Nassim Taleb, author of the Inconcerto series and, most famously, The Black Swan, is out with a new paper called Error, Dimensionality, and Predictability.  You can get a copy here.

To quote some of the scary bits ...

From the abstract:

We show how adding random variables from any distribution makes the total error (from initial measurement of probability) diverge; it grows in a convex manner. There is a point in which adding a single variable doubles the total error.

...

Higher dimensional systems – if unconstrained – become eventually totally unpredictable in the presence of the slightest error in measurement regardless of the probability distribution of the individual components.

And from the first page of the paper:

In fact errors are so convex that the contribution of a single additional variable could increase the total error more than the previous one. The nth variable brings more errors than the combined previous n-1 variables!

The point has some importance for “prediction” in complex domains, such as ecology or in any higher dimensional problem (economics). But it also thwarts predictability in domains deemed “classical” and not complex, under enlargement of the space of variables.

This paper especially caught my eye after Ilya Kipnis reached out with the following (elided) tweet(s):

Here we couldn't agree with Ilya more.  Estimation risk is one of the most dangerous latent variables rarely discussed in model research. Everyone discusses assumptions, but nobody likes to admit that the slightly wrong model with the right inputs may be better than the right model with slightly wrong inputs.

The subtext, in our opinion, to Taleb's paper and Ilya's tweets are that increased model complexity can lead to significant errors because of estimation risk – and the impact grows non-linearly with a linear increase of complexity.

We cannot overstate enough our philosophy that a focus on simplicity is key in being robust to complexity – especially when building quantitative models in financial markets.

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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