Category: Investing Insight

Investing insight to make you a better investor.

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.


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

SMA Strategies, Part III

In Part II of SMA Strategies, we saw how we could reduce drawdowns by making sure that we go long only when the slope of the SMA is positive. i.e., when the SMA is trending higher. Here, we will look at cross-overs.

While previous strategies compared the current value of the index vs. its SMA, a cross-over strategy uses a smaller look-back SMA instead of the index. Essentially, go long if SMA(N/4) > SMA(N).

NIFTY 50 Cumulative Returns

Cross-over only


Cross-over with slope check


The stand-alone slope check from Part II has lower peak drawdowns than the cross-over versions. The additional averaging of recent prices leads to a lagged response. Given the proclivity of our markets to cliff dive, a lagged response will result in higher drawdowns. It could, however, lead to lower transaction costs by papering over short-term mean-reverting moves.

Code and additional charts are on github.

Book Review: Scarcity

In the book Scarcity: Why having too little means so much (Amazon,) authors Sendhil Mullainathan and Eldar Shafir show how scarcity impairs judgment, and decision making.

The book lays out how scarcity – of time, money and attention – ends up creating tunnel vision that trades short-term hacks to longer-term solutions. However, the book is short on solutions and long on anecdotes. It is almost reads like a teaser to Nudge. The book is also a strong endorsement for having a bit of slack in our lives.

Toward the end of the book, I couldn’t help but conclude that the only way not to fall into the scarcity trap is to automate an increasing amount decisions that we make. Everything from buying toothpaste to scheduling appointments can be handled by bots so that we can take longer naps.

Recommendation: worth a read.

SMA Strategies, Part II

In Part I we saw how a simple tactical strategy that can be implemented by ETfs out-performs an actively managed mutual fund even after transaction costs. However, there are more than a million ways to implement an SMA strategy. Everything from picking the lookback period, cross-overs and enveloping are all open questions. There is no single “best” way to do it. Here, we add a simple check that makes sure that the SMA is trending higher before going long.

Quite simply, for an N-day SMA, we compare Nth-day to N/2th-day. If it is higher, then we go long.

Cumulative returns








The gross returns are lower than the “raw” strategy that we saw in Part I. However, the drawdowns for the 10-day SMA are a lot shallower. Shallower drawdowns allow a bit of leverage to be employed. This could be a good starting point for a NIFTY futures trading strategy.

In Part III, we look at how cross-over strategies perform.

Code and charts are on github.

SMA Strategies using ETFs

A simple moving average of an index is nothing but the average of closing prices of that index over a specified period of time. We did a quick backtest to see how an SMA based toggle between going long an index vs. cash performed.

Cumulative returns








The backtest, unsurprisingly, shows that shorter the SMA look-back period, better the performance. However, the boost in performance comes at the expense of higher number of trades. Lower look-backs are only viable now thanks to brokerages where you would pay zero for these trades (however, you still pay the securities transaction tax.) To see how this would shake out in the real world, have a look at how our Tactical Midcap 100 Theme has performed in the last ~2 years:

The Theme used the M100 ETF (Motilal Oswal Midcap 100 ETF) with a 10-day SMA toggle to switch between the ETF and LIQUIDBEES. The blue line represents zero brokerage and 0.1% STT and the green line represents a brokerage of 5p and 0.1% STT. The chart shows it beating an actively managed midcap fund across all transaction fee scenarios.

The snag is that this strategy is tough to scale. The M100 ETF just doesn’t trade enough for this strategy to absorb more than Rs. 10 lakhs. And there is no small cap ETF on the horizon to implement the strategy in that space.

The second problem is that M100 trades to a wide premium/discount to NAV (see: ETF Premium/Discount to NAV.) This is another layer of risk that an investor could do without.

However, things seem to be moving in the right direction. Reliance Capital launched a new ETF recently that tracks the NIFTY MIDCAP 150 index. Their ETFs usually trade better – tighter spreads, narrower tracking errors, better liquidity. Hopefully, it will emerge as a stronger alternative to M100 and allow these strategies to scale. We setup the Tactical Midcap 150 Theme that uses the RETFMID150 ETF instead of the M100 ETF for those who are interested.

In Part II, we will see how adding a simple check on the SMA can reduce drawdowns.

Code and charts are on github.