Tag: momentum

Intraday Momentum, an Update

Back in 2016, we ran a sniff test on Intraday Momentum: The First Half-Hour Return Predicts the Last Half-Hour Return (pdf). We promised an update so here it is (eight years later).

We ran the strategy with both the first 15min and 30min formations with and without considering gaps. It continues to not work with the three indices we used: NIFTY, BANK NIFTY and MID SELECT. Here’s the one for the NIFTY. The rest are on github.

Some strategies may benefit from becoming well known. However, a vast majority of them don’t. This one belongs to the former.

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Prophet for Momentum, Part II

Previously, we discussed using Meta’s Prophet to create a momentum portfolio that was rebalanced monthly. Is there a benefit to rebalancing this at a higher frequency?

Turns out that there isn’t. Even before factoring in costs, Prophet works best with a monthly schedule.

And does significantly worse if bring costs into the equation.

The problem is that Prophet portfolios have little overlap.

Once again, a +1 to KISS.

Code and charts on github.

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Momentum Rebalance Frequency, Part II

Previously, we looked at momentum rebalance frequencies with a monthly increment. However, if you observe the individual returns of momentum stocks (Returns under Momentum), you’ll notice that the returns of momentum stocks tail off after the first two weeks. Does switching to a weekly rebalance frequency make sense?

The biggest problem with a higher frequency of rebalance is the higher transaction cost that comes with it. So, we set the drag to be 0.5% and run 1- through 4-week rebalancing scenarios.

Turns out, there is an advantage to rebalancing a momentum portfolio once in two weeks rather than once a month.

The transaction costs are roughly 5% (annualized) vs. 3% of the monthly rebalanced version.

The main thing to watch out for is the portfolio overlap between rebalancing. The lower the overlap, higher the costs.

Costs are permanent and immediate while returns are hypothetical and distant. Make of this what you will.

Code and charts on github.

Related: Factors

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Linear Model Momentum

More often than not, simple models outperform complicated ones. Inspired by some recent academic research that showed that linear regressions yielded better momentum performance, we did a quick backtest to check if building a linear model through recent 12 and 1/3/6-month performance and creating a portfolio using its next-month predictions made sense.

Counter-intuitively, a naïve momentum strategy outperformed linear models.

This is not our first run-in with linear regressions. Our Dynamic Linear Model strategy simply regresses prices to a 45* line and ranks them based on goodness of fit.

Most of the time, of all the different ways to skin the cat, the simplest is the best one.

Code on github.

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