Momentum Crashes: The Hidden Risk in Momentum Investing

Momentum is one of the most documented edges in markets — and one of the most punishing when it breaks. The breaks have a name in the academic literature: momentum crashes. They’re rare. They’re severe. And they cluster in a specific, predictable setting that turns the strategy’s normal logic upside down.

The short version. A momentum crash is a sudden, severe loss in a momentum strategy — typically a multi-month drawdown that arrives during a sharp market rebound after a panic. When beaten-down losers rocket higher fastest, a strategy positioned in recent winners gets caught on the wrong side. Daniel and Moskowitz (2016) showed this pattern is consistent across more than 80 years of data. The good news is the same research that named the problem also pointed toward fixes — scaling exposure to volatility chief among them.

What is a momentum crash?

A momentum crash isn’t just a bad month for a momentum strategy. It’s a specific, recognizable failure mode where momentum doesn’t merely underperform — it can give back years of gains in a matter of weeks.

The defining feature is the setting. Momentum crashes tend to occur during sharp market rebounds following an extended drawdown. The market plunges, panic peaks, and then — fast and violently — it snaps back. The stocks that fell hardest are typically the ones that rip the highest in the rebound, because they had the most room to recover. A momentum strategy, by definition, was avoiding those losers and holding the recent winners. The rebound rewards exactly what momentum had been selling, and punishes exactly what it had been buying.

This isn’t a glitch. It’s the strategy working as designed and getting whipsawed by a regime change.

The research: Daniel and Moskowitz (2016)

The definitive treatment of this failure mode is Kent Daniel and Tobias Moskowitz’s 2016 paper, “Momentum Crashes,” published in the Journal of Financial Economics. They built a long-short U.S. equity momentum portfolio going back to 1927 and isolated every episode where the strategy suffered an extreme negative return.

The pattern was striking. The worst losses were not random across the sample. They concentrated in periods that shared three features:

1. A bear market that had already produced a deep drawdown.
2. Elevated market volatility — the strategy itself was experiencing wider swings in the lead-up.
3. A subsequent market rebound that turned high beta into the best-performing factor for a window of weeks to months.

Two examples in the paper are unmistakable: the rebound off the 1932 lows, and the rebound off the March 2009 low. In both, the long-short momentum strategy posted losses of more than 70% in a matter of months — losses big enough to wipe out years of preceding outperformance.

What made the result citable, not just dramatic, was that Daniel and Moskowitz showed the conditions for the crash were forecastable. The strategy’s own recent volatility and the broader market’s state contained information about the likelihood of a crash forming. The blowups weren’t bolts from the blue. They were predictable from observable inputs — which meant they were, at least in principle, manageable.

Why crashes happen — the mechanics

Three forces compound in a crash:

1. The losers became cheap because they were the most damaged. In a bear market, the names that fall the hardest are typically the most leveraged, most cyclical, or most distressed. Momentum has been short or zero-weighted on those names for months. When the regime flips, they don’t just rebound — they re-rate violently, because the market is repricing not just price but the probability of survival.

2. Momentum’s beta to the market inverts. In normal markets, a long-only momentum strategy has roughly market-like beta. In the kind of bear-then-rebound regime where crashes happen, the winners tend to be lower-beta defensive names (consumer staples, utilities) and the losers tend to be high-beta cyclicals. When the market rebounds, momentum is unintentionally short the highest-beta names in the market — and the rebound clobbers it.

3. Trader behavior reinforces the move. In a panic-rebound, short-covering, forced buying, and a sudden return of risk appetite all concentrate on the most beaten-down names first. The rebound moves through the market unevenly, and momentum is structurally on the wrong side of where it concentrates.

The mechanics aren’t mysterious. They explain why the same pattern shows up in the data again and again across nearly a century.

How crashes have shown up historically

The Daniel-Moskowitz paper documents the U.S. equity record, but momentum crashes are not a U.S. phenomenon — they show up across international equity markets as well, in roughly the same regime.

A few well-known episodes for context:

• 1932 — Following the 1929–32 collapse, the bottoming process produced a multi-month rebound that delivered one of the worst momentum drawdowns in the historical record.
• 2009 — The rebound off the March 2009 low produced a sharp momentum crash; for several months in 2009, beaten-down financials and cyclicals outpaced everything else.
• April 2020 — Less severe than the canonical examples, but the rebound off the COVID-19 March 2020 low produced a meaningful momentum drawdown, particularly for strategies that had rotated into defensives during the panic.

Each of these episodes shares the same anatomy. None of them was a slow underperformance — they were fast, concentrated, and arrived just when an investor was most relieved that the worst was over.

Why momentum still works despite the crashes

It is fair to ask: if momentum has periodic blowups this severe, why does it still rank as one of the strongest factors in the academic record? Two reasons.

First, crashes are rare. Daniel and Moskowitz’s nearly-90-year sample contains only a handful of true crash periods. Between them, momentum tends to produce steady relative outperformance. The long-run record nets the rare crashes against many years of compounding edge — and the edge still wins decisively over decades.

Second, crashes are partially predictable. The same paper that named the problem showed that scaling exposure based on the strategy’s own recent volatility produces materially better risk-adjusted outcomes. Subsequent research has confirmed and extended this — Barroso and Santa-Clara’s “Momentum Has Its Moments” (2015) is the standard citation. The takeaway: investors who treat momentum as a static, always-on strategy bear the full crash risk. Investors who manage exposure based on observable regime signals can keep most of the long-run upside while meaningfully reducing the catastrophic downside.

This is the central point. Momentum crashes are not an argument against momentum. They are an argument against naive momentum.

