The term "black swan" has become synonymous with "rare event" – but the original concept proposed by Nassim Taleb was a bit more nuanced.  The term came from the latin phrase "rara avis in terris nigroque simillima cygno": a rare bird in the lands and very much like a black swan.  The phrase originated at a time when it was assumed that a black swan didn't exist and over the years the phrase came to embody the idea of an impossibility.

Except for when, in 1697, the Dutch explorer Willem de Vlamingh discovered black swans in Western Australia.  Whoops.  So much for the impossible event.

After that fateful day, the term black swan has meant something more subtle than just "rare event."  The black swan story quite elegantly captures the notion that proving something as true is much more onerous than disproving it.  While all historical records of swans up until 1697 showed that they had white feathers, it took only a single black swan to disprove an entire history of evidence.  For something to be true, it must be proved across all cases; for something to be false, it must only be disproved in a single case.  While all swans were white across all historical evidence, a single example in 1697 demonstrated just how fragile the evidence was.

I bring this up because investors build their portfolios upon a foundation of assumptions, and frequently these assumptions are rooted in empirical evidence.  Most prevalent is the notion of stocks for the long run: that over a long enough horizon, stocks have a significant positive expected return.  Or that value stocks will generate a significant premium to the market over a long period of time.  Or that a well diversified portfolio will mitigate a disproportional amount of risk to the expected return reduced.  These are all commonly accepted market truisms, when in reality they have just been empirically true and theoretically postulated.

Personally, I'm starting to think Japan is starting to become the black swan of stocks for the long run...

I don't think you can construct a portfolio without making some sort of assumption, but I also believe that the fewer assumptions you make, the more robust you are to a future that looks nothing like the past.  There is little harm in evaluating your assumptions and identifying which among them are the most dangerous to you if they prove to be wrong.  Assumptions are a risk you have to bear – but it is worth knowing the potential cost of those risks.

One misconception we hear fairly frequently is that we utilize sectors within our models because we are trying to perform sector rotation.  Sector rotation is the notion that certain sectors outperform during different periods of the economic cycle.  For example, in theory, rotating into utilities should help mitigate losses in a recession while rotating into technology will help us capture outsized returns as the market recovers.

SectorCycle

We actually use sectors because it helps us take a diversified approach to momentum-based investing, helping mitigate the impact of model risk.  But let us assume we did actually try to perform sector rotation.  Our embedded assumption is that sectors follow a similar pattern over economic cycles.  In my opinion, it takes but a few counter-examples – our black swans – to disprove this notion.  Utilities, for example, were the second worst performing sector during the dot-com crash and the second best in the following bull market.  Technology was actually the safe haven in the credit crisis.

Fortunately, Jacobsen, Stangl, and Visaltanachoti decided to go much deeper than a few counter-examples in Sector Rotation across the Business Cycle.  I think the study is extremely cleverly designed in that it actually assumes an investor that has perfect foresight of the business cycle stages and tests whether with this perfect foresight a sector rotation methodology adds value.  From 1948-2007, they find that with perfect foresight and zero transaction costs, they generate, at best, an excess 2.3 percent annual outperformance.  It sounds like a lot, but under more realistic settings, the outperformance disappears.  Even with perfect foresight, sector rotation barely works.  Imagine trying it without the crystal ball...

I bring this up because often the simplest assumptions are never tested because they sound like common sense: they are commonly held truisms in the market place.  But many of the market's truisms are simply built on empirical evidence, and we should always remember that all the empirical evidence in the world cannot make something true – but a single counter-example can make it false.

Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients.

Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn.

Corey is a frequent speaker on industry panels and contributes to ETF.com, ETF Trends, and Forbes.com’s Great Speculations blog. He was named a 2014 ETF All Star by ETF.com.

Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University.

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