Tuesday, December 20, 2016

How To Beat 5 Global Asset Managers Using An ETF Portfolio

Summary

Here's the question we wanted answered: "Can we find a do-it-yourself ETF portfolio recipe that beats some of the leading asset managers?".
So we graphed the risk vs. return profile (over the past 10 years) for five leading asset managers.
The five chosen managers/firms are Ivy Asset Management (Ben Inker), GMO, Leuthold, UBS, and BlackRock.
We used an Asset Allocation Fund from each manager to represent their performance.
We found 46 Portfolio Recipes that beat all the managers by generating higher return with less risk.
"There are tons of people who are late to trends... They exist in mutual funds. They exist in clothes. They exist in cars. They exist in lifestyles." -- Jim Cramer
Buying an asset allocation mutual fund is one way to counter the market's volatility. By purchasing a single fund, you get a diversified portfolio that avoids putting everything in one basket. This approach sounds like a plausible plan for tapping the expertise of fund managers to diversify your portfolio. But the statistics show that many of these fund managers are the "late to trends" kind of people in the Jim Cramer quote above.
Over the past 10 years, 81% of Global Funds underperformed their corresponding benchmark, the S&P 1200 Global Index (source: SPIVA Mid-Year 2016 Scorecard). In other words, in most cases, you would have been better off buying index funds instead of an actively managed fund. Although this 81% statistic relates to equity-only funds, our suspicion is that asset allocation funds underperform, too.
So what's an investor to do? What's a better way to create a diversified portfolio instead of using a mediocre asset manager? In this article, we identify dozens of do-it-yourself ETF Portfolio Recipes that beat the famous asset managers.
The Five Funds to Beat
We identified five Asset Allocation Funds from well-known fund companies. We wanted to find Portfolio Recipes that outperform all five funds based on return and risk. To avoid "cherry picking" only easy-to-beat funds, we did not review each fund's performance before choosing them. The five that we selected met the following criteria:
  • Each fund is managed by a notable portfolio manager or company.
  • Each fund uses global asset allocation across multiple asset classes (e.g., stocks, bonds, cash).
  • Each fund has been operating for more than ten years.
  • Each fund is actively managed and re-allocates based on market conditions to minimize risk and maximize return.
The five chosen "funds to beat" are as follows:
  • UBS Global Allocation (MUTF:BPGLX)
  • GMO Global Allocation (MUTF:GMWAX)
  • Ivy Asset Strategy (MUTF:IVAEX)
  • Leuthold Core (MUTF:LCORX)
  • BlackRock Global (MUTF:MALOX)
The Asset Managers' Performance
Our goal is to find Portfolio Recipes with both a higher return and lower risk than all five of the reference portfolios over the past 10 years. Looking back ten years allows us to capture the last major market downturn in 2007-2008. For the return metric, we'll use total annual return including dividends and distributions. For the risk metric, we'll use maximum drawdown, which is the largest peak-to-trough drop in a portfolio's value.
Exhibit A (below) gives us a quick idea how these funds have performed over the past ten years. Ivy Asset Strategy outperforms all other funds in terms of both risk and return. BlackRock Global, which has $40 billion in assets under management, has performed about the same as the Ivy fund.
Exhibit A. Risk and Return of five asset allocation funds
5 asset allocation funds
Since we track the ingredients and performance for over 200 "portfolio recipes" (our name for asset allocation models) at RecipeInvesting.com, we can pit these five funds against a broader set of Portfolio Recipes.
So let's create a scatterplot showing the total return vs. risk for the five managers plus all the other Portfolio Recipes we track at recipeinvesting.com.
Exhibit B (below) shows the results for the 10 years (120 months) ending November 30, 2016. This gives us a visual comparison and allows us to find the winners and losers more quickly. The best portfolios will be nearest to the top left corner of the chart, which represents the ideal combination of high return and low risk.
Exhibit B. Risk vs. Return Scatterplots of Five Asset Allocation Funds vs. Portfolio Recipes
risk vs. return for asset allocation model portfolios
source: VizMetrics Inc.
After this filtering exercise, we found 46 portfolios at recipeinvesting.com that beat all five of the global asset managers. These are shown in the light green area in Exhibit B (above).
By "beat" we mean that these 46 Portfolio Recipes had both a higher return and lower risk than all five reference portfolios. The best reference portfolio generated 5.1% return with 29.2% maximum drawdown. So all the Portfolio Recipes in the green shaded region have a better return with less risk than all five reference portfolios.
  • The blue squares represent tactical Portfolio Recipes that beat the five funds.
  • The purple squares represent strategic (static) ETF Portfolio Recipes that beat the five funds.
