Tag: momentum

Dual Momentum: NIFTY vs MIDCAP

In Global Equities Momentum, we looked at how toggling between US Equity Momentum and World ex-US Equity Momentum ETFs gave superior returns to buy-and-hold. Can the same framework be applied to toggle between NIFTY 50, MIDCAP 100 and bonds?

Relative Performance

For dual momentum to work, you need the excess returns of the two equity assets to be un-correlated (or very loosely correlated.) Here is the plot of rolling 200-day cumulative returns of the equity indices minus that of the 0-5 year bond total return index:

excess returns of NIFTY and MIDCAP
The line marked RELATIVE is the difference between MIDCAP 100 returns and NIFTY 50 returns.

What we see here is that there is a high degree of correlation between the two when it comes to excess returns over bonds. At the same time, however, the relative performance between the two equity indices tends to be sticky. So, a dual momentum model tuned to sniff out the “regime” should be able to give returns better than buy-and-hold.

For reference, here is how buy-and-hold performed:
buy-and-hold NIFTY/MIDCAP/bonds

Backtest

Over different look-back periods, here is how the dual-momentum strategy worked:
NIFTY/MIDCAP dual momentum over different look-back periods

Over 3- and 4- month look-backs, the model does seem to show higher returns and lower drawdowns than buy-and-hold. But this is probably going to over-fit past data. But what happens if we specify the model to use “any” of the lookbacks? i.e., stay in equities if any of the look-backs signals the NIFTY 50 has out-performed bonds over the same period?

NIFTY 50/MIDCAP 100 dual momentum over any lookback

Here are its worst drawdowns:
drawdowns of NIFTY 50/MIDCAP 100 dual momentum over any lookback

A model setup this way has lower drawdowns and returns that better than NIFTY 50 but lower than that of MIDCAP 100 buy-and-hold. It really just boils down to how much pain you can bear – for those with a lot of testicular fortitude, buy-and-hold MIDCAPs are the best. But for the rest of us mere mortals, this strategy makes sense. And unlike an SMA model that checks for potential trades every day, this one checks only once a month. This keeps transaction costs low for long-term investors.

Code and more charts are on github.

Global Equities Momentum Trades and Portfolio

We described a simple dual momentum strategy last month that toggles between equity and bond ETFs. In order to make it easier for you to follow the strategy, we have setup a Slack channel that posts trades triggered by this strategy. You can also follow the performance of this strategy through two virtual portfolios: Global Equities Momentum I and Global Equities Momentum II.

The GEM strategy employs a monthly rebalance schedule. So, at the maximum, expect one SELL and one BUY trade a month.

To request an invite to our Slack channel, click here. WhatsApp us if you have any questions!

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

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

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.