return for multiple securities over a common period. Unlike price returns, total returns account for dividends and other corporate actions of stocks. For mutual funds, this adjustment involves reinvestment of distributions.

To understand why this important, consider this finding from the “Dividend Choices” piece in the August 31, 2011 edition of the S&P The Outlook:

Over the long-term, dividends add significant value to stock. From 1926, dividends represent almost 42% of the total return of the S&P 500.

In that context, you may also want to review the following Wall Street Journal articles:

To access the service, start the app, open the navigation drawer and tap the Total Return item:

Alpholio™ App for Android - Services

This will open a new screen, on which you can enter inputs for the chart. To expand the Dates and Return Frequency sections, simply tap on each section header:

Alpholio™ App for Android - Total Return Input

You can specify up to seven tickers (security symbols). The default tickers are SPY (SPDR® S&P 500® ETF), EFA (iShares MSCI EAFE ETF) and EEM (iShares MSCI Emerging Markets ETF).

To modify a ticker, tap on its field and use the pop-up keyboard to edit it. (If you need to find the ticker based on other information, use the Security Lookup service of the app.) To delete a ticker, tap on the Del button on the same line; you will not be able to remove the last remaining ticker. To add a new ticker, tap the Add Ticker button at the bottom of the ticker list.

To change either the From or To date, tap its corresponding button. This will pop up a date selection dialog:

Alpholio™ App for Android - Total Return Input - Date

You can either scroll the month, day and year column, or tap on the respective item in the middle row to set it directly. Please note that the From date has to chronologically precede the To date.

Finally, you can select the frequency of returns to match the date range. Monthly returns (a default setting) are less volatile than weekly or daily ones, and will generally produce a smoother graph.

After you specify all parameters, tap the Get Total Return button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to generate the chart, your device must be able to access the Internet.

After the app obtains and processes the data, you should see the following screen:

Alpholio™ App for Android - Total Return Output

The first thing you may notice is that the beginning date of the chart is May 30, 2003, and not January 1, 2000 that was specified as the From date in the previous screen. That is because the former date is the earliest possible one on which all three ETFs had a full-month return (the inception date of EEM is April 7, 2003). This is an example of how the app automatically adjusts input dates to provide sensible analysis outcomes in all services.

To zoom in on a portion of the chart, tap the + button or use a spread gesture. To scroll a zoomed-in chart horizontally or vertically, use a corresponding swipe gesture. To zoom out, tap the button or use a pinch gesture. To immediately restore the chart to its original view, tap the 1:1 button.

The sample total return chart above shows that since mid-2003 the emerging market equities provided a much greater return than that of the developed market or domestic ones, but at the expense of higher volatility. Also, the cumulative return of domestic stocks recently exceeded that of developed market ones.

We hope that you will find this simple tool useful in further exploration of total returns. Give the app a try today:

Get It on Google Play

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Alpholio™ App for Android – Security Lookup Service
app

In the previous post, we introduced the Alpholio™ app for Android. This post is the first one in a series covering the app’s services in more detail.

The Security Lookup service helps you find the security based on partial information. To access the service, start the app, open the navigation drawer and tap the Security Lookup item:

Alpholio™ App for Android - Services

This will open a new screen, on which you can enter search parameters. To expand and collapse the Security Type, Search In and Show Up To sections, simply tap on each section header:

Alpholio™ App for Android - Security Lookup Input

By default, the app searches for text “ama” in both tickers and security names of ETFs, mutual funds and stocks, presenting up to 20 results. To change the search text, tap the field in the Search For section and use the pop-up keyboard for editing. To select other search options, tap the corresponding radio button.

When you complete all inputs, tap the Look Up button. If you do not specify the text to search for, you will see a pop-up warning. Otherwise, all your settings will be saved on the device for subsequent use. Please note that to conduct a search, your device must be able to access the Internet.

After the app downloads and processes the search results, you should see the following screen:

Alpholio™ App for Android - Security Lookup Output

You can see the search text “ama” highlighted in tickers and security names. The search is not case sensitive, so “AMA” and “Ama” will match.

We have purposely designed this service not to conduct incremental searches (auto-completion) as you type in each letter of the search text or change a search setting. The reason is that your device may be using a mobile connection with a data cap, so it is important to limit data transfers, however small.

We hope that you will find this tool helpful in identifying securities to use in other services of the app. Give the app a try today:

Get It on Google Play

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Introducing Alpholio™ App for Android
app

It is our pleasure to announce the Alpholio™ app for Android. The app is currently in public beta and can be downloaded free of charge from

Android App on Google Play

When started for the first time, the app automatically registers you for a trial as an anonymous user. This way, your privacy is protected should you decide to stop using the app after a quick try.