  • The green squares represent other managed Portfolio Recipes that beat the five funds.
  • The small gray dots represent all the other Portfolio Recipes tracked at recipeinvesting.com. These did not beat all five of the reference portfolios on both risk and return. However, several of these portfolios (gray dots) did beat the reference portfolios (orange dots) if you only consider risk or return, but not both.
As a benchmark comparison, U.S. Equities (NYSEARCA:SPY), shown as the pink dot, returned 6.8% with a maximum drawdown of 50.8% during the same period. So while SPY had a better return than the five funds, it had a much worse drawdown.
Categorizing the Winners
These 46 winning portfolios can be placed into three categories:
  1. Other active managers: These are mutual funds that have a better risk vs. return profile than all five of the reference portfolios. 7 of the 46 portfolios fall into this category.
  2. Tactical Portfolio Recipes: These are ETF model portfolios that are updated monthly based on an underlying algorithm or asset allocation methodology. 34 of the 46 portfolios fall into this category.
  3. Strategic Portfolio Recipes: These are static ETF model portfolios with ingredients that do not change month-to-month. These recipes hold a fixed set of ETFs which do not change, but the holdings are rebalanced monthly to match the original asset allocation specified by the Portfolio Recipe. 5 of the 46 portfolios fall into this category.
Now let's take a closer look at the Top 3 Portfolio Recipes in each of the above categories (Managed, Tactical, Strategic). We'll rank based on drawdown (lowest drawdown is best). We provide detailed risk and return metrics for each of these portfolios at RecipeInvesting.com.
Top 3 Managed Portfolio Recipes
  • Managed Portfolio #1: Fidelity Strategic Income (MUTF:FSICX) is a multi-strategy income portfolio. This fund returned 5.6% annually over the past 10 years with a maximum drawdown of 16.2%.
  • Managed Portfolio #2: Hundredfold Select (MUTF:SFHYX) uses multiple actively managed diversified and uncorrelated trading strategies in bond and equity markets. This fund returned 5.6% annually over the past 10 years with a maximum drawdown of 16.8%.
  • Managed Portfolio #3: Vanguard Wellesley Admiral (MUTF:VWIAX) follows an investment style of asset allocation with 30% to 50% in equities, mostly in the U.S. market. This fund returned 7.0% annually over the past 10 years with a maximum drawdown of 28.8%.
Top 3 Tactical ETF Portfolio Recipes
  • Tactical Portfolio #1: Minimum CvaR Portfolio (t.cvar) uses a conditional value-at-risk (CVaR) approach for portfolio optimization. The December 2016 ETF ingredients for this recipe are the iShares MSCI EAFE ETF (EFA) (20% allocation), SPDR Gold Trust ETF (GLD) (19%), iShares Russell 2000 ETF (IWM) (9%), and SPY (52%). This recipe returned 8.7% annually over the past 10 years with a maximum drawdown of 11.0%.
  • Tactical Portfolio #2: Minimum Mean Absolute Deviation Portfolio (t.madm) uses a risk-driven portfolio optimization technique. The December 2016 ETF ingredients for this recipe are GLD (29% allocation), SPY (56%), and the iShares 20+ Year Treasury Bond ETF (TLT) (15%). This recipe returned 9.5% annually over the past 10 years with a maximum drawdown of 11.2%.
  • Tactical Portfolio #3: Minimum Downside MAD Portfolio (t.madd) approach is similar to the "t.madm" recipe, but accounts for minimum variance. The December 2016 ETF ingredients for this recipes are similar to t.madm. This recipe returned 9.4% annually over the past 10 years with a maximum drawdown of 11.2%.
Top 3 Strategic ETF Portfolio Recipes
  • Strategic Portfolio #1: Harry Browne-inspired Portfolio Recipe (s.brow) allocates equally to stocks (like the Vanguard Total Stock Market ETF (VTI)), long-term U.S. Treasurys (like the iShares 20+ Year Treasury Bond ETF (TLT)), cash (like the iShares 1-3 Year Treasury Bond ETF (SHY)), and gold (NYSEARCA:GLD). This recipe returned 6.1% annually over the past 10 years with a maximum drawdown of 12.6%.
  • Strategic Portfolio #2: Permanent Plus Portfolio (s.plus) starts with Harry Browne's 4-part portfolio, but then removes the cash component to increase the portfolio's total return (with some increased risk). This recipe returned 7.7% annually over the past 10 years with a maximum drawdown of 17.1%.
  • Strategic Portfolio #3: No Equity Portfolio (s.noeq) holds no stocks and consists of just three ETFs (TLT, GLD, and the Vanguard REIT Index ETF (VNQ)). This recipe returned 6.9% annually over the past 10 years with a maximum drawdown of 19.5%.
Conclusion
Professionally-managed, asset allocation mutual funds haven't kept pace with several other approaches over the past 10 years. We found 46 Portfolio Recipes which outperformed several well-known asset allocation funds in term of both total return and risk.
Investors should consider ETF Portfolio Recipes like the ones uncovered in this article. Investors can employ a do-it-yourself approach or partner with an advisor who knows how to choose and implements asset allocation recipes that produce results.

