All Weather Portfolio
alternatives, analysis, asset allocation, hedge fund, portfolio

Today’s post on Yahoo Finance discusses an “all weather” portfolio recommended by one of the most famous hedge fund managers. The portfolio strives to achieve an equal distribution of risk across macro periods of inflation, deflation, high and low economic growth.

The portfolio consists of:

  • 30% stocks
  • 15% intermediate-term government bonds
  • 40% long-term bonds
  • 7.5% gold
  • 7.5% commodities

The portfolio has a large fixed-income component relative to equities to get close to a risk parity (yet, it does not use bond derivatives). The portfolio should be rebalanced at least annually.

Let’s use the Portfolio Service of the Alpholio™ App for Android to analyze this all weather portfolio. To do so, let’s construct a portfolio of ETFs that represent the above asset classes:

These ETFs were selected to have the earliest possible inception dates and lowest sponsor fees (expense ratios). The time span of the analysis is limited by the inception date of DBC. An alternative commodity ETF, the iShares S&P GSCI Commodity-Indexed Trust (GSG), became available about five months after DBC, therefore the latter was chosen. Since about 8% of DBC tracks gold, the weight of IAU is lower than that of DBC by one percentage point (due to the limitation of setting widgets, the app only accepts whole percentage weights).

Here is the setup for the analysis (the Dates, Return Frequency and Rebalance Frequency sections can be expanded by tapping their respective headers):

All Weather Portfolio - Setup

Here are the analysis results for the above portfolio with monthly returns and quarterly rebalancing:

All Weather Portfolio - Quarterly Rebalancing

With semi-annual (i.e. every six months) rebalancing, the all weather portfolio performed slightly better in terms of the higher annualized return and Sharpe ratio as well as smaller maximum drawdown:

All Weather Portfolio - Semi-Annual Rebalancing

Annual rebalancing yielded no further improvement in the annualized return or Sharpe ratio, but reduced the maximum drawdown to 12.1% and lowered the beta to 0.20.

For reference, here are the results for a traditional balanced portfolio, comprised of 60% SPY and 40% of iShares Core U.S. Aggregate Bond ETF (AGG), with monthly returns and semi-annual rebalancing in the same analysis period:

Balanced Portfolio - Semi-Annual Rebalancing

Compared to the traditional balanced portfolio, the all weather portfolio had all the desirable characteristics: a higher annualized return and Sharpe ratio, coupled with a significantly lower beta and maximum drawdown. However, the above analysis covered a prolonged period of decreasing and historically low interest rates that drove the returns of intermediate- and long-term bonds, the dominant positions in the portfolio. In an environment of rising interest rates (generally expected to begin next year) and falling commodity prices (already taking place), a risk-parity oriented portfolio, even with no bond leverage, may suffer.

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Analysis of Morgan Stanley Institutional Growth Fund
analysis, mutual fund

A recent profile in Barron’s features the Morgan Stanley Institutional Growth Fund (MSEGX, Class A retail shares). This $3.5 billion fund has a maximum 5.25% front load, 0.96% expense ratio and 31% turnover. The fund can invest up to 25% of assets in foreign securities. According to the article

The fund is up an average of 10.3% a year over the past decade, better than 94% if its large growth peers.

The primary benchmark for the Morgan Stanley Institutional Growth is the Russell 1000® Growth index. An accessible implementation of this index is the iShares Russell 1000 Growth ETF (IWF). Alpholio™’s calculations show that since mid-2004 (when the current management took over), the fund outperformed the ETF in about 61% of all rolling 12-month periods.

On an annualized basis, the fund returned more than the ETF in the past three-, five- and ten-year periods. However, the fund’s Sharpe ratio was below that of the ETF in the first two periods and just slightly higher in the third one. This was due to the fund’s higher standard deviation (volatility) compared to that of the ETF. (For example, in 2008 the fund lost over 50%, while the ETF about 38%.) As a result, the stated index may not be the most applicable benchmark for the fund.

Let’s take a look at the fund’s performance through the lens of Alpholio™’s methodology, which more accurately adjusts for risk. Here is a chart of the cumulative RealAlpha™ for the fund:

Cumulative RealAlpha™ for MSEGX

In the past ten years, the general trend of cumulative RealAlpha™ for the fund has been negative. As a result, the fund generated about negative 3.6% and negative 2.6% of annualized discounted regular and lag RealAlpha™, respectively (to learn more about the regular and lag RealAlpha™, please consult our FAQ). The fund’s annualized standard deviation was about 19.2%, approximately one percent higher than that of its reference ETF portfolio. The RealBeta™ of about 1.19 underscored the elevated volatility of the fund.

The following chart shows changes of ETF weights in the reference portfolio for the fund in the same analysis period:

Reference Weights for MSEGX

The fund had top equivalent positions in the PowerShares QQQ™ ETF (QQQ; average weight of about 48.8%), iShares Morningstar Mid-Cap Growth ETF (JKH; 29.8%), Vanguard Materials ETF (VAW; 10.3%), iShares MSCI Hong Kong ETF (EWH; 5.7%), SPDR® Dow Jones® REIT ETF (RWR; 3.9%), and iShares MSCI Taiwan ETF (EWT; 1.5%). Since collectively these six ETFs were sufficient to explain the returns of the fund, the Other component in the chart was nil.

