Model Portfolio Results from 2002 to 2017


The world of investment management is filled with backtests — the use of historical data to show how a proposed investment strategy or portfolio would have performed in the past. The problem is that this creates a strong temptation to tweak proposed strategies so that their backtests produce impressive results.

However, in a complex adaptive system like the global economy and financial market there is a strong likelihood that the future will not perfectly resemble the past. Put differently, in such systems it is often the case that the harder you try to fit your model to historical data, the less robust it will be to future uncertainties.

It is therefore highly interesting for investors to see how real, implemented strategies actually play out. Unfortunately, the in this case the problem is "survivorship bias", which is a fancy way of saying that this type of historical analysis can lead to overoptimism and overconfidence because poorly performing strategies and funds are often killed off quickly, and disappear from data sets without leaving a long-term track record to examine.

Our time-out from publishing for a few years deprived us of the opportunity to quickly tweak our model portfolios had they not been working, or had we simply lost confidence in our methodology when financial markets hit hard times. We can therefore look at 15 years of historical data — from December 2002 to December 2017 — to see (warts and all) how they actually performed.

Methodology

Our model portfolios were based on a regime switching model. We assumed that the financial markets could be in one of three regimes, that we termed normal times, high inflation, and high uncertainty. Based on our analysis of historical time series data, we estimated the probability that, conditional on being in one regime in a given year, the system would switch to one of the two others.

For each regime, we then estimated asset class inputs, including the average real return, standard deviation of return, and correlation with the real returns on other asset classes. To do this, we combined historical data with the outputs from our asset pricing model. Within each regime we assumed Gaussian/normal distributions of asset class returns. However, the different distributions within each regime and use of the regime switching model produced an aggregate distribution of asset class returns that was quite close to the distribution features observed in the historical data (i.e., fat tails, clustered volatility, etc.).

We used broad asset class definitions, and only included those classes for which retail index fund products were available at the end of 2002. As the introduction of new products made more asset classes investable, we updated our model again in 2007 to include them, as well as a weighted mix of uncorrelated alpha products that had become available to retail investors (e.g., equity market neutral and global tactical asset allocation).

The 2002 asset classes (and associated funds) include (1) real return bonds (VIPSX); (2) domestic investment grade bonds (both government and credit) (VBMFX); (3) foreign government bonds (without currency hedging) (PFBDX); (4) commodities (PCRDX); (5) domestic commercial property REITs (VGSIX); (6) domestic equity (VTSMX); (7) foreign developed markets equity (VTMGX); and (8) emerging markets equity (EEIEX).

In 2007, we added (9) foreign commercial property (RWX and VNQI), and (10 timber (a wighted mix of timber REITs RYN and PCL, and WY after it bought the latter).

We also established certain constraints on maximum drawdown and allowable asset allocation solutions, to avoid so-called "corner solutions" where almost all of a portfolio is allocated to one asset class.

Our models also sought to optimize rebalancing frequency to minimize risk and gain incremental return. This was accomplished by setting asset class portfolio weight thresholds that would trigger rebalancing (e.g., 10% above target weight) and when those occurred a target for incrementally overweighting the most underweight asset class or asset classes and underweighting the most overweight (to capture incremental returns from markets overshooting fair value in either direction).

For each long-term real return target (7%, 5%, and 3%), we employed simulation optimization to identify robust portfolios that maximized the probability of achieving the target within he constraints we set. These simulations included both the regime switching model and, within each regime, the range of possible return on each asset class.

Our Benchmarks

We have judged the performance of our model portfolios against two benchmarks. The first is whether they have met their compound annual (i.e., geometric average) real return targets. The second is how they compared to the results from an equally weighted mix of the broad asset classes we used.

To be clear, equal weighting implies that an investor has no confidence in their ability to predict meaningful differences in future asset class returns, standard deviations, and correlations. Put differently, it implies an investor has no confidence in their ability to predict future asset class exposures to the factors that drive returns (e.g., macroeconomic and investor behavior variables), and/or in their ability to predict the future returns associated with different degrees of exposure to those factors.

Model Portfolio Results

The following table shows the nominal return and standard deviation for our target return and equally weighted portfolios along with (a) the amount of return per unit of standard deviation; (b) the value at the end of December 2017 of an investment of 100 made at the end of December 2002; (c) the compound nominal return on this investment (i.e., the geometric average), and the compound real return (which can be compared to the portfolio target).

One equally weighted portfolio keeps the same eight asset classes for all 15 years. The second adds two new asset classes in 2007. The 7%, 5%, and 3% portfolios also added allocations to active alpha strategies that were expected to have a low correlation with domestic equity returns.

Please keep some caveats in mind when looking at these results:

  • We do not adjust for annual expense charges on the funds whose results we track
  • We assume constant asset allocation, and do not include the expenses associated with this. Nor do we show the incremental impact of the model portfolio's systematic rebalancing strategy.
  • Most important, we do not show the very substantial impact from what we call "episodic" rebalancing to avoid losses from substantial asset class overvaluations (e.g., our May 2007 warning that severe financial turbulence lay ahead, and our recommendation to move into cash).

Model and Benchmark (Equally Weighted) Portfolios

Average Nominal Return, 2002-2017

Standard Deviation of Nominal Returns

Average Return/Standard Deviation

Value at Dec 2017 of 100 Invested at Dec 2002

Compound (Geometric Average) Real Return, 2002-2017

Equally Weighted 8 Asset Class Portfolio for all 15 Years

8.80%

14.70%

.60

308

5.69%

Equally Weighted 8 Asset Class Portfolio 2002-2007, then 10

9.10%

14.60%

.62

322

6.01%

7% Target Real Return Portfolio. Up to 8 Asset Classes to 2007; then up to 11

10.60%

17.60%

.60

366

6.95%

5% Target Real Return Portfolio. Up to 8 Asset Classes to 2007; then up to 11

8.20%

13.50%

.61

291

5.30%

3% Target Real Return Portfolio. Up to 8 Asset Classes to 2007; then up to 11

6.00%

8.70%

.69

227

3.53%

7% Target Real Return Portfolio. 2002 Weights for All 15 Years

9.60%

15.90%

.60

337

6.35%

5% Target Real Return Portfolio. 2002 Weights for All 15 years

9.20%

13.20%

.70

336

6.32%

3% Target Real Return Portfolio. 2002 Weights for All 15 Years

5.90%

7.60%

.78

229

3.59%

You can see the asset class weights for the 2007 real target return portfolios here.

This table highlights a number of important points:

  • The equally weighted portfolios have, over the 15 year period from December 2002 to December 2017, delivered compound real returns of 5.69% to 6.01% — even taking into account the 2008 Global Financial Crisis. Not bad at all for very little work!

  • That said, it took more than two years for our equally weighted portfolios to return to their December 2007 values after the Global Financial Crisis hit the markets in 2008. Looking at this a different way, if the equally weighted portfolio that was updated in 2007 had avoided the 2008 market downturn (say, by heeding the warning we published in May 2007), the ending value in 2017 (after 15 years) of an original investment of 100 would have been 454 instead of 322 — a 41% difference. As we repeatedly emphasize, when it comes to achieving long-term investing goals, avoiding large drawdowns is critical.

  • The 7%, 5%, and 3% portfolios were meeting their compound average real return targets by the end of 2017, and their standard deviations (i.e., one measure of their degree of risk) were in line with their returns. That said, one can legitimately ask how much incremental benefit was realized through the use of these portfolios instead of the equally weighted approach. The strongest argument in favor of the model portfolio approach is the 3% target, where the risk reduction benefits compared to the equally rated approach were significant. However, to be honest, the argument for the 7% portfolio seems weaker, and for the 5% portfolio weakest of all.

  • On balance, based on our 15 year experiment, the argument in favor of the equal weighting approach seems quite strong, unless you are seeking significantly less risk or much higher returns. However, this conclusion comes with a critical caveat: moving away from the equally weighted approach also logically requires that you have some ability to predict either the relative future returns on different asset classes, or their future relative degrees of risk. The latter seems the easier task, which supports the argument in favor of a portfolio that is expected to have less risk than the equally weighted baseline, but also deliver lower returns. Predicting future returns on different asset classes seems to be a much harder task.

  • The 7% compound average real return target looks to be aggressive assuming a an approach that stays fully invested all the time; given this strategy, a 6% target seems to be a more realistic maximum. However, if you assume that our 2007 warning was heeded and an investor had moved significantly into cash, then all the portfolios' returns would have been higher — so assuming the avoidance of large downside losses, reaching the 7% target is possible.

  • With the benefit of 20/20 insight, the addition of foreign commercial property, timber, and uncorrelated alpha strategies at the end of 2007 appears to have been problematic. In the case of the 7% target portfolio, it boosted both returns and risk. But in the case of the 3% target the impact was minimal, and in the 5% case it was negative. To better understand the dynamics involved, the following table presents broad asset class returns from the end of 2007 to the end of 2017:

Asset Class

Value of 100 Invested at 31Dec2007 on 31Dec2017

Compound (Geometric) Average Real Rate of Return

Real Return Bonds

136

1.51%

Domestic Investment Grade Bonds

145

2.20%

Foreign Government Bonds

161

3.24%

Domestic Commercial Property

165

3.51%

Foreign Commercial Property

166

3.55%

Commodities

56

(7.19%)

Timber

174

4.06%

Domestic Equity

228

6.98%

Foreign Developed Market Equity

129

0.95%

Emerging Markets Equity

114

(0.29%)

Uncorrelated Alpha Strategies

156

2.92%


As you can see in the table (and see even more clearly in the time series which is not presented here), for most asset classes the real recovery from the Global Financial Crisis has been slow and painful (e.g., the case of emerging markets). This makes it clear how deceptive (if normal) it can be to let one's feelings be driven by one year nominal returns, even when they are strongly positive.

The table also reinforces the issue we have previously discussed with respect to whether US Equity and Foreign Developed Market Equity should be treated as two separate asset classes. To be sure, over the 2007-2017 period, the former has strongly outperformed, substantially because of its heavier weight in technology. But for an investor seeking exposure to the full range of factors that drive equity market returns, this difference makes a strong case for combining the two into single Developed Markets index fund (e.g., VEA).

Moreover, as previously noted in our discussion of asset classes, the flow of money into futures-based commodity index may well have precipitated a structural change that has caused roll yields to become more negative in many markets, thus depressing the returns on this asset class.

Finally, it is interesting to note the performance of the active strategies we included in our model portfolios at the end of 2007. In theory, they were intended to have a low correlation with the US equity market. Mathematically, this type of investment can have a very beneficial impact on the returns from a diversified mix of broad asset class investments. As it turned out, over the decade ending at the end of 2017, our uncorrelated alpha funds (chiefly equity market neutral and global macro/global tactical asset allocation products like OGNAX and PASAX), this allocation had a beneficial impact on our model portfolios, even though its weighted correlation to US equity returns was .48 (moderate, but not as low as we had hoped).

Hopefully, our discussion of 15 years of model portfolio results has made it clear that
risk management — avoiding large losses — is just as important as asset allocation when it comes to achieving long term investing goals. We will now turn to that critical subject.
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