Tuesday, December 6, 2016

4 Steps To Prepare Your Portfolio For The Coming Uncertainty

Summary

Market volatility is at an historic low, but uncertainty is all around us.
The Minimum Variance Algorithm (MVA) presents an interesting way to build a lower-risk portfolio.
We discuss the steps to choose the best inputs to run this algorithm for creating an effective portfolio.
The resulting MVA portfolio has returned 10% annually over the past 10 years, based on the backtest.
"Never think that lack of variability is stability. Don't confuse lack of volatility with stability, ever."
-- Nassim Nicholas Taleb
For many investors, a primary objective is to safeguard their investment from the market's volatility. The market may seem quiet (or at least favorable) at times, but that does not mean it's stable, as Taleb reminds us.
The benchmark S&P 500 Index has edged closer to an all-time high after Federal Reserve Chair Janet Yellen signaled that an interest rate increase is forthcoming. Also, there is an increased expectation that Donald Trump may stimulate the economy by reducing taxes and increasing infrastructure investment.
Looking at Exhibit A (below) the VIX is hovering near its 12-month low, which also happens to be near its all-time low.
Exhibit A. Chicago Board Option Exchange Volatility Index (VIX), past 12 months
VIX Volatility
Source: Yahoo Finance
The S&P 500 Index is near an all-time high level, and its volatility is close to an all-time low. Even the CBOE S&P 500 Short-Term Volatility Index, which exhibits the market expectation of S&P 500 Index swings in the next nine days, has also reached to its all-time lowest level relative to the VIX.
Exhibit B (below) shows that the trailing 12-month price-to-earnings ratio of the S&P 500 is trading at a post-crisis high of 19.9%. This could point to the current market being overly optimistic.
Exhibit B. S&P 500 Trailing 12-Month Price-to-Earnings Ratio
Trailing 12-month P/E
Using the CME FedWatch Tool, we see that future traders are pricing a more than 90% likelihood of higher interest rates in December. And the U.S. dollar index is currently trading at all-time high. Higher borrowing costs from rising interest rates combined with dollar appreciation may impact the corporate earnings unfavorably. So it's important that we diversify our portfolio so that we don't get burned by focusing too much on a particular asset class such as U.S. equities.
Applying the Minimum Variance Portfolio
We track the ingredients and performance for over 200 "portfolio recipes" (our name for asset allocation models) at RecipeInvesting.com. Several asset allocation portfolios continue to outperform the benchmarks. All of these use portfolio diversification to allocate into different asset classes and then re-allocate monthly. One portfolio diversification technique for reducing risk and maximizing returns is to select assets using the Minimum Variance Algorithm.
The fundamental approach of this portfolio recipe is to allocate each asset class such that the resulting portfolio has the lowest overall covariance. The Minimum Variance Portfolio (which we identify using the tag "t.mva3") gives investors access to a dynamic, tactical portfolio that uses an asset allocation algorithm which maximizes risk-adjusted return and responds to market conditions.
Let's take a closer look at four steps we can take to make the most of this method for creating a portfolio.
Four Steps to build an effective portfolio using the Minimum Variance Algorithm
  1. Choose a set of global asset classes
  2. Choose a reasonable lookback period
  3. Confirm that the results beat the benchmarks
  4. Beware of data snooping
Now let's discuss each of these in more detail, and review the performance of a portfolio that was created using these steps.
Step 1. Choose a set of global asset classes
We want to select a set of asset classes that are not distinct to one country, sector, or asset type. This increases the flexibility of the algorithm, and allows us to benefit from positive global trends, not just U.S. trends. This portfolio recipe invests in liquid, exchange-traded funds (ETFs). The 8 possible asset classes and corresponding ETFs are as follows:
  • U.S. Large Cap Equity (NYSEARCA:SPY)
  • U.S. Small Cap Equity (NYSEARCA:IWM)
  • NASDAQ 100 Equity (NASDAQ:QQQ)
  • U.S. Real Estate (NYSEARCA:IYR)
  • U.S. Long Term Treasury Bonds (NYSEARCA:TLT)
  • Emerging Markets Equity (NYSEARCA:EEM)
  • International Developed Markets Equity (NYSEARCA:EFA)
  • Gold (NYSEARCA:GLD)
Although five of the eight are U.S. asset classes, the algorithm can choose an allocation to any of the assets in any proportion, thereby giving the portfolio global exposure.
Step 2. Choose a reasonable lookback period
The lookback period is the number of prior trading days that the algorithm uses to determine the relationships between the assets and the recommended amount of each asset to own. We want a lookback period that is short enough to adapt to market conditions, but long enough to capture the overall trend without jumping in and out of positions needlessly.
We are not interested in day-trading; we are interested in a portfolio recipe that updates once per month. So we should choose a lookback period that is at least one calendar month in length (approximately 22 trading days) but not so long that we are stuck in unfavorable assets based on their characteristics from many months ago.
