This is the seventh post in a series discussing some of the problems associated with investing in “smart beta” strategies. For the previous post on how combining factors might result in regular beta, click here.
The Tacoma Narrows bridge spanned the Tacoma Narrows strait in the US state of Washington, and was opened to the public in July 1940. It was the third longest suspension bridge in the world at the time, behind only the Golden Gate Bridge and the George Washington bridge. But 5 months after opening, it collapsed, killing a local cocker spaniel named Tubby.
Engineers had designed the bridge to be in accordance with the best engineering theory at the time, with no reason to doubt its structural integrity. In theory, the bridge worked perfectly well, but soon after its construction, there was clearly something wrong. Whenever the wind blew, the bridge swayed so violently that locals gave the bridge the name ‘Gallopin’ Gertie’. Even a mild wind would cause rolling sections of the bridge to visibly rise and fall.
The engineers hadn’t designed the bridge to correctly account for wind, and 5 months later the bridge collapsed – incredible footage of which can be seen here (albeit with a very strange choice of accompanying music).
What worked well on paper didn’t work well at all under real-world conditions.
Similar comparisons can be made in the world of investing – there have been countless strategies in the annals of investing history that worked well on paper, but which have been thwarted by the constraints of the real world. Of relevance to us is the difference between academic factor investing and the real world applications of those factors – smart beta. Academic factors are long/short, with equally weighted stocks, and with zero trading costs. Smart beta, on the other hand, is long only, market-cap weighted, and incurs real-life trading costs. Investors really shouldn’t be expecting the returns from smart beta to be the same as what’s found in academia.
Beginning with an example, researchers at Factor Research have shown how the value factor’s returns in academia have dwarfed those from value’s smart beta returns in real life:
Source: Factor Research
And the same paper suggests that the difference isn’t just confined to the value factor, but is consistent across most other factors:
Source: Factor Research
Given that there’s quite a lot of research on the topic of academic factors vs real life, I’ve split this post up into several sections. Each section deals with one reason why factors might not provide the same returns in real life that they do in academia:
- Factors are long/short
- Factors work better in small caps
- Trading costs
- The human factors – career risk, incentives, and performance chasing
- The results
So let’s get stuck into part 1.