The above analysis clearly revealed that the fund’s investments were dominated by the technology sector as well as mid-cap securities. The former was responsible for the increased standard deviation of the fund. Eight of the fund’s top-ten holdings (which together accounted for 48% of assets) are traded on Nasdaq and are members of the index underlying the QQQ ETF. Therefore, the Nasdaq-100 Index®, and its QQQ implementation, would be a better benchmark for the fund. Alpholio™’s calculations indicate that the fund returned more than that ETF in only about 56% of all rolling 12-month periods since mid-2004.

Over the past ten years, the Morgan Stanley Institutional Growth Fund exhibited an unimpressive performance when truly adjusted for risk. The recovery in the fund’s cumulative RealAlpha™ that began in the second quarter of 2013 turned out to be short-lived. Investors should also be mindful of the fund’s heavy orientation toward technology stocks, which makes it more a sector rather than core large-cap growth holding in the overall portfolio. The fund could easily be substituted with a dynamic combination of just six ETFs with superior return and risk results. Finally, the fund’s substantial sales charge does not add to its appeal.

To learn more about the Morgan Stanley Institutional Growth and other mutual funds, please register on our website.

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Merger Arbitrage Funds as Portfolio Diversifiers
alternatives, analysis, app, asset allocation, correlation, portfolio

A recent article in The Wall Street Journal’s Investing in Funds & ETFs report discusses merger arbitrage mutual funds. According to the article, such funds

…may offer an attractive way to diversify away from the risks of stocks or bonds …[but] can’t replace bonds, because their returns aren’t certain and come mostly through any price appreciation, not yield. But held in tandem with bonds, they can offer a way to hedge against interest-rate risk and might cushion part of a portfolio against stock-market volatility

Let’s take a closer look at these statements with the help of a recently introduced Alpholio™ App for Android, and specifically its Portfolio, Correlation, Total Return and Efficient Frontier services. For the purposes of this analysis, the base portfolio consists of 60% SPDR® S&P 500® ETF (SPY) and 40% of the iShares Core U.S. Aggregate Bond ETF (AGG), i.e. a traditional balanced mix of stocks and bonds. Here is the baseline chart with statistics generated from total monthly returns of both ETFs and quarterly rebalancing of the portfolio:

Portfolio 60% SPY + 40% AGG

The reason why the beta of this portfolio is not exactly 0.6 (i.e. equal to the 60% weight of the SPY) is threefold. Alpholio™ uses a broader definition of “the market” than just the S&P 500® index. Also, the correlation between the market and AGG is not zero. Finally, the portfolio is rebalanced quarterly, not monthly, which can lead to a temporary divergence of SPY/AGG weights from the original 60/40% level.

For reference, in the same time frame a portfolio consisting of just the SPY would have an annualized return of 8.52% with a standard deviation of 14.25%, Sharpe ratio of 0.55 and maximum drawdown of 50.8%. Adding AGG to such an equity-only portfolio decreases its return but reduces its volatility even more, thus improving the Sharpe ratio. The maximum drawdown is also significantly diminished.

The article quotes two merger arbitrage funds with substantial assets: The Merger Fund® (MERFX) and The Arbitrage Fund (ARBFX). To effectively diversify the balanced portfolio, should either fund replace a portion of stocks, a portion of bonds, or a combination of both? What should be the extent of such a replacement?

To answer the first question, let’s take a look at the correlation between SPY, AGG and either fund using the Correlation service of the Alpholio™ app. Here is a chart of the rolling 12-month correlation coefficient for monthly returns of SPY and MERFX:

Correlation SPY - MERFX

The starting date of the chart stems from the earliest availability of AGG whose first full monthly return was in October 2003. The average correlation of 0.56 indicates that MERFX was a marginal diversifier for SPY (generally, a correlation of 0.6 or less is desirable). Here is a similar chart for AGG and MERFX:

Correlation AGG - MERFX

The average correlation of just below zero indicates that MERFX was a much better diversifier for AGG than SPY. Similarly, the average correlation between SPY and ARBFX was about 0.42 and virtually zero between AGG and ARBFX. Therefore, to effectively diversify the base portfolio, it should generally be better to allocate more of SPY rather than AGG to MERFX or ARBFX. However, this would also suppress portfolio returns — as the following total return chart shows, MERFX and ARBFX had steadier but smaller cumulative returns than SPY:

Total Return of SPY, MERFX and ARBFX

To answer the second question: a portfolio with the highest Sharpe ratio (i.e. the tangency portfolio) would be mostly composed of AGG and MERFX. Here is an efficient frontier chart in which the current portfolio, depicted by a standalone marker inside the frontier, had 80% in AGG and 20% in MERFX but no SPY and was very close to the tangency portfolio:

Efficient Frontier 0% SPY + 80% AGG + 20% MERFX

Adding MERFX at the expense of SPY decreased the portfolio volatility and increased its Sharpe ratio, but resulted in lower returns. To illustrate this further, here is a chart and statistics for a portfolio that consisted of 45% SPY, 40% AGG and 15% MERFX, rebalanced quarterly:

Portfolio 45% SPY + 40% AGG + 15% MERFX

Ultimately, it is up to the investor to trade off portfolio returns for risk — some may choose to optimize for the highest return per unit of risk, while others may strive for higher returns at the expense of a sub-optimal Sharpe ratio. The Alpholio™ app for Android provides a set of tools that facilitate the exploration of historical data and construction of desired portfolios, with the usual caveat that the past performance is not a guarantee of future results.

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