We chose a lookback period of 60 trading days or approximately three calendar months for calculating the covariance required by the Minimum Variance Algorithm. This is the same lookback period specified by Michael Kapler in 2011 on his Systematic Investor blog.
Step 3. Confirm that the results beat the benchmarks
We want to make sure that effort to create this portfolio is worthwhile. The portfolio resulting from this algorithm must have the potential of at least outperforming the "no-brainer" benchmarks. For example, why bother with using a tactical algorithm like this if it can't even beat a dead-simple 60/40 (60% stocks and 40% bonds) portfolio?
Exhibit C (below) show portfolios ranked by total return over the past 1, 5, and 10 years. We have compared the t.mva3 portfolio to its tactical peer group and to three benchmarks: the S&P 500 , U.S. Bonds (NYSEARCA:BND), and a balanced portfolio which is 60% Equity (NYSEARCA:VTI) and 40% Bonds . The time period is the span ending November 2016.
  • Over the past year, t.mva3 portfolio has outperformed its peer group, the 60/40 balanced portfolio, and global equities.
  • Over the past five years, t.mva3, with its 7.4% annual return, has lagged SPY's frothy 14.4% return. But it would be overly optimistic to think that SPY will continue to offer this return over the next five years. We need to prepare for more volatility.
  • Over the past 10 years, t.mva3 does considerably better. Remember that only the 10-year period includes the market crisis of 2008. The 5-year column ignores the market turbulence of 2008.
Exhibit C. Total Return (Compound Annual Growth Rate)
Total Return - Minimum Variance Portfolio
Exhibit D (below) gives a visual comparison of risk vs. return using scatterplots for the 1-, 5- and 10-year periods ending November 2016. We compare the Minimum Variance (t.mva3) recipe, shown as an orange dot, to three benchmarks. We use Maximum Drawdown as the risk measure. Standard deviation and downside deviation can also be used to measure risk and we create more scatterplots using those metrics at RecipeInvesting.com.
  • In the 1-year scatterplot we can see the t.mva3 portfolio (the orange dot) slightly above the balanced portfolio (the light blue dot) but with a slightly larger maximum drawdown.
  • Looking at the 10-year scatterplot, the real story emerges. This scatterplot includes the downturn of 2008, so we can see the brutal impact that period had on equity portfolios such as SPY. The dark blue dot represents the S&P 500 Index, using an ETF as a proxy. Note that the SPY blue dot is perilously close to the red frowny face, which is the worst-possible combination of low return with high risk. The orange dot, representing t.mva3, performs much better on a risk-adjusted basis. The t.mva3 orange dot is closer to the ideal top-right corner with green smiley face.
Exhibit D. Risk vs. Return Scatterplots
Risk vs. Return for Minimum Variance Algorithm
The VizMetrics Score is another method of ranking portfolios based on risk vs. return that has been described in a previous article on Seeking Alpha. A score of 100 means that the portfolio has performed, on a risk adjusted basis, better than the best possible portfolio formed from a basket of global asset classes. In other words, the VizMetrics Score is a numerical measure of a portfolio's "northwest-ness" or how close it comes to reaching the ideal upper-left location (near the green smiley face) on the risk vs. return scatterplots.
Exhibit E (below) provides the VizMetrics Score for t.mva3 and benchmarks for the period ending November 2016. Based on the VizMetrics Score, we find t.mva3 portfolio to be a solid performer, and is the only portfolio shown that scores 75 or greater over the 1-, 5-, and 10-year time periods. Additional analysis at Recipeinvesting.com adds the 3-year and 7-year time period for all risk and return metrics.
Exhibit E. The VizMetrics Score for the Minimum Variance Portfolio and Benchmarks
VizMetrics Score
Step 4. Beware of Data Snooping
Data snooping is when an algorithm developer adjusts the input parameters to create the best possible output using historical data. In other words, the developer benefits from perfect hindsight. To minimize this, we have applied the same lookback period of 60 trading days that Michael Kapler used in 2011.
The antidote to data snooping is testing the algorithm out-of-sample, which means seeing how well the portfolio performs after the algorithm has been finalized. We have been running our MVA algorithm (which we identify as "t.mva3") since March 2014, but using Kapler's algorithm from 2011.
Conclusion
Since we don't know which global asset classes will zig upward and which will zag downward in the coming days, we need to be prepared with a strategy that can adapt to changing market conditions. We like the idea of a diversified portfolio that systematically minimizes risk.
The Minimum Variance Algorithm (as implemented in the t.mva3 example) provides a portfolio recipe that could help a prudent investor achieve a long-term return with less risk than the benchmark portfolios.

