Momentum vs Moving Averages


  • Trend-following is one of the oldest investment methods
  • Labeled as technical analysis, trend-following went largely un-researched by academics
  • Research of cross-sectional momentum exploded after Narasimhan Jegadeesh and Sheridan Titman published their seminal 1992 study, but time-series momentum remained largely ignored until after 2008
  • Price-based trend-following techniques, like moving average systems, remained separate from return-based time-series momentum techniques.
  • New research shows that moving average systems and time-series momentum­ are mathematically-linked techniques

But Let's Assume We're Wrong – Weekly Commentary

This weekly commentary is available as a PDF here.

Commentary Summary

  • Risk management is a core focus at Newfound
  • “Risk management” most often translates to a conversation about capital preservation – but risk manifests in many ways
  • Manager risk is the risk driven by decisions in active management
  • We believe that keeping strategies simple helps reduce the potential negative impacts of manager risk

New Paper from Markowitz: Introducing the Gerber Statistic

Harry Markowitz, father of modern portfolio theory, has a new paper out with Sander Gerber and Punit Pujara titled Enhancing multi-asset portfolio construction under Modern Portfolio Theory with a robust co-movement measure.  You can download it here.

The big take away is the introduction of a new co-movement measure called the Gerber Statistic, which is designed to be more robust to outlier data in measuring co-movement among asset classes.


Weekly Commentary – Lessons from a Crystal Ball

A PDF of this commentary can be downloaded here.


  • Dalbar studies tell us that investors often sell after losses and wait for markets to reclaim high water marks before re-entering – behavior that is guaranteed to lead to underperformance
  • Other, more dynamic, approaches may help investors achieve their dual goals of participating with market growth while managing capital loss
  • While nobody has a crystal ball, pretending to have had a crystal ball over the last decade can tell us something about how we should behave
    • Losses happen; we should remain de-sensitized to losses in bull markets in effort to participate with growth
    • Whipsaw can be worse than market losses; being overly sensitive to losses can compound drawdowns
    • Each bear market is unique: being highly sensitive to losses may be the optimal strategy in one bear market and the worst strategy in another
3648x2736 uncropped TheAppBuilder-icon-morro-strand-sunset-21nov2008-49.RAW20 Aug 2013, the original 1024 x 1024 icon version has been replaced with a larger 3648x2736 original here due to a Getty request to represent the image.

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New Nassim Taleb: Error, Dimensionality, and Predictability

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.