When we ran Carver’s Strategy 9 with 15 Instruments, we noticed how most of the returns were driven by crypto. However, that had only the three big coins – BTC, ETF and SOL. Since hand selecting instruments to trend-follow is also a form of overfitting, we expanded the universe to include all x-USDT coins listed in Binance since before the year 2019. There are 21 of those.
Once you expand the universe, the sheen wears off.
While the highest returns came from using a Binary Long-Only Equal-weight strategy, it came with a 60% drawdown, ruling out leverage.
Digging into the coin-level metrics, we see how a fair number of coins have negative contributions.
While the Big 3 coins had favorable trend-following returns, expanding the universe did not yield a better portfolio.
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.
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.
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.
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:
