Tag: momentum

US Relative Momentum – Year One

From an Indian investor’s point of view, the attractiveness of US stocks are many:

  1. There is no STT. So trading costs are orders of magnitude lower.
  2. Lower volatility overall.
  3. Rupee usually depreciates ~2.5% annually.
  4. Non-overlapping political cycles.
  5. Diversification. US is 40% of overall world market cap while India is less than 3%.

Most investors looking to invest in the US would be better off buying and holding the S&P 500 ETF (SPY) or its momentum equivalent (MTUM.) However, for those who are looking for a more active portfolio, we ported our relative momentum strategy to the US markets last year.

Dynamic Momentum

Our dynamic relative momentum strategy applies a trailing stop-loss of 5% on each constituent, everyday, and kicks out stocks that have sunk below it. Replacements are chosen based on their relative momentum scores.

While the strategy is still recovering from the 18% drawdown that occurred in September 2018, it has beaten MTUM and SPY handily over the entire period. The stats are updated every day here. Please bookmark!

Static Momentum

Our static relative momentum strategy follows a once-a-month rebalance schedule. Unless any of the constituent stocks undergoes a delisting/merger etc., the algorithm is run at the beginning of the month and the portfolio is held for the entire month.

The strategy is still recovering from the 30% drawdown that occurred in June 2018 but it has out-performed both MTUM and SPY handily in the last 20- and 50-days. The stats are updated every day here. Please bookmark!

Static vs. Dynamic

The dynamic strategy suffers from higher transaction costs but is likely to experience shallower drawdowns and be more responsive to market trends. Unlike in India, the US doesn’t have STT. So if you pay a flat brokerage (rates from Interactive Brokers can be as low as $1 per trade) then dynamic is the way to go. Here is the static vs. dynamic experience in Indian markets:

There is a ~15% return differential, July’16 through Feb’19, between the two versions.

Suitability

Here is how investors should weigh the alternatives, in order from simple to complex, cheap to expensive:

  1. Buy and hold SPY.
  2. Buy and hold MTUM. Given the high degree of correlation between SPY and MTUM, this makes sense if investors can manage the additional volatility through allocation.
  3. GEM – Global Equities Momentum outlined here.
  4. Static Relative Momentum. Over a longer time-frame, it should perform in-line with its dynamic counterpart after transaction costs.
  5. Dynamic Relative Momentum. For investors who enjoy a low cost of trading.

We are happy to further explore these options with you. Drop us a note!

Related: US vs. Indian Midcaps

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Global Equities Momentum

A brief introduction to Global Equities Momentum and a look at various alternative scenarios.
Read: Part I

Could it work on value indices?
Read: Part II

Swapping momentum at the final step boosts returns significantly.
Read: Part III

Averaging out returns over different formation periods boosts returns and reduces drawdowns.
Read: Part IV

Track the virtual portfolios we setup using these strategies and follow the trades on our Slack channel.
Details: Trades and Portfolio

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Global Equities Momentum, Part IV

Our GEM backtest in Part III used a 12-month formation period to measure momentum. Here, we look at alternative formation periods with an eye on drawdowns.

6- through 12-month formation periods

GEM.6-12mo.cumulative

Even though the 10-month version has higher returns, the 6-month one has lower peak drawdowns.

The average of all

The problem with picking one formation period out of 6 is that it smells of data-mining. What happens if you average them all out?

GEM.avg.cumulative

The average works in reducing drawdowns compared to the traditional 12-month version.

GEM.avg.dd

GEM.m12.dd

We will setup a virtual portfolio for this “averaging” strategy and post the link here when it is up and running.

Code and more charts on github.

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Global Equities Momentum, Part III

We saw in our earlier posts on Global Equities Momentum (Part I, Part II) that by swapping the momentum equivalent of the equity indices in the GEM decision tree, one could significantly boost returns. Also, momentum trumped value.

Correlation between momentum and base indices

In the original GEM dual momentum model, the S&P 500 index was used to decide and to trade. What we claim here is that we can continue to use the S&P 500 index to decide, but we will use the momentum equivalents to trade. To back our claim, we present the correlation in the monthly returns of the base/momentum index pairs:
SP500.USA-MOMENTUM.correlation
WORLD%20ex%20USA.WORLD%20ex%20USA%20MOMENTUM.correlation

The indices move pretty much in tandem.

Robustness

If dual momentum is robust, then our strategy piggybacks on its robustness through the decision tree. Where we differ is in the way we express the trade. And our backtest shows that GEM is superior to buying and holding the underlying indices themselves both in terms of returns and drawdowns:

USA%20MOMENTUM.WORLD%20ex%20USA%20MOMENTUM.GEM.cumulative

Annual returns:
USA%20MOMENTUM.WORLD%20ex%20USA%20MOMENTUM.GEM.annual

Instruments

Implementing this strategy is fairly straightforward. You need to track the following ETFs:

  • SPY: for S&P 500
  • BIL: for US T-bills
  • IDEV: World ex-US
  • MTUM: US Momentum
  • IMTM: World ex-US Momentum
  • AGG: Aggregated bond

You will be long one of the last three ETFs above at any given point in time:
USA%20MOMENTUM.WORLD%20ex%20USA%20MOMENTUM.GEM.instruments

We will setup a virtual portfolio for this strategy and post the link here when it is up and running.

Code and more charts on github.

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Global Equities Momentum, Part II

In our previous post on Global Equities Momentum, we explored how we could potentially replace the indices used in the GEM decision tree with their momentum counterparts to boost returns. Corey Hoffstein (@choffstein) pointed out that given the excess turnover of momentum strategies, measuring their trend maybe adding too much noise. Also, could using value indices, given their lower turnover, make more sense?

We setup the following backtest to fix the first problem and explore the second.

  1. We will use the S&P 500 index to make the first decision of the GEM model: Should we invest in equities or bonds?
  2. Once we get past #1, we will use different sets of indices to make the next one: USA or ex-USA? And trade the same.
  3. We will use the MSCI USA PRIME Value index to represent US Value and MSCI ACWI ex USA PRIME Value index and MSCI WORLD ex USA PRIME Value index, in turn, to represent international value.

USA/All World ex-USA Value GEM
USA/All World ex-US Value GEM

USA/Developed World ex-USA Value GEM
USA/Developed World ex-US Value GEM

  1. The GEM models both show vastly better returns and shallower drawdowns compared to buying and holding the underlying indices alone.
  2. There is not a lot of difference between the two GEM models.
  3. However, there are no equivalent ETFs for investors interested in implementing either of these GEM models.

In contrast, USA Momentum/MSCI World ex-USA Momentum:
USA Momentum/MSCI World ex-USA Momentum

Not only does the momentum GEM vastly outperform the value GEMs, it can be easily implemented with the MTUM and IMTM etfs.

Before jumping into any of these strategies, it is worth asking: Is this just data mining? How can we be sure that these backtests are statistically valid? By the same token, how can we be sure that even the dual-momentum model is robust? Gary Antonacci’s GEM backtest goes as far back as 1971 but we only have MSCI index data starting from 1995 or later. Besides, we are not sure if it is even possible to construct a reasonable momentum index going that far back. So, caveat emptor!

Code and more charts on github.

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