Author: shyam

Defensive Asset Allocation

Keller, Wouter J. and Keuning, Jan Willem, Breadth Momentum and the Canary Universe: Defensive Asset Allocation (DAA) (July 12, 2018, SSRN) is a follow up to their Vigilant Asset Allocation paper that we discussed previously. A summary of their paper with some drawbacks of their approach can be found here.

The paper used the Emerging Market ETF – EEM – to be one of the canary assets. While EM equities are indeed risky, a risk-off in them doesn’t really mean a risk-off in DM equities. So, we ran a parallel backtest with SPHB (High-Beta ETF) instead of EEM.

As with VAA, the out-of-sample performance of the strategy is abysmal. Using SPHB makes it marginally better but nothing great to write home about.

To reiterate, practically, investors are better off managing drawdowns through asset allocation (hence giving up the upside during booms) or through using simple trend-following to avoid steep drawdowns (hence incurring higher transaction costs due to whipsaws) than trying to construct a Rube Goldberg machine.

Code and charts are on github.

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Vigilant Asset Allocation

Investors hate drawdowns. However, pretty much every attempt at shallower drawdowns comes with costs. Transaction costs and taxes aside, the biggest one is the opportunity cost of missing out on subsequent rallies after having avoided a deep drawdown.

While drawdowns are a very visible risk, the model risk of a strategy that tries to avoid them should not be discounted. The problems with drawdowns is that they are a rare beast compared to the generally upward trending nature of markets. This makes arriving at robust parameters pretty much impossible. Fragile models do not give a lot of confidence when they underperform market beta leading investors to abandon them just before a drawdown that they are designed to avoid comes calling.

However, these problems do not prevent academics from soldering on. Keller, Wouter J. and Keuning, Jan Willem, Breadth Momentum and Vigilant Asset Allocation (VAA): Winning More by Losing Less (July 14,
2017, SSRN) is one such attempt. A summary of the paper can be found here.

We reproduced it with listed ETFs and the results are sobering.

An in-sample hero but an out-of-sample dud.

No amount of behavioral reinforcements would have been enough to stick with this model when equity markets rallied after 2022.

Practically, investors are better off managing drawdowns through asset allocation (hence giving up the upside during booms) or through using simple trend-following to avoid steep drawdowns (hence incurring higher transaction costs due to whipsaws) than trying to construct a Rube Goldberg machine.

Code and charts on github.

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Accelerate or Die

The only reason to invest in Emerging Markets is for growth. However, India has its fair share of zombies that don’t grow their top line and operate in mature industries with mature economics. For example, over the last decade, Indian Fast-Moving Consumer Goods (FMCG) revenue growth has consistently trailed broader nominal GDP growth.

If new investment dollars chase growth in EMs, why bother with the cruft? Why not only invest in companies that have accelerating top lines?

This is the thought process behind Accelerating Toplines (Annual) and Accelerating Toplines (Quarterly) Themes. As their names suggest, the former is based on annual financials whereas the latter is based on quarterly reports.

Growth stocks tend to be volatile – these strategies should appeal to the more risk-seeking investor class.

Our pricing can be found here.

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Industry Momentum

Do industries (stocks collectively grouped by the industries they belong to) exhibit momentum? Does going long the best few industries lead to out-performance?

Yes, in theory.

If you go long the 5 best industries by relative strength, you do end up outperforming the market.

Rebalance cap-weighted industry indices once in 4 weeks and stagger it to avoid rebalance timing luck and you have the basics of the strategy.

The implementation is the thorniest part. Unlike basic equity momentum where you can equal weight your positions and call it a day, running an equal-weighted strategy on cap-weighted industry indices is going to take some work. We took a crack at a basic version of it here: Industry Momentum. However, the backtest indicates that the trend component is stronger than the momentum component (probably why cap-weighting outperforms equal-weighting) and staggering rebalances matters. This makes the execution of this strategy a bit challenging.

Code and charts on github.

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MSCI Country Index Momentum

There are currently around 40 to 45 single-country ETFs actively trading on US exchanges. Is it possible to construct a momentum portfolio that beats a generic all-world momentum offering using them?

We ran a few scenarios. First, we looked at 50-, 100- and 200-day momentum and then we overlaid the same length of trend over them. Compared to both the market-cap and momentum all-world indices, 200-day Momentum + Trend out-performed.

You could also average out the look-backs to get a parameter-free portfolio without regrets.

With the portfolio being equal weighted, it avoids the geographic and industry concentration problem that plagues most momentum ETFs. Besides, there are no ETFs that track the MSCI ACWI Momentum Index right now. Until such a time, DIY!

Code and charts on github.

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