Alpholio™ App for Android Trial Notice

You can use the app three times before it will ask you to register with an email address. You will need to select one of your email addresses known to Google Play Services on the device, such as your Gmail address. After this registration, you can use the app an unlimited number of times.

To access the app’s services, tap the “hamburger” menu icon Android Navigation Drawer Icon to the left of Alpholio™ logo, or swipe from the left edge of the screen:

Alpholio™ App for Android Welcome

This will open the navigation drawer:

Alpholio™ App for Android Services

Access any service by tapping a corresponding item in the drawer. We will describe each service in detail in subsequent posts.

You can access additional information about the app from the options menu. On older devices, press the Menu button; on newer ones, tap the overflow menu icon Android Overflow Menu Icon on the action bar:

Alpholio™ App for Android Options Menu

We welcome your feedback about the app — simply tap Settings followed by Contact Us in the Support section:

Alpholio™ App for Android Settings

Give the app a try today:

Get It on Google Play

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Analysis of Franklin Mutual Global Discovery Fund
analysis, foreign equity, mutual fund

This week’s edition of Barron’s profiles the Franklin Mutual Global Discovery Fund (TEDIX; class A shares). This $25.5 billion (all share classes) load fund has a maximum initial sales charge of 5.75%, expense ratio of 1.28% and portfolio turnover of 24%. According to the article

The fund’s results have been especially good in down markets, such as 2001, though performance tends to trail rival funds during rallies, as occurred in 2012. Over the past five years, Franklin Mutual Global Discovery has returned an average annualized 8.6%, versus 6.5% for the MSCI World Index.

The current two managers took over the fund in December 2009. Therefore, this analysis will only consider the period from January 2010 onwards.

The two benchmarks for the fund are the S&P 500® index and the MSCI World index. One of the available implementations of the former is the iShares Core S&P 500 ETF (IVV). According to Alpholio™’s calculations, since early 2010 the fund returned more than this ETF in only about 6.4% of all rolling 12-month periods; the average underperformance was about 5%. Given the global nature of the fund, this index does not appear to be a truly applicable benchmark.

The second index can be accessed through the iShares MSCI World ETF (URTH). Unfortunately, this ETF became available only in January 2012. Since then, the fund beat that ETF in about 29% of all rolling 12-month periods, with mean and median underperformance of 0.6% and 1.0%, respectively.

Let’s take a look at the Franklin Mutual Global Discovery’s performance using Alpholio™’s methodology, in which the membership of ETFs in the reference portfolio is fixed but their weights can fluctuate. Here is the resulting cumulative RealAlpha™ chart for the fund:

Cumulative RealAlpha™ for Franklin Mutual Global Discovery (TEDIX)

Over the almost five years under current management, the fund generated a modest amount of RealAlpha™ (2.33% and 0.46% for the annualized discounted regular and lag RealAlpha™, respectively). The lag cumulative RealAlpha™ curve was significantly below the regular one. This indicates that new investment ideas did not work out as well as expected. In other words, in many sub-periods investors would have been better off by keeping the previously established reference ETF portfolio rather than following the fund. (To learn more about this aspect of the analysis, please visit our FAQ.)

The annualized standard deviation for the fund in this analysis period was about 11.3%, or close to 0.7% higher than that of the reference ETF portfolio. However, the fund’s volatility was low compared to that of the entire US market, as underscored by the RealBeta™ of only 0.7.

Here is a chart of ETF weights in the reference portfolio of the fund over the same analysis period:

Reference Weights for Franklin Mutual Global Discovery (TEDIX)

The fund’s equivalent position with the largest average weight of 31.7% was in the iShares 1-3 Year Treasury Bond ETF (SHY). This position represents cash, fixed-income and other low-volatility holdings of the fund. For example, as of the end of September 2014, the fund had about 9.1% of assets in cash and “other net assets.” Interestingly, the most recently published holdings included a Puerto Rico long bond with maturity date of 2035, a risky position an investor would probably not expect in this global equity fund.

The fund’s equivalent stock positions included the Vanguard Value ETF (VTV; average weight of 28.2%), iShares MSCI United Kingdom ETF (EWU; 8.3%), iShares MSCI Germany ETF (EWG; 8.0%), PowerShares Dynamic Market Portfolio (PWC; 7.8%), and SPDR® EURO STOXX 50® ETF (FEZ; 3.5%). The Other component in the above chart collectively represents six additional ETFs with smaller average weights.

Under current management since late 2009, the Franklin Mutual Global Discovery Fund added a modest amount of value on a truly risk adjusted basis. The fund’s hefty front load also diminishes its appeal. Finally, despite its low turnover the fund had substantial distributions in each of the past three years, which made it less attractive for taxable accounts.

To learn more about the Franklin Mutual Global Discovery and other mutual funds, please register on our website.


