Author: shyam

Hedging Momos

Previously, we discussed how momentum itself trends and how that can be used to manage risk. Using simple moving averages showed promise when it came to some versions of our slow momentum models (see Trending Momentum Models). However, given the faster turnover of our Momos, it wasn’t a suitable approach (see Trending Momo Models).

What if we shorted the NIFTY to hedge against market-risk instead?

The na├»ve approach, tagged “HEDGE_FULL” above, shorts the NIFTY in proportion to the rolling beta of the strategy. Turns out, this is a very sub-optimal way to go about it. Hence “HEDGE_SMART”, that tries to minimize the basis risk inherent in this approach, adds about 3-4% to the strategy’s returns (likely eaten away by transaction costs & taxes) and reduces the max-drawdown by a significant amount.

The question is whether the benefit of lower drawdowns is worth the added cost and complexity? In the case of Velocity, it could be.


Momentum Portfolio Size

Previously, we looked at skip-months, rebalance frequencies and formation periods for momentum portfolios. A 1-month skip & monthly rebalance turned out to be ideal. However, the most popular 12-month formation period is “magic” – not a terrible choice but not super scientific either. The only thing left to toggle is the portfolio size.

A 20-stock momentum portfolio seems to be the ideal config.

This is pretty much the standard direct-equity momentum portfolio: 12-month formation, 1-month skip, 20-stocks with a monthly rebalance.

Code and chart on github.

Momentum Formation Period

Previously, we looked at skip-months and rebalance frequencies for momentum portfolios. A 1-month skip & monthly rebalance turned out to be ideal. However, we did these analyses keeping the formation period the same at 12-months. What if we changed that as well?

Turns out, there is no single “ideal” formation period where all stats converge. However, if set the rebalance frequency to 1-month, the average of the formation periods of the top performing portfolios works out to 12-months.

If the momentum fund is large enough, then it could probably be sliced into multiple sub-portfolios, each with different configs to avoid this magic 12-month formation.

Also, since the underlying process creating these portfolios is the same, the equity curves come out all bunched together. There maybe differences in month-over-month performances but they are all highly correlated.

Code, charts and statistics on github.

Momentum Rebalance Frequency

Previously, we found that the traditional 12_1 momentum configuration, where you look at the previous 12-month performance while skipping the most recent month and rebalancing every month, was indeed an ideal config (MOM_1_1). However, there are momentum index funds that rebalance once in 6-months (MOM_[0,1]_6). Is there any performance give-up if you rebalance infrequently?

Turns out that the traditional config is quantifiably better than others. However, there’s isn’t much of a performance give-up even if you rebalance once in 6-months (MOM_0_6).

Besides, the analysis here doesn’t factor in transaction costs which would be a bigger drag on the monthly rebalance config. When you add the tax-advantage and low-cost of index funds into the mix, the current crop of momentum index funds don’t look all that shabby.

Code and charts on github.

Momentum Skip Month

The original Jegadeesh and Titman momentum paper (pdf) used a “skip month” to manage the reversal effect (quant.stackexchange). However, why is it one month and not two, or three or four?

Here’s what the equity curves of different skip month configs look like.

The “skip one month” is indeed a magical config. Also, if you are optimizing for Sharpe, skip two.

Code and charts on github.