Tag: trend following

MAD – Moving Average Distance

Sometimes, a research paper comes along that gives academic rigor to an obvious thing that trend-followers were doing for decades and makes you sit up and take notice. Moving Average Distance as a Predictor of Equity Returns, Avramov, Kaplanski and Subrahmanyam (SSRN) does just that.

Turns out, a simple moving average crossover signal proves robust to momentum, 52-week highs, profitability, and other prominent anomalies.

A later paper extends it to international stocks and finds similar results (SSRN).

A quick backtest shows that it works for Indian stocks as well.

It looks like COVID turbo-charged this strategy. The pre-COVID equity curve is saner.

The returns are good but it comes with some nasty drawdowns. Not sure if most investors can stomach a 25% drawdown that lasts over a year. Can it be made better by applying a volatility filter?

By sacrificing 2 points of returns, you can get to a sub 20% drawdown. Also, the filter worked during the most recent 2021-23 drawdown as well.

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

Code and charts on github.

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Trend-following Bonds

Does trend following work on bonds? According to alphaarchitect, it should. However, they use data going back to 1928 and we wanted to look at something more recent. Also, we wanted to check if it worked for Indian bonds?

For Indian bonds, you are better off buying and holding. Once you consider transaction costs and taxes, there is no benefit.

For US, we ran the same SMA scenarios on the TLT (20+), IEF (7-10), SHY (1-3) and AGG etfs. There is some benefit to applying a 100-day SMA filter on the first three. However, the after-cost benefits are questionable.

Code and charts on github.

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Mahalanobis Distance with Trend

Previously, we constructed a portfolio that switches between equities and bonds based on the Mahalanobis distance between them. Here, keeping everything else the same, we add a trend filter to the same set of indices.

The composite regime-switching model ends up with superior Sharpe Ratios. However, if you don’t switch to bonds (and stay in cash, earning zero), then you maybe better off with a simple trend model.

The alpha seems to be in earning the risk-free rate when things are “bad” and getting long equities only when things are “favorable.”

Code and charts are on github.

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Trending Momo Models

Previously, we discussed how applying a trend filter to a midcap momentum index could make sense. Then, we extended that to our homegrown momentum models. In both cases, there are certain situations where trended momentum side-steps deep drawdowns. However, if you are only looking at “raw” returns, you would be better off with monthly rebalanced momentum versions.

In this post, we run a similar test on the Momo versions of our homegrown momentum models.

Momentum (momo)
Velocity (momo)
Acceleration (momo)

The problem with high turnover strategies, beyond transaction costs, is the higher operational risk it entails. You could probably get away with postponing trades by a day or two in the monthly rebalance strategies but with these, you need automated trading systems.

You can track these strategies here: Tactical Momo (Momentum), Tactical Momo (Velocity) and Tactical Momo (Acceleration).

Code and charts: github

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Trending Momentum Models

Momentum is known to trend. Our previous post explored trend overlays on momentum indices. The question now is, does it make sense to do the same to our own homegrown momentum models?

Our Momentum, Velocity and Acceleration models created between 2013 and 2015 have a monthly rebalance schedule. As a risk management measure, trailing stop-losses were introduced to them in 2016 and their momo versions – Momo (Relative) v1.1, Momo (Velocity) v1.0 and Momo (Acceleration) v1.0 – were born.

While these momo strategies do well with sudden market crashes, the problem has always been markets that grind down. Does a trend overlay on the original strategies perform better than momos after transaction costs?

Momentum
Velocity
Acceleration

The trend-overlay strategies seem to avoid drawdowns and perform better than their momo counterparts. The post-2020 Corona Crash market rally was one for the record books. So, a strategy that sidesteps the crash may not necessarily perform better during the rally but the full dataset will show superior performance. What if we took the crash data out of the picture?

Momentum
Velocity
Acceleration

It looks like Momentum and Acceleration strategies saw big pickups in performance. A trend overlay on Velocity resulted in lower drawdowns but that came with a big performance penalty.

You can track these strategies here: Tactical Momentum, Tactical Velocity and Tactical Acceleration.

Code and charts: github

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