Tag: momentum

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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Global Equities Momentum (Update)

We had first discussed Gary Antonacci’s Global Equities Momentum in 2019. We had forecast that using basic indices to drive momentum longs would yield better returns than using market-cap ETFs.

Since then, GEM’s momentum flavor under-performed its market-cap version. Also, buying & holding the S&P 500 index out-performed GEM. You would’ve done even better by buying & holding MTUM (the US momentum ETF).

You do get lower drawdowns in GEM. However, during this period, the lag between when the market recovered and GEM caught up overshadowed the benefit of lower drawdowns.

Charts and code on github.

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Factor MAX

The paper Factor MAX and Predictable Factor Returns from Liyao Wang and Ming Zeng presents a twist on momentum investing that goes long the factor that had the largest single-day return in the previous month. It is distinct from factor momentum goes long the factor that had the largest return over a specific formation period.

We have been running factor and model momentum for a while now with mixed results so we decided to have a look at this new strategy in the Indian long-only context.

tl;dr: not so hot!

We selected the NIFTY500 factor indices: LOW VOLATILITY 50 TR, MOMENTUM 50 TR, QUALITY 50 TR and VALUE 50 TR to compare Factor MAX vs. Factor Momentum. Factor Momentum out-performed Factor MAX.

The problem with using a single day’s performance to select a factor is that more volatile factors get picked more often. Here’s a plot of the monthly active factor between the two strategies.

Quality and Low-volatility factors do not jump around every day. Hence, their low representation in Factor MAX. You could use volatility adjusted returns to paper over this. However, we felt that went against the main thrust of the paper that investors systematically under-react to factor-level news embedded in these extreme returns, creating exploitable return predictability.

We ran the same backtest over a subset of our momentum and value models. Factor Momentum bested Factor MAX here as well.

If you want to DIY Factor Momentum based on this backtest, you can do so with cheap index funds:

  • Nippon India Nifty 500 Quality 50
  • Nippon India Nifty 500 Low Volatility 50
  • Nippon India Nifty 500 Momentum 50
  • Axis Nifty500 Value 50

Code and charts on github.

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Sector Momentum

Previously, we had looked at using the momentum of S&P 500 Sector SPDRs for potential rotation strategies. How would the Indian story unfold?

We take 16 sector indices, use a 6-month look-back window and go long the sector with the highest returns, holding it for a month.

You end up with higher returns but lower Sharpe – makes sense given the super-concentrated nature of the portfolio.

The 4 points of out-performance (after costs, pre-tax) over the NIFTY 100 index is not much to write home about. Besides, this strategy trailed the benchmark pre-2020. If this were pitched back then, nobody would’ve deployed it and nobody would’ve been around for the post-2020 out-performance. On a positive note, the availability of index funds and ETFs should make this strategy fairly easy to implement.

The main caveat is that the index construction rules themselves are subject to change. Mid last year, SEBI capped the maximum concentration of a single stock for a sector index at 35% and required them to have at least 10 stocks.

Code and charts are on github.

Here are some other things we tried, so that you don’t have to:

Equal-weight all Sector Indices

Inverse-volatility weight all Sector Indices

Equal-weight Sectors in an Up Trend

Inverse-volatility weight Sectors in an Up Trend

The excess returns of these alternatives do not justify the costs.

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