We track more than 60 tactical asset allocation strategies. In this post we look at which of those strategies are most often recommended by common portfolio optimization techniques, and why strategies that “play well with others” are not always the best strategies.
First, a little background for the uninitiated:
Members can combine the strategies we track into what we call “Model Portfolios”. Combining strategies in this way reduces the risk of any single strategy going off the rails and helps to provide smoother, more consistent investment returns.
Deciding which strategies to trade in your Model Portfolio and how much to allocate to each can be daunting. To make that process easier, we provide Model Portfolios that have been optimal in the past based on common portfolio optimization techniques, such as maximizing the Sharpe Ratio or minimizing volatility. Members: visit the Portfolio Optimizer.
Certain strategies consistently bubble to the top of these optimal portfolios. They are often not the best standalone choices, but they “play well with others”, meaning they exhibit some unique quality (usually low correlation to other strategies) that makes them effective when traded in combination.
Strategies that play well with others when return is a factor:
Most of the optimal portfolios we provide are based on investment objectives that take into account return. These include maximizing the Sharpe Ratio or Sortino Ratio (two measures of risk-adjusted performance), targeting the return of the S&P 500, or targeting the risk of the 60/40 benchmark.
Across these 5 objectives (both with and without shrinkage applied, for a total of 10 optimizations), the following strategies are the most heavily weighted:
| Most Heavily Weighted Strategies When Return Considered Includes 5 Investment Objectives, With and w/o Shrinkage |
|
|---|---|
| Strategy | Links to More Info |
| Aspect Partners’ Risk Managed Momentum | members | public |
| Vigilant Asset Allocation – Aggressive | members | public |
| Risk Premium Value – Best Value | members | public |
What factors help a strategy make this short list of key strategies? It’s not just performance. Strategies that play well with others don’t necessarily equate to the “best” strategies. The strategies that make this list tend to exhibit both strong performance relative to risk AND behave very differently than other strategies that we track (for the geeks: the average pair-wise correlation across all strategies is 57%, but only 42% for these three).
By combining return streams that behave very differently from one another, we lower total risk while maintaining long-term performance. Put another way, we improve risk-adjusted performance.
Improving risk-adjusted performance has tangible benefits like increasing safe withdrawal rates, and perhaps more importantly, intangible benefits like making an investment plan less stressful to follow, ensuring that we stay the course during the hard times.
Strategies that play well with others when return is NOT a factor:
We also provide investment objectives that don’t consider return. These are only based on how unique the strategies are and how little volatility (a proxy for risk) they’ve exhibited. These objectives include minimizing variance, minimizing correlation and maximizing diversification.
The most heavily weighed strategies across these non-return optimization objectives are:
| Most Heavily Weighted Strategies When Return Not Considered Includes 3 Investment Objectives, With and w/o Shrinkage |
|
|---|---|
| Strategy | Links to More Info |
| Novell’s Tactical Bond Strategy | members | public |
| Momentum Turning Points | members | public |
| Growth-Trend Timing – Original | members | public |
The big difference between these results and the previous ones is that here, how well the strategy has performed is ignored.
That may seem counterintuitive, but some investors see future returns as difficult to predict, but correlation and volatility as more stable over time. For those investors, a portfolio that ignores return may make more sense.
Tax efficient strategies that play well with others:
We also provide all of these same optimized portfolios based only on strategies that have been tax efficient. We define tax efficient here as at least 75% of historical profits coming from long-term capital gains or dividends (of course, that only matters if trading in a taxable account).
Here is the list of the most heavily weighted tax-efficient strategies. We’ve grouped together all investment objectives, regardless of whether return is considered:
| Most Heavily Weighted Tax Efficient Strategies Includes 8 Investment Objectives, With and w/o Shrinkage |
|
|---|---|
| Strategy | Links to More Info |
| Global Risk Parity Trend Following | members | public |
| Risk Premium Value – Best Value | members | public |
| Composite Dual Momentum | members | public |
Strategies that play well with others are not necessarily the “best” strategies:
As a general concept, portfolio optimization favors strategies that behave very differently than other strategies in the portfolio (in other words, exhibit low correlation), because the more unalike the strategies are, the more risk is reduced.
That means strategies that are “within themselves” more diversified – by blending components of multiple other strategies we track – are less likely to be chosen.
Our own Meta Strategy is an obvious example. Meta is a combination of 10 strategies on the platform, so it is by definition a little bit of everything (for the geeks: its average pair-wise correlation of 68% is the second highest of any strategy we track). So despite strong historical performance, it will rarely be chosen by the optimizer.
Put another way, strategies that are heavily favored by the portfolio optimizer shouldn’t be confused with the “best”. If trading just one or two strategies, the best strategies are likely a better choice. If combining many strategies together, less good but more diverse strategies can be more effective.
Pro tip: Members can put less weight on picking low correlation strategies and more weight on picking high performing strategies by clicking “Shrink Estimators” on the portfolio optimizer.
This is only a starting point:
It’s important to note that there are all sorts of factors that may be important to a member that the portfolio optimizer does not consider. Perhaps the member prioritizes minimizing exposure to rising interest rates (there’s a report for that), or maximizing safe withdrawal rates (there’s a report for that too) or has some other unique approach to judging strategies. The possibilities are endless. The point is that the portfolio optimizer should only be viewed as a starting point in your research.
Quick note: the impetus for this analysis:
We made a change to the portfolio optimizer recently, and we recommend you take a look if you haven’t reviewed the tool in a while.
Previously, we set no upper bounds on how much the optimizer could allocate to a given strategy. We now limit allocation to each strategy to ensure reasonably diversified results. Those limits are set based on the size of the portfolio. For 10 strategy portfolios, no strategy will be allocated more than 20%. For 5 strategy portfolios, 40%. And for 3 strategy portfolios, 50%.
This has no effect on your personal Model Portfolios, which can be configured however you see fit.
While working under the hood, we were inspired to put together this post because we found it interesting how certain strategies consistently bubbled to the top of various portfolios. We wanted to talk about why that’s the case, and to make clear that just because a strategy doesn’t appear often on the optimizer, it’s not necessarily an indication that the strategy isn’t worthwhile.
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