Category: Investing Insight

Investing insight to make you a better investor.

Strategy Capacity

A recent Verdad Capital newsletter, Dan Rasmussen points out that it is impossible to scale value investing as defined in academia. Almost all of “value” ETFs and actively managed funds are completely avoiding the cheapest stocks (high book-to-market) while instead owning primarily stocks that are more expensive (low book-to-market).

And what explains this puzzle? Strategy capacity.

The cheapest stocks are disproportionately small in terms of size and volume. This means that an active manager looking to choose, say, the best 40 of these stocks would be unable to manage more than $200M or so. The average small value fund tracked by Morningstar has $1.3B of assets under management. It is close to impossible to deploy that amount of capital exclusively in the cheapest two deciles of the stock market.

This is true for Indian mutual funds as well. Most managers claim to be “value” investors while actually hugging the index with a GARP/momentum tilt. Given the size of most of these funds, there is no way they can invest in value.

Strategy capacity should be one of the questions investors should ask of their fund managers/advisers. Especially advisers of direct equity portfolios who do not know the aggregate exposure that their subscribers have across portfolios.

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Is the Low Volatility Regime Breaking?

NIFTY 50 volatility the last couple of years have been extremely low by historical standards. If you look at the rolling median of weekly returns over 50 weeks (about a year), you can see how the range has narrowed:
median weekly returns
The standard deviation, a popular measure of volatility, has come down as well:
standard deviation of weekly returns

As the charts illustrate, the markets have been moving in tight ranges. And narratives have been built around it:

  1. Global central banks (US, Europe, Japan) have been flooding the markets with liquidity, essentially writing a put on the market.
  2. Markets have become less riskier thanks to increased regulations after the 2008 global financial crisis.
  3. Investors have a new-found enthusiasm for “SIP it and forget it.” This, plus the NPS bid, has cushioned the NIFTY 50.
  4. Increased liquidity in the derivatives market has allowed investors to buy volatility, thereby reducing the need to decrease risk by offloading equities in the cash market.
  5. The majority government at the center has provided policy certainty and political scams have not paralyzed decision making.

Narratives can change overnight. And if the last few months have taught us anything, the market drives the narrative. Also, new investors have only seen a low volatility environment and think it to be “normal.” So any reversion to the old volatility regime would be a rude awakening. Are we really in a new world or is volatility about to revert to its longer-term mean?

Code and additional charts on github.
Also read our Volatility Collection.

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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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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

NIFTY%2050

Cross-over with slope check

NIFTY%2050

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.

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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

NIFTY 50

NIFTY%2050

NIFTY MIDCAP 100

NIFTY%20MIDCAP%20100

NIFTY SMLCAP 100

NIFTY%20SMLCAP%20100

Take-away

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.

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