Strategy 9 with 15 Instruments

Our previous post on Rob Carver’s Strategy 9 experimented with four major Indian indices. We saw that only two of them contributed to out-performance while the others dragged.

Can we just run those that worked and throw away the rest?

The whole point of using multiple moving averages is to avoid overfitting. Hand selecting instruments to trend-follow is also a form of overfitting. Carver repeatedly says that his approach works best on a large set of instruments (start with 100 and whittle down.) However, as an Indian retail trader, we do not have many options. Realistically, we can lay our hands on at most 15 different instruments.

With these 15, we played around with: scaled vs. binary x long-only vs. long-short x equal-weighted vs. inverse volatility weighted.

The results are sobering.

Long-only Equal-weight

Long-short Equal-weight

Long-only Inverse-volatility-weighting

Long-short Inverse-volatility-weighting

Of these, only the scaled long-only equal-weight setup looks promising. However, if you look at how individual instruments performed, it is hard to remain unbiased.

The largest contributor is crypto.

Charts and code are up on github (equal-weight, inverse-volatility-weight)

Categories , Tags , , ,

Strategy 9

In Rob Carver’s Advanced Futures Trading Strategies (Amazon,) there’s a chapter, “Strategy Nine: Multiple Trend Following Rules,” that uses composite trend-following rules to drive a long-short strategy. We explore the strategy through an Indian market participant’s lens.

There are a number of decision points to navigate. Primarily, long-only vs. long-short, binary vs. scaled and equal-weight vs. cost-screen. Some of these are not practical for retail futures traders. For example, you could use the different SMA rules to scale in and out of positions instead of taking a binary approach. However, that implies that each position will need at least a dozen contracts – a capital requirement that is out of reach of a typical retail trader. Another problem is that the Indian market is notoriously expensive to trade. The cost-screen used by Carver throws out a lot of short-term SMAs, making the strategy unresponsive to short-term market moves.

Summarizing NIFTY 50, NIFTY BANK, NIFTY MIDCAP and SMALLCAP indices through Strategy 9:

NIFTY 50 and NIFTY BANK are poor candidates for this system – consistently under-performing buy & hold. The SMALLCAP index doesn’t have futures listed on it – forcing a realistic implementation to be long-only. The MIDCAP index does have futures on it, making long-short possible. The cost-screen version can be safely ignored.

If we decide to use futures for MIDCAP, we need to make sure we don’t blow up because of leverage. The problem with the long-short strategy is the periodic 20% drawdowns. Even at 2x leverage, that’s a capital impairment of over 40%.

However, looking at the equity curve, it may be worth the extra antacid budget?

The next question is, scaled vs. binary?

The scaled long-short version (red line in the chart above) is objectively worse than binary long-short (green line).

So, the version that worked for MIDCAPs is a binary long-short without cost-screen.

For SMALLCAP, given that it doesn’t have listed futures, we’ll have to settle for the binary long-only version without cost-screen.

We interrogated Claude as to the robustness of Carver’s approach. You can read the back-and-forth here.

Code and charts on github.

Categories Tags , ,

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.

Categories Tags ,

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.

Categories Tags , ,

Winning with Market-cap ETFs

The Parag Parikh stable of funds attract a lot of attention because they are good story-tellers. In their Flexi-cap Fund, they are simultaneously placing concentrated bets on US and Indian equities, hedging, arbitraging, selling cover-calls, making cash allocation calls, and so on. And they talk about it a lot.

All this activity should surely result in superior performance?

We had written a couple of notes around this back in 2015 and 2019. Our concern revolved around return-attribution. When you are doing so many things, how do we know if you are actually good at any one of them?

If you look at returns since the first note came out, they under-perform the MIDCAP 150 index.

Not that there weren’t years where they out-performed. However, given all that activity, is this all they could do?

In our second note, we had mentioned that you could, technically, replace the fund with a midcap and S&P 500 index ETF in a 65-35 ratio. So, from that point on, if you were to construct such a portfolio, it would beat the fund as well.

It is not that their stock picks are bad. If you analyze the Indian equity portion of their portfolio over time, their stock picks, on average, has delivered 2% over the midcap index during the holding period.

And while digging through this, we noticed that there is alpha in keeping track of stocks that they have exited.

There is a decent skew in favor of entries but not as much as exits.

It is often said that exiting a position is tougher than entering it. In that sense, the fund managers have displayed good skill.

Tracking the current portfolio may not yield much. For example, if you look at positions held for more than 12 months, excess returns are distributed across the spectrum.

Our suggestion is that you can treat the fund as a research project for your own edification, but when it comes to deploying your own capital, you can stick with market-cap ETFs and index funds.

Code and charts on github.

Categories Tags