Tag: momentum

Momentum Rebalance Frequency, Part II

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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.

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

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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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Rolling MADs

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Our previous posts introduced portfolios based on Moving Average Distance (Part 1, Part 2). To answer questions regarding the stability of the moving average lookbacks, we ran a rolling window, picked the “best” MA lookbacks and walked the portfolio forward by a month. We expanded the window through 12 to 60 months in 12 month increments.

Turns out, most of them fall within the 20/200 region.

The data-mined parameters create portfolios that perform on par with the 21/200 used in the paper. While we are always skeptical about magical parameters that make the research work, at least in this case, the magic is not too far fetched.

You can follow along the live version of the original strategy here: MAD 21/200

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Multiple MADs

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Our previous post introduced a paper that used a moving average crossover to create a portfolio of stocks. While the backtest using the parameters in the paper looks good, the presence of these “magic” lookback parameters gives us pause. Did the authors just try a bunch of different parameters and published what worked? What if we do an exhaustive search through all possible combinations?

Here are the annualized returns and Sharpe ratios pre-COVID:

The magic 21/200 lookbacks look legit. However, the post-COVID picture looks different:

The magic parameters don’t quite figure in the top 5. However, even if you used the data-mined set, you would be ok?

Also, the paper used a “sigma” parameter as a threshold to activate the crossover. Getting rid of it seemed to have lopped 10% off the post-COVID returns.

You can follow along the live version of the original strategy here: MAD 21/200

Code and charts on github.

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