Tag: momentum

Rolling MADs

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

Multiple MADs

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.

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.

Daily Momentum

Daily Momentum and New Investors in an Emerging Stock Market (SSRN) describes the trading behavior of Chinese retail investors.

Our study finds that daily returns, instead of monthly returns, display price momentum and attributes it to the trading behaviors of new investors using account-level transaction data.

Apparently, most new entrants to the market in China take a very short-term punt on whatever worked on the day. They go on to study a bunch of DM and EM markets and its worth a read.

The interesting bit is that Indian investors don’t chase daily momentum. In fact, for an equal-weighted “buy the best performing quintile and hold till tomorrow’s close” strategy, after transaction costs and taxes, there’s nothing left, on average.

The median average next-day return of the 5th quintile (highest return) is 0.30%, before slippage.

Also, buying the worst performers did no better either.

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 naïve 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.