Generalized Protective Momentum looks like a ranking strategy until the breadth rule fires. It scores 13 risky assets, selects 3 leaders, and then asks a separate question: how many assets have a positive score at all? If the answer falls to 6 or fewer, the strategy does not reduce risk gradually. It moves the full portfolio to short-term Treasuries.
That all-cash cliff is the feature worth understanding. It is also the feature most likely to create disagreement. A breadth signal can prevent a portfolio from holding the best of a broadly bad set, but a binary cutoff means 1 asset crossing zero can change the allocation from a 3-asset portfolio to 100% defense.
The current BestFolio card for the standard GPM implementation covers 40.5 years, from February 1986 through August 2026. It reports 8.85% annualized return, a 1.14 Sharpe ratio, 7.52% volatility, and a -15.07% maximum drawdown. The modest return and low volatility fit the strategy's intended role: protection and portfolio ballast rather than a standalone return engine.
Step 1: score the universe
GPM begins with 13 assets spanning US and international equities, bonds, real assets, and defensive holdings. Each asset receives a momentum score built from its 1-month, 3-month, 6-month, and 12-month total returns.
The score is then adjusted by how correlated the asset has been with the equal-weight universe. An asset with strong momentum and a lower correlation receives more credit than another leader moving in lockstep with everything else. Keller and Keuning's design is not only looking for what has risen. It is looking for leaders that add something different.
Step 2: count positive scores
Let N be the number of assets with a positive score. With 13 assets in the universe, the rule asks whether more than 6 remain positive. If 6 or fewer pass, the portfolio holds 100% SHV, the short-term Treasury position used for defense.
If more than 6 pass, the defensive fraction rises as breadth weakens. The formula compares the number of failed assets with a threshold set at 1 quarter of the universe. The remaining risk budget is split equally among the top 3 scored assets.
The scaling is steep. With 12 of 13 assets positive, about 10% of the portfolio is already defensive. By 7 positives the defensive fraction is about 62%. One step further is the cliff: at 6 the entire portfolio is in SHV.
This gives the strategy 2 defensive behaviors. Weakening breadth can scale protection upward while the portfolio still owns leaders. Crossing the final threshold sends the whole portfolio to defense.
Why the cliff can help
Relative momentum always finds a winner. In a broad decline, the best-ranked risky asset can simply be the one losing least. The positivity count stops that ranking machine from turning a bad universe into a confident allocation.
The all-cash rule also makes the strategy auditable. There is no discretion about whether the backdrop feels dangerous. A reader can reproduce the 13 scores, count the positives, and know whether the next allocation is risky, partly protected, or fully defensive.
Why the cliff can hurt
Binary thresholds make nearby months look more different than they are. Suppose 6 assets have a slightly positive score and 7 are slightly negative. A small data revision or a different month-end close can change the count and the entire allocation. The portfolio is continuous on one side of the threshold and discontinuous at the boundary.
That raises practical questions for any implementation. Are the inputs adjusted for distributions? Is the score computed from official month-end closes? Does the backtest trade on information that was available at the time? Does a 1-day delay change the historical switches? GPM's published rule is precise enough to test those questions, which is an advantage, but the clean rule does not remove the timing risk.
How I would use it
GPM's 8.85% annualized return should not be compared with a concentrated equity strategy as if the 2 were substitutes. Its 7.52% volatility and -15.07% maximum drawdown show a different job. In a blended portfolio, the question is whether GPM's defensive switches and low-correlation leaders improve the total allocation.
I would also watch the months around the breadth cutoff. A backtest summary can tell us the long-run effect of the rule. The switch log tells us whether the investor could follow it through a cluster of rapid moves between risk and defense.
The design's best idea is simple: do not confuse the highest-ranked asset with a healthy opportunity set. The cost of that idea is a sharp boundary. GPM is most convincing when both pieces are visible.
Explore Generalized Protective Momentum.
Past performance does not guarantee future results. Backtested results are hypothetical and do not represent actual trading.