Tuesday, December 20, 2016

How To Beat 5 Global Asset Managers Using An ETF Portfolio


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%.
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


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)
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.
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.