Investors Can Bundle Multiple Trend Strategies To Smooth Out Returns And Reduce Volatility

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In his recent interview with Tobias, Adam Butler, CIO of ReSolve Asset Management discusses how investors can bundle multiple trend strategies to smooth out earnings and reduce volatility over time. Here’s an excerpt from the interview:

Tobias Carlisle: So then how do you then work out how much you allocate to each of these different trend models? So, you say the one that has performed the best gets a little bit more or do you just say we don’t know which one is going to be performing the best over this period of time so we equal weight, or do you use that risk parity approach?

Adam Butler: Well, it depends on what strategy. I mean, we’re launching a strategy, it’s a global equity momentum index that Corey at Newfound…

Tobias Carlisle: Corey Hoffstein

Adam Butler: … and ReSolve are co-launching, yeah, and we’re just sort of equal weighting a variety of different trend specifications. We’ve got models that allow us to determine analytically over a long horizon what the correlation should be, for example, between a one month time series trend specification and a 12 month time series trend specification, or a 12 month time series trend and a 10 month 200 moving average cross trend specification should be.

These have actually well-defined analytical correlation relationships that you can use to maximize diversification across the trend specifications, and we do use those internally. But I mean, even just equal weighting them is more advantageous than having to look across all of the different trend strategies or trend specifications historically and say which one of them has outperformed in sample.

Adam Butler: I mean, we just wrote a paper on this global equity momentum concept, and you probably know Gary Antonacci, who I’m a big fan, and he’s a great guy and a smart guy. And I wrote a paper in 2012 on this idea of dual momentum. And one of the strategies was where you want to be invested in equities subject to the fact that equities are in a positive trend, and then if equities are on a positive trend, then you want to be in either US equities or international equities depending on which one of those has the highest momentum. And he defined trend and momentum based on the 12 month return, right? Which is a perfectly valid specification. And if you look back to 1950, that strategy has performed very, very well, right?

Adam Butler: But what’s interesting is that the original study that he did in 2012 used monthly data from 1974 to 2011, and he used the 12 month approach, and he showed the 12 month approach was good, and then he expanded it, he got new data and he expanded it back to 1950, and then from 2011 to, I don’t know, 2018 or ’17 or something.

Right. Which is great because now we have an out of sample sample, right? And so what’s interesting is you’ve got this specification that worked the best from 1974 to 2011, and then if you look at the performance of that specification, that 12 month specification on the period from 1950 to 1973 and from 2012 to 2018, what you find is that that strategy performs at about the median of all of these other different trend specifications, which is exactly what you’d expect if your prior belief is that all of these different trend specifications are equally valid, right? If they’re all equally valid, then over the long run, they’re all going to converge to the median, right?

Adam Butler: But what’s great about it is that they all have the same expected return but they’re not perfectly correlated to one another. So when you put them all together, you get the same expected return, but you get a lower volatility. And so that means you’ve got, over a rolling five year period, the expected tail loss is much smaller if you use the ensemble of all of them than if you use any single specification. Your expected maximum draw down is smaller.

Tobias Carlisle: Goes up.

Adam Butler: The expected shortfalls is smaller. Like there’s all these benefits just from ensembling all these different methods.

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