Managing crash risk in practice

There is no single technique that eliminates crash risk, but several approaches — used together — substantially reduce it.

• Risk-aware ranking. Rather than ranking stocks purely by recent return, rank by a measure that accounts for how volatile each name has been. This tends to tilt the strategy away from the most fragile high-beta names — the very names that bounce hardest in a rebound.
• Strategy-level exposure scaling. When the momentum strategy itself is exhibiting elevated turbulence, run less of it. This is the time-series scaling approach from the Barroso-Santa-Clara line of research, and it’s the most direct way to dampen the crash setup.
• Regime awareness. When broad market conditions deteriorate — drawdowns, volatility spikes, breadth collapses — consider rotating part or all of the strategy’s exposure into defensive assets like short-duration bonds or cash. The cost is some upside; the benefit is sidestepping the worst regimes entirely.
• Diversification across factors. Momentum’s worst periods often coincide with value’s best periods, and vice versa. A portfolio that combines momentum with negatively-correlated factors smooths the worst of either factor’s drawdowns. (See our companion piece, Value and Momentum: Why the Combination Beats Either Alone.)

None of these techniques is exotic. None of them requires a quant team. But they require discipline — and they require building them into the strategy before the next crash setup forms, not afterward.

How SignalStrike approaches crash risk

Crash risk is not an afterthought in how SignalStrike was designed. It’s a first-order concern, reflected in three concrete design choices.

First, the ranking layer offers a volatility-adjusted measure alongside raw momentum, so a strategy can be built that systematically de-emphasizes the most turbulent names from the start. This is the cross-sectional half of the toolkit — applied to which names get into the basket.

Second, the platform includes regime-responsive tools designed for exactly the conditions where momentum gets into trouble — including options to rotate toward bonds or to cash when conditions deteriorate. These don’t eliminate the strategy; they let you scale exposure honestly based on what the market is actually doing.

Third, the entire framework is backtestable and transparent. Every parameter — including the crash-risk-management tools — is visible, configurable, and runs the same way in historical simulation as in the live screen. That means crash-management choices can be evaluated on their own historical record before they’re trusted with real capital. You can build a configuration and test it against the worst historical regimes before risking anything.

For advisors evaluating momentum as a sleeve for client portfolios, the crash-risk treatment is among the most important due-diligence items — and one that’s worth walking through in detail. The full methodology is available to review.

The founders run this on their own capital in live markets, and the public portfolio tracker updates with every rebalance.

Frequently Asked Questions

What is a momentum crash?

A momentum crash is a sudden, severe loss in a momentum investing strategy, typically occurring during a sharp market rebound that follows an extended drawdown. When the market plunges and then snaps back, the most beaten-down losers tend to rebound fastest — but a momentum strategy is positioned in recent winners and against those losers. Daniel and Moskowitz documented the pattern in 2016, showing it has appeared consistently across nearly nine decades of U.S. equity data.

When do momentum strategies fail?

Momentum strategies tend to fail in a specific regime: a bear-market drawdown that resolves with a fast, high-beta rebound. Outside of those conditions, momentum has historically performed in line with or ahead of broad market benchmarks. The largest historical examples — 1932, 2009, and to a lesser extent April 2020 — all share this anatomy. Quiet, trending, single-direction markets are not where momentum gets hurt; sharp regime changes are.

Can momentum crashes be predicted?

Not perfectly, but the conditions that precede them are observable. Daniel and Moskowitz’s research showed that the strategy’s own recent volatility and the broader market’s drawdown state contain information about crash likelihood. That doesn’t mean any particular rebound can be forecast in advance, but it does mean the setup for a crash is visible — which is why scaling exposure based on those signals is the standard mitigation in the academic literature.

How do you manage momentum crash risk?

The main techniques are: ranking stocks by a risk-adjusted measure rather than raw return alone, scaling overall strategy exposure based on its own recent volatility, rotating to defensive assets when market regime signals deteriorate, and combining momentum with negatively-correlated factors like value. None of these eliminates crash risk entirely, but together they substantially reduce drawdowns without giving up most of the long-run edge.

Is momentum investing still worth it given the crash risk?

Decades of academic research suggest the long-run risk-adjusted return of momentum has compensated for the rare crash periods. But that’s a backward-looking statement, not a forward-looking guarantee. A more useful framing: momentum’s edge is real, the crash risk is real, and how an investor treats the crash risk — manage it actively, or accept it passively — is the single biggest determinant of whether their experience matches the long-run academic record.

Further Reading

• Daniel, K., & Moskowitz, T. J. (2016). “Momentum Crashes.” Journal of Financial Economics.
• Barroso, P., & Santa-Clara, P. (2015). “Momentum Has Its Moments.” Journal of Financial Economics.
• Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). “Value and Momentum Everywhere.” Journal of Finance.
• Jegadeesh, N., & Titman, S. (1993). “Returns to Buying Winners and Selling Losers.” Journal of Finance.
• For the broader context on momentum as a factor, see our pillar overview: Momentum Investing: How It Works and Why It Persists.

Related Research

• What Is Momentum Investing? A Complete Guide — Learn how momentum investing works - the academic research, why baskets beat individual picks, and why monthly rebalancing outperforms trailing stops.
• Momentum Investing Research: 30 Years of Academic Findings — The most-cited academic papers on momentum investing — what each study showed, why it matters, and how the field has evolved across three decades of research.
• Value and Momentum: Why the Combination Beats Either Alone — Value and momentum are negatively correlated — they shine and struggle at different times. Why combining them has produced steadier results than either alone.

Disclosures