Wednesday, November 30, 2016

The Adaptive Asset Allocation Portfolio: How To Maximize Return Using Minimum Variance And Momentum

Summary

The Adaptive Asset Allocation (AAA) portfolio combines two different tactical approaches (momentum and minimum variance) into one algorithm.
The intention of this portfolio recipe is to optimize risk-adjusted returns by combining two approaches.
Michael Kapler implemented a version of this portfolio algorithm in 2012 and published his results.
We have updated the analysis and backtesting using current data.
The AAA portfolio has returned 14.8% per year over the past 10 years with a maximum drawdown of 13.4% over that same period.
Adaptive Asset Allocation (AAA) holds an alluring prospect for tactical investors: a nimble portfolio with a risk-adjusted return than beats the benchmarks. By combining two different tactical approaches (momentum and minimum variance) into one algorithm, the adaptive approach builds a portfolio that responds to market conditions with the promise of lower risk.
But does Adaptive Allocation really work? To answer this, let's first understand the analytical basis for this approach, and then we can dive into the results and risk-adjusted returns.
The analytical basis for the Adaptive Asset Allocation Portfolio
In 2012, Macquarie Private Wealth published a paper entitled "Adaptive asset allocation: A primer" using data through May 8, 2012. This paper described a few possible implementations of the methodology. David Varadi then discussed the robustness of this algorithm on his blog, CSSA: New Concepts in Quantitative Research.
In August 2012, Michael Kapler posted his own implementation of Adaptive Asset Allocation on the Systematic Investor Blog. Kapler used the R programming language to implement a version of the AAA algorithm that used 10 asset class ETFs and was rebalanced monthly.
What's the methodology?
The adaptive asset allocation algorithm (or "portfolio recipe") uses two distinct mechanisms to choose assets and percentage allocations for the portfolio.
  1. Momentum. This is defined by the total return over the past 180 trading days.
  2. Minimum variance. According to the Macquarie paper, "The minimum variance algorithm takes into account the volatility and correlations between the Top 5 assets to create the momentum portfolio with the lowest expected portfolio level volatility." This mechanism uses volatility to choose the asset allocation each month, as defined by the standard deviation over the past 20 trading days.

