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Momentum Score Matrices

June 26, 2014

In the previous post we introduced the momentum score as a measure of the potential for momentum profits for a given investment universe. Before proceeding to part 2 of the series, I thought it would be interesting for readers to see a pairwise matrix of momentum scores to get a better feel for how they work in practice. Note that higher scores indicate higher potential for momentum profits. Below are the pairwise momentum score matrices for both sectors and asset classes:

momentum score matrix sectors

Notice that sectors with similar macro-factor exposure have lower scores: for example materials and energy which tend to thrive in cyclical upturns in the economy (XLE/XLB), or health care and consumer staples (XLP/XLV) which thrive in recessions or cyclical downturns. The highest scores accrue to sectors that are likely to do well at different times in the economic cycle such as energy and utilities (XLE/XLU). This makes logical sense– momentum strategies require the ability to rotate to assets that are doing well at different times.

momentum score matrix asset classes

Notice that pairings of equity, real estate or commodity assets classes (e.g. SPY,EEM, IEV, DBC, RWX) with TLT and GLD tend to have the highest momentum scores. The combination of near substitute assets such as intermediate bonds (IEF) and long-term bonds (TLT), or say S&P500 (SPY) and European stocks (IEV) tend to have very low scores by comparison. In general most pairwise scores for asset classes are substantially higher than those contained within the sector momentum score matrix.

5 Comments leave one →
  1. Neil permalink
    September 21, 2014 10:39 am

    Hi David,

    I have been reading your articles with great interest. Thank you for sharing your thoughts and research on momentum and adaptive asset allocation – my particular area of interest. I am a novice quant, so I sometimes need to read these a few times before they sink in. Your work and Butler, Philbrick & Gordillo are helping me to confirm a strategy that I have been using, albeit less sophisticated (ie raw momentum in a multi asset class environment). In my own testing and research, I sometimes found the addition of another asset not to affect the risk return profile of the portfolio. The contribution to overall return or risk was insignificant and it didn’t seem to add any value – so I omitted it. I wonder if that would be the same as excluding those assets or sectors with overall low momentum scores – like in your matrices. Do you agree that in this case less is more? That is, a smaller universe with higher momentum scores is preferable to a larger universe with a mix of high, medium and low momentum scores?

  2. October 20, 2014 2:16 pm

    Wouldn’t be these results similar to pairs of stock with low correlation?

    • david varadi permalink*
      October 21, 2014 9:55 pm

      yes and no Jil, having high variance is also important. an asset with a low variance and low correlation can’t contribute as much to return as an asset with moderate correlation and very high variance. of course there is also the autocorrelation issue as well- ie the lead lag effect.


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