12 months ago • 10 mins
When it comes to allocating your investments, there are “tactical” ways to go about it – and some of these strategies have very impressive track records. So it’s about time we dig into a few of them: in this special three-part series, you’ll learn three different tactical asset allocation strategies to inspire your investing and boost your portfolio. First up: Defensive Asset Allocation (DAA).
Tactical asset allocation is a “dynamic” investment strategy – one where you’re actively adjusting your portfolio to take advantage of market trends, aiming to maximize returns while minimizing risks. The best part about tactical asset allocation: it’s relatively easy to implement using exchange-traded funds (ETFs).
DAA is a “momentum strategy”: you invest in asset classes that have recently performed well based on the tendency of them to continue to perform well in the near future. Momentum is a well-studied phenomenon within the academic community. One of the reasons it works is the fact that investors have deep behavioral biases – like herding and extrapolation.
There’s another neat concept embedded in the DAA strategy: a market timing signal as a way to sidestep crashes. That is, the strategy tries to shift to safe-haven assets (like government bonds) when a market crash is imminent. It does this when certain groups of assets – which the strategy refers to as a “canary universe” – start to display negative momentum. This functions as an early warning system to exit the market, similar to the proverbial canary in the coal mine.
DAA trades 16 different ETFs, each representing a different asset class or subclass. The 16 ETFs are organized into three different groups, or “universes”: risk, canary, and cash.
The strategy trades and rebalances monthly. So on the last trading day of the month, you would do the following.
Calculate a momentum score for each of the 16 ETFs. The authors of the strategy use a “13612W” score – a bit of a mouthful, but here’s how you work it out:
13612W = (12 (p0 / p1 – 1)) + (4 (p0 / p3 – 1)) + (2 * (p0 / p6 – 1)) + (p0 / p12 – 1)
Where p0 = the ETF’s price today, p1 = the ETF’s price one month ago, p3 = the ETF’s price three months ago, and so on.
The first term – (p0 / p1 – 1) – is the ETF’s percentage price change over the past month, and the second term – (p0 / p3 – 1) – is the ETF’s percentage price change over the past three months. You get the idea. Also, notice how the 13612W score overweights the most recent returns: the ETF’s one-month performance is multiplied by 12, its three-month performance is multiplied by a third of that (four), and so on. This gives more recent returns a greater impact on the strategy than older ones.
Look at the 13612W scores of the two ETFs in the canary universe. There are three possible scenarios:
I’ve summarized these portfolio allocations in the table below.
Hold on to the ETFs until the last trading day of the following month. Then go back and repeat the steps above. Even if the ETFs you end up investing in don’t change, repeating the steps above ensures that you rebalance your portfolio to the strategy’s intended weights – that’s important.
Performance tracker AllocateSmartly has compiled robust data on the DAA strategy’s historical performance, even projecting it back to 1973. While looking at this in isolation is all well and good, it’s more informative to compare performance to a benchmark – in this case, a basic 60/40 strategy that invests 60% in US stocks and 40% in US government bonds.
Measured over the past 50 years or so, the DAA strategy generated an impressive average annual return of 14.7% – significantly higher than the 60/40 benchmark’s 9.7%. What’s more, it managed this while taking on slightly less risk: the DAA strategy’s volatility was 9.0% over the same period – lower than the 60/40 portfolio’s 9.8%. Taken together, this means that the DAA strategy generated much higher investment returns per unit of risk.
Another important measure for assessing a strategy’s risk is maximum drawdown (MDD): the largest peak-to-trough decline in the value of a portfolio. The DAA strategy’s MDD over the past half-century was a relatively modest 12% – less than half the 60/40 benchmark’s 30%.
DAA has an impressive track record: it has generated excellent investment returns per unit of risk. And the strategy’s modest 12% MDD suggests it has sidestepped market crashes well historically: its use of a canary universe as a market timing signal has worked. So if you apply this strategy to your investments, you may well boost your returns. Having said that, the old investment adage – “past performance is no guarantee of future results” – applies here: the DAA strategy may not necessarily perform well in the future and/or may experience a larger peak-to-trough loss of 12% at some point.
DAA has two drawbacks. First, it moves into bonds when one or two of the canary ETFs start exhibiting negative momentum. This reliance on bonds has worked well in the past considering the decades-long bond bull market. But going forward, bond yields cannot – mathematically speaking – go much lower. Weak bond returns today – and the prospect of negative returns in the future if bond yields rise meaningfully – could detract from the DAA strategy’s future performance. Second, the strategy has a relatively high turnover, with an average of 46 trades a year. Such a high number racks up trading fees that detract from investment performance.
That’s it for part one: DAA. Don’t miss part two of this series, where I’ll break down another high-performing tactical asset allocation strategy.
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