What's the monthly update process?
Let's look in more detail at the step-by-step process for how the algorithm selects the investable assets each month.
  1. Choose the asset universe. This is the set of assets that we will choose from to create the portfolio. Kapler's version of the Adaptive Asset Allocation Portfolio used 10 exchange-traded funds representing global asset classes (SPY, EFA, EWJ, EEM, IYR, RWX, IEF, TLT, DBC, GLD). We have simplified this and our analysis uses a set of nine global asset classes (SPY, EFA, EEM, QQQ, DBC, GLD, TLT, IWM, IYR).
  2. Calculate total return (including dividends) for each ETF in the asset universe over the past 180 trading days. Then rank the ETFs from highest to lowest return.
  3. Choose the top five ETFs based on their total return over the past 180 trading days. The five chosen ETFs will be the ingredients for the portfolio, but the percentage allocation to each ETF is not yet defined.
  4. For each of the five chosen ETFs in this portfolio recipe, apply a volatility metric to determine how much of each ETF to buy. Each ETF will receive a percentage weighting, and the sum of the weightings will be 100%. The percentage weightings are calculated such that the portfolio's volatility is minimized. This is done using a minimum variance algorithm that uses standard deviation with a look-back period of 20 trading days.
  5. At the end of each month, re-run the algorithm to create a new list of the top five ETFs and the percentage allocation to each. Then rebalance the portfolio holdings to match the percentages from the updated portfolio recipe.

This portfolio recipe uses monthly rebalancing, which makes it viable as a tactical, do-it-yourself recipe which can be used by individual investors or financial advisors.
The Results: how has the portfolio performed?
We have been tracking variations of the Adaptive Asset Allocation Portfolio since 2014 at recipeinvesting.com. Our backtests extend back to 2003. The particular variation discussed here is shown on recipeinvesting.com as portfolio "t.aaaf"
Over the past 12 months (ending October 31, 2016) the portfolio's total return is 9.2%. Over the past five years, the portfolio has a total return of 12.9% versus the S&P 500's total return of 5.9%. A balanced portfolio of 60% equities and 40% bonds has returned 7.0% over the past five years.
Exhibit A (below) shows the normalized return of the Adaptive Allocation Portfolio against benchmarks for the past five years. The Adaptive Allocation Portfolio (t.aaaf, in yellow) keeps pace with the S&P 500. We have used an ETF (NYSEARCA:SPY) as a proxy for the S&P 500.
Exhibit A: 5-year total return vs. key benchmarks
Adaptive Asset Allocation Portfolio
Now let's look at risk-adjusted returns. Exhibit B (below) plots the various tactical portfolio recipes that we track at recipeinvesting.com. Each dot represents one portfolio recipe, plotted according to its risk (horizontal axis) and annualized total return (vertical axis).
The green smiley face marks the fabled and elusive "northwest corner," where a perfect low-risk, high-return portfolio would be plotted. Since that perfect portfolio doesn't exist, instead we look for portfolios that are closest to the top left corner.
The yellow dot shows the total return and risk for the Adaptive Asset Allocation Portfolio recipe. For each time period, the AAA portfolio is one of the better choices since it appears closer to the top left corner.
The dark blue dot shows the S&P 500. Note that the AAA portfolio has generated higher return with lower risk over all periods except the 5-year period when the AAA portfolio return was slightly less than SPY.
The purple dot shows U.S. Bonds (NYSEARCA:BND). The light blue dot shows a balanced portfolio recipe consisting of 60% equity and 40% bonds. The risk on these graphs is measured by maximum drawdown (the largest peak to trough loss) over the specified period.
Exhibit B: Risk (horizontal axis) vs. return (vertical axis)
Adaptive Asset Allocation Portfolio
We can also look at standard deviation as a measure of volatility. Using this metric, we can see in Exhibit C (below) that the AAA portfolio (in the row labeled t.aaaf) has consistently shown a lower standard deviation than SPY over all time periods. The Peer Group average is the group of tactical, do-it-yourself portfolios tracked by VizMetrics at www.recipeinvesting.com
Exhibit C: Volatility, as measured by Standard Deviation
data through October 31, 2016
Adaptive Asset Allocation Portfolio
Exhibit D (below) shows the historical return percentages of the Adaptive Asset Allocation recipe (t.aaaf) along with its peers from 2011 to 2015. t.aaaf has lagged SPY in three of the past five years, but t.aaaf's outperformance in 2011 was notable, when it beat SPY by 19.6% that year.
Exhibit D: Historical annual returns
Adaptive Asset Allocation Portfolio
Conclusion
Kapler's Adaptive Asset Allocation methodology showed solid results when he first published in 2012, and we have been able to produce similar results using recent data and a modified set of ETFs -- we use a nine ETFs and Kapler used ten. Our backtested version of the AAA portfolio (t.aaaf) has generated a return of 14.8% over the past 10 years, with less risk than the S&P 500 (using SPY as a proxy). We will continue to track the performance of Adaptive Asset Allocation Portfolio (t.aaaf) at www.recipeinvesting.com.

