Tag: momentum

Typically, momentum strategies create portfolios of 20-50 stocks. What if only a single stock was used?

Ammann, Manuel and Moellenbeck, Marcel and Schmid, Markus, Feasible Momentum Strategies in the US Stock Market (November 17, 2010, SSRN) explores precisely this idea (summary).

We ran a bunch of backtest scenarios adapted for the Indian market.

Methodology

• Each month, stocks are ranked by past return over a formation period J (3, 6, or 12 months).
• The best-performing stock(s) are “winners” (bought).
• Short the NIFTY.
• Positions are held for a holding period K (3, 6, or 12 months), using the standard overlapping-portfolio construction (K staggered investment “strands” so only 1/K of the book turns over each month).
• A one-month lag is inserted between the formation period and the holding period to avoid short-term reversal effects.
• Portfolios of 1, 3, 5, or 10 stocks N per side are tested (buying more than just the single best).

Paper Best

Using the “best” config in the paper, we realized that considering just returns without volatility leads to failure.

Beta hedging + Omega

Momentum stocks tend to be high beta, so instead of shorting the same notional, we shorted the beta. Besides, Sharpe penalizes volatility symmetrically. Omega with NIFTY as MAR penalizes only underperformance relative to the index.

Also, the training set pointed us toward a longer holding period K (6 vs. the paper’s 3) and a larger number of stocks in the portfolio N (3 vs. the paper’s 1).

With these changes, we were able to squeeze out 5 more points in the hedged variant and almost match the buy & hold momentum index in the long-only variant.

More details can be found here.

The most important contribution of this paper is the parameterization of the holding period and the usage of strands (portfolio sleeves) to create over-lapping portfolios. Something that can be carried over to future research in this area.

Code and charts on github.

In Barardehi, Yashar and Bogousslavsky, Vincent and Muravyev, Dmitriy, What Drives Momentum and Reversal? Evidence from Day and Night Signals (February 6, 2023, SSRN) the authors posit that momentum is entirely an intraday phenomenon (summary).

The authors split the standard past-return momentum signal into its intraday and overnight components and test which piece actually predicts future returns. They find that intraday-signal momentum works – stocks with high past intraday returns keep outperforming – while past overnight returns show no significant predictive power for future returns.

What we found with Indian stocks:

tl;dr: while overnight returns under-perform, intraday returns also under-perform a “total” (close-to-close) return ranking scheme.

Code and charts on github.

Keller, Wouter J., Relative and Absolute Momentum in Times of Rising/Low Yields: Bold Asset Allocation (BAA) (July 18, 2022, SSRN) builds on PAA, VAA and DAA following the same philosophy. The differences between them and some of the weaknesses of BAA is summarized here.

The author swapped out SPY (the S&P 500 ETF) with QQQ (the NASDAQ 100 ETF) to juice returns. We added the original back into our backtest for completeness. Also, we ran a parallel backtest with SPHB (High-Beta ETF) instead of EEM in the canary assets like before.

The results are just as disappointing as they were before.

As much as you might hate 60/40, at least there is no model risk.

Not to belabor the point but, practically speaking, investors are better off managing drawdowns through asset allocation (hence giving up the upside during booms) or through using simple trend-following to avoid steep drawdowns (hence incurring higher transaction costs due to whipsaws) than trying to construct a Rube Goldberg machine.

Code and charts on github.

Keller, Wouter J. and Keuning, Jan Willem, Protective Asset Allocation (PAA): A Simple Momentum-Based Alternative for Term Deposits (April 5, 2016, SSRN) successfully protects your portfolio from returns.

This paper, superseded their VAA and DAA papers. The key differences between them can be found here. The paper used the Emerging Market ETF – EEM – to be one of the canary assets. While EM equities are indeed risky, a risk-off in them doesn’t really mean a risk-off in DM equities. So, we ran a parallel backtest with SPHB (High-Beta ETF) instead of EEM.

Here too, the out-of-sample performance leaves much to be desired.

Once again, investors are better off managing drawdowns through asset allocation (hence giving up the upside during booms) or through using simple trend-following to avoid steep drawdowns (hence incurring higher transaction costs due to whipsaws) than trying to construct a Rube Goldberg machine.

Code and charts are on github.