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Analysis of Hotchkis&Wiley Value Opportunities Fund
analysis, mutual fund

Today’s profile in Barron’s features the Hotchkis & Wiley Value Opportunities Fund (HWAAX, Class A shares; HWACX, Class C shares; HWAIX, Class I shares). Class A shares of this $538 million fund have a front-end sales charge of up to 5.25%, expense ratio of 1.25% and turnover ratio of 45%.

According to the article:

This go-anywhere, highly concentrated style can produce above-average performance, as well as above-average volatility—such as in 2011, when it was down 7%. Over the longer term, however, the fund has beaten the market and its peers (Morningstar puts it in the mid-value category), with average annual returns of 10% over the past decade, and nearly 20% over the past five years, better than 98% of its peers.

There is a clear problem with the fund’s classification. The fund “seeks to own companies, regardless of market capitalization” and “may also own preferred stock, fixed income securities.” Morningstar currently shows that about half of the fund’s portfolio is in “giant-cap” equities, totally absent from the Russell Midcap® Value Index, an analyst-assigned benchmark. Nevertheless, if this benchmark were to be used, its accessible realization is the iShares Russell Mid-Cap Value ETF (IWS). Alpholio™ calculates that since late 2004, the fund returned more than the ETF in about 50% of all rolling 12-month periods, with a median outperformance of only 0.14%.

The fund’s prospectus benchmark is the S&P 500® index. One of practical implementations of the index is the SPDR® S&P 500® ETF (SPY). Alpholio™ estimates that over ten years the fund beat that ETF in almost 55% of all rolling 12-month period by a median of about 1.2%.

Alpholio™’s methodology is much-better suited to the analysis of a multi-cap, go-anywhere fund because it does not attempt to shoehorn the fund into a narrow category. Instead, without any preconception Alpholio™ finds a collection of ETFs that best match a given fund.

In the simplest approach, both membership and weights of ETFs in the reference portfolio are fixed. Such an analysis indicates that the fund had a significant exposure to the finance sector: a 29.3% weight of the iShares U.S. Financial Services ETF (IYG). This exposure, well in excess of approximately 16% sector’s weighting in the S&P 500® index, is corroborated by a further analysis (see below).

In a more elaborate Alpholio™ approach, ETF membership in the reference portfolio is fixed, but ETF weights can fluctuate over time to better match the analyzed fund’s holdings. Here is the resulting chart of cumulative RealAlpha™ for Hotchkis & Wiley Value Opportunities:

Cumulative RealAlpha™ for HWAAX

In the three years from early 2005, the fund generated about minus 30% of cumulative RealAlpha™. After that, the cumulative RealAlpha™ strongly rebounded, with an exception of a brief pullback in the second half of 2011. However, it took about five years for the cumulative RealAlpha™ to recover to the initial level. Overall, the fund generated only about 2% of annualized discounted RealAlpha™ over the entire analysis period. That is because early losses were weighted more than subsequent gains (to learn more, please visit our FAQ).

The fund was quite volatile: At almost 21%, its standard deviation was about 4% higher than that of the reference ETF portfolio. This indicates that the reference portfolio was unable to fully track the fund, which could be expected given the fund’s concentrated holdings. (For example, at present top-ten positions account for almost 51% of assets.) A RealBeta™ of 1.15 also underscores the risk of the fund.

The following chart shows the composition of the reference ETF portfolio in the same analysis period as above:

Reference Weights for HWAAX

The fund’s top equivalent positions were in the iShares S&P 100 ETF (OEF; average weight of 24.4%), Vanguard Financials ETF (VFH; 21.4%), iShares Morningstar Small-Cap Value ETF (JKL; 14.7%), Guggenheim S&P 500® Equal Weight ETF (RSP; 12.2%), iShares Transportation Average ETF (IYT; 7.4%), and iShares Morningstar Mid-Cap Growth ETF (JKH; 5.9%). The Other component in the chart collectively represents four additional ETFs with smaller average weights.

The fund continues to have a heavy exposure to financials. As of the end of August 2014, it had over 32% of assets in the insurance and banks industries. The fund’s top position of 11.5% in a single financial stock (AIG) is worrisome, despite management’s assurance that it is not as risky as it would seem.

Over the past ten years, the Hotchkis & Wiley Value Opportunities Fund has added value for its shareholders but at the expense of elevated volatility of returns. The fund’s concentrated portfolio as well as substantial sector and single equity bets can potentially backfire. In addition, in each of the past two calendar years the fund generated total distributions of about 6-7% of the net asset value, and with a large portion in short-term capital gains. This diminished the fund’s appeal for taxable accounts.

To learn more about the Hotchkis & Wiley Value Opportunities and other mutual funds, please register on our website.


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