Thursday, September 1, 2016

Beating The Couch Potato: 20 Portfolios That Outperform Scott Burns's Simple Asset Allocation

Summary

Scott Burns's Couch Potato Portfolio was devised in 1991 as a super-simple way to invest.
New brokerage trading technologies allow investors to create more sophisticated portfolios and rebalance easily. So we evaluate if the Couch Potato Portfolio is still a good approach.
On a risk-adjusted basis, the Couch Potato Portfolio has performed well against other static portfolios.
However, several tactical ETF portfolios offer superior returns with less risk.
In 1991, Dallas Morning News columnist Scott Burns proposed a dead-simple portfolio: invest 50% in the U.S. Total Stock Market and 50% in a U.S. Total Bond Market fund or Treasury Inflation-Protected Securities. This simple portfolio has moved steadily forward with a 6.6% annual return over the past 10 years.
However, do-it-yourself portfolio management has gotten easier. You can now create and rebalance a portfolio using percentage allocations with just a few clicks at FolioInvesting.com or Motif Investing. Also, discount brokers like TD Ameritrade and Interactive Brokers offer rebalancing tools to rebalance your portfolio using percentage allocations instead of having to manually update share quantities.
So with do-it-yourself portfolios easier to build and maintain, and with many portfolio recipes to choose from, is the simplicity of the Couch Potato Portfolio worthwhile? It's super-easy, but are there other portfolios that are just as easy, with better results?
Let's look for model portfolio allocations (or "Portfolio Recipes" in our parlance) that beat the Couch Potato Portfolio.
We'll specify that in order to "beat the Couch Potato Portfolio," a portfolio must have a greater total return over the past 3-, 5-, and 10-year periods. Then we'll apply a risk filter for these portfolios, and also compare them to the S&P 500 benchmark.
Our Approach
  1. Rank asset allocation portfolios by total return over the past 3, 5, and 10 years.
  2. Identify performance winners, based on total return, by comparing returns to the Couch Potato Portfolio.
  3. Create a risk vs. return scatterplot, using the 10-year maximum drawdown to measure risk.
  4. Identify Portfolio Recipes that have greater return and lower risk, when compared to the Couch Potato Portfolio.
Step 1: Rank portfolios by total return over the past 3, 5, and 10 years.
First we rank more than 200 asset allocation portfolios that we track at RecipeInvesting.com to the Couch Potato Portfolio over the past 3, 5, and 10 years.
Step 2: Identify performance winners by comparing ranked portfolios to the Couch Potato Portfolio.
Next we see which of these Portfolio Recipes have beaten the Couch Potato Portfolio's total return over the past 3, 5, and 10 years.
The annual returns for the Couch Potato Portfolio are as follows:
  • 6.4% over the past 3 years
  • 7.5% over the past 5 years
  • 6.6% over the past 10 years
This is based on the following implementation of the Couch Potato portfolio:
  • 50% Vanguard Total Stock Market exchange-traded fund (NYSEARCA:VTI)
  • 50% iShares TIPS Bond exchange-traded fund (NYSEARCA:TIP)
Scott Burns mentions that you can also use a Total Bond Market fund (e.g., BND, AGG, or VBMFX) instead of TIPS. We have also implemented this as a separate variation that we call the "Strategic 50-50 Portfolio."
For the full stats on the Couch Potato, see the Portfolio Recipe Summary page.
We found the following Portfolio Recipes that beat the Couch Potato Portfolio:
  • Over the past 3 years, 76 Portfolio Recipes beat the Couch Potato Portfolio.
  • Over the past 5 years, 66 Portfolio Recipes beat the Couch Potato Portfolio.
  • Over the past 10 years, 74 Portfolio Recipes beat the Couch Potato Portfolio.
But only 46 Portfolio Recipes beat the Couch Potato Portfolio over all three periods (3, 5, and 10 years).
Here are the top Portfolio Recipes in each of three categories, based on annual total return over the past 10 years.
  • In the "Tactical: Do-it-yourself" category, Quartile Sector Rotation (t.srqr) has returned 18.7% per year.
  • In the "Tactical: Managed" category, Berkshire Hathaway (BRK.A) has returned 9.0% per year.
  • In the "Strategic (Static): Do-it-yourself" category, the Talmud Dividend Equities Portfolio (s.talm) has returned 8.9% per year.
Step 3: Create risk vs. return scatterplot
We need to refine the list of 46 portfolios to find the ones that beat Couch Potato not only by having greater total return but also by having less risk.
Let's create a scatterplot to see this risk vs. return relationship clearly.
We like to use the 10-year maximum drawdown for the risk measurement since this time period includes the market downturn of 2008. We also include an S&P 500 portfolio (NYSEARCA:SPY) as a benchmark.
Each portfolio is a dot on the scatterplot below (Exhibit A) and the Couch Potato Portfolio is shown as an orange dot. A portfolio with a higher return than the Couch Potato Portfolio will appear above the orange dot. A portfolio with lower risk will appear to the left of the orange dot. So we look for portfolios that are both above and to the left of the Couch Potato Portfolio's orange dot. This region is shown as the light green shaded area in Exhibit A.
While Exhibit A shows the 10-year risk vs. return for each Portfolio Recipe, our selection criteria also included the 3-year and 5-year total returns. For brevity, we are only showing the 10-year scatterplot.
Exhibit A: Risk vs. Return of Portfolio Recipes for 10 years ending July 31, 2016
  • Couch Potato Portfolio is shown as an orange dot.
  • Strategic Portfolios (fixed allocations) are shown as purple squares.
  • S&P 500 is shown as a dark blue dot.
  • Tactical Do-it-yourself, ETF Portfolios are shown as light blue squares.
  • Tactical Managed portfolios are shown as gray dots.
Step 4: Identify winning portfolios, after risk adjustment
After analyzing all three periods (3, 5, and 10 years), there are 20 Portfolio Recipes that beat the Couch Potato Portfolio in total return and have a lower 10-year maximum drawdown.
Here is a sample of these "better than Couch Potato" Portfolio Recipes, from three categories:
  • In the "Tactical: Do-it-yourself" category, "Target Return Post-Modern Portfolio" (t.cvar) has returned 12.7% per year.
  • In the "Tactical: Managed" category, Janus Balanced (MUTF:JANBX) has returned 7.8% per year.
  • In the "Strategic (Static): Do-it-yourself" category, "Strategic 50-50" (s.5050) has returned 6.8% per year. This Portfolio Recipe uses 50% total U.S. Stock Market and 50% Total U.S. Bond Market (NYSEARCA:BND) so this can be considered a variation of the Couch Potato Portfolio.
Conclusion
The Couch Potato Portfolio is indeed simple and offers a consistent return. 20 Portfolio Recipes beat the Couch Potato Portfolio on a risk-adjusted basis.
  • 3 mutual funds, including JANBX with 7.8% annual return
  • 16 tactical portfolios, including t.cvar with 10.7% annual return
  • 1 static portfolio (s.5050) with 6.8% annual return
If you want to stay on the couch, the Couch Potato Portfolio offers a simple asset allocation with a modest risk-adjusted return.
But if you are willing to get off the couch and expend some slight effort to rebalance monthly, you can find better returns with lower risk.
The rebalancing effort can be greatly reduced by using a "portfolio brokerage" that offers automated rebalancing tools. This allows you to create a portfolio and buy and sell based on allocation percentages rather than share quantities. Examples of Portfolio Brokerages include FolioInvesting.com and Motif Investing.