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#T4 Momentum: do recent winners keep winning?
This page explains an idea and what our tests found when we tried it.
Last checked: 26 October 2026.
Where it comes from
- Traders have long bought "relative strength": the things that have risen most lately. Robert Levy studied it in stocks in 1967.
- The modern result is Narasimhan Jegadeesh and Sheridan Titman's (1993). In US stocks from the 1960s to the 1980s, buying the past 3 to 12 months' winners and selling the losers earned about 1% a month.
- Others extended it:
- Mark Carhart (1997) made momentum a standard factor for judging fund managers.
- Clifford Asness, Tobias Moskowitz and Lasse Pedersen (2013) found it in stocks, bonds, currencies and commodities worldwide.
- Moskowitz, Yao Hua Ooi and Pedersen (2012) found a cousin, time series momentum: a market that has risen tends to keep rising for a while.
- In crypto, Yukun Liu, Aleh Tsyvinski and Xi Wu (2022) found momentum across coins.
- Two neighbours point the other way:
- over a week or a month, recent losers tend to bounce (Jegadeesh 1990; Bruce Lehmann 1990);
- over three to five years, losers tend to recover (Werner De Bondt and Richard Thaler, 1985).
The idea in plain words
Each week or month, rank many coins by how they did lately.
- Buy the strongest and sell the weakest short, in equal amounts.
- The market's own rise or fall then mostly cancels out. What's left is the gap between winners and losers.
If winners keep winning for a while, that gap pays.
The maths, step by step
1. The ranking. Four coins over the last 4 weeks: A +30%, B +10%, C −5%, D −20%. Put half the money long A and half short D.
2. A good week. Next week A rises 4% and D falls 2%.
- Long half: 0.5 × 4% = +2%.
- Short half: 0.5 × 2% = +1% (a short gains when the price falls).
- The book: +3.0%, whether the market rose or fell.
3. A crash. After a sharp fall, the market rebounds, and the beaten- down coins bounce hardest. A rises 5% and D rises 30%.
- 0.5 × 5% − 0.5 × 30% = −12.5% in a week.
- This is a momentum crash: the losers you are short spring back all at once.
4. The fee. Replacing both coins means selling and buying each half of the book: twice the book traded. At 0.055% a trade that's 0.11% of the book. Weekly books pay it often.
5. Ups and downs don't cancel. +3% then −3% is 1.03 × 0.97 = 0.9991: a small loss. Wild weeks cost a book more than their average suggests.
What others found
This section is other people's work, not ours.
- It's one of the best-documented patterns in finance. Eugene Fama and Kenneth French (2008) called it the "premier anomaly": the hardest one for their own models to explain.
- It crashes. Kent Daniel and Tobias Moskowitz (2016) showed that momentum's worst losses come in sharp rebounds after market falls, as in step 3.
- Why it might exist is still argued. One view is that investors react too slowly to news (Barberis, Shleifer and Vishny, 1998; Hong and Stein, 1999). The other is that it is pay for a risk.
What we tested and found
This section is ours. It tests the known pattern on crypto.
- On Binance futures, 2021 to 2026 (every coin, including those later removed): every momentum setting made money, both in the years used to choose and in the last year held back. Every reversed book lost.
- But the one setting fixed in advance didn't beat the best of 16 random books often enough (67% of draws, against a 95% bar), so the test failed.
- About half of the last year's profit was funding, paid by squeezed small coins that could move 90% or more in a week. One, held by the book, fell 97.5% in a single week in July 2026.
- A fresh test on spot, 2018 to 2020, none of it seen before: the same setting made +0.33% a week, 2 of 3 years up, and beat 89% of random books. The bar was 95%, so it is not confirmed.
- Among the largest coins only, 2021 to 2026, momentum lost: −0.24% a week, and every ranking period lost. The momentum we found lived in the small, volatile coins.
A paper test of the same ranking on live prices starts on 19 October 2026; its results will be added here as a dated update.
So the direction we found agrees with the published work, but our own pre-set tests haven't confirmed it, and the version we found depends on coins that can lose most of their value in a week.
The catch
- Crashes. A momentum book can lose a large part of its money in the rebound after a fall (step 3).
- Where the momentum is. In our tests it lived in small, wild coins, where a book can't always trade the size it wants, and where a coin can fall 97.5% in a week (the same fall as above).
- Funding. In crypto, a long in a squeezed coin can be paid large funding. That can make the strategy look better than the price pattern alone.
- Many tries. With so many ways to rank and hold, some setting will look good by luck. That's why we judge against random books.
Limits
- The worked examples use made-up returns for four coins.
- Our tests use one exchange, weekly rebalancing, and a few ranking periods.
Credits
- Robert A. Levy, "Relative Strength as a Criterion for Investment Selection", Journal of Finance, 1967.
- Narasimhan Jegadeesh and Sheridan Titman, "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency", Journal of Finance, 1993.
- Mark M. Carhart, "On Persistence in Mutual Fund Performance", Journal of Finance, 1997.
- Clifford S. Asness, Tobias J. Moskowitz and Lasse Heje Pedersen, "Value and Momentum Everywhere", Journal of Finance, 2013.
- Tobias J. Moskowitz, Yao Hua Ooi and Lasse Heje Pedersen, "Time Series Momentum", Journal of Financial Economics, 2012.
- Yukun Liu, Aleh Tsyvinski and Xi Wu, "Common Risk Factors in Cryptocurrency", Journal of Finance, 2022.
- Narasimhan Jegadeesh, "Evidence of Predictable Behavior of Security Returns", Journal of Finance, 1990; Bruce N. Lehmann, "Fads, Martingales, and Market Efficiency", Quarterly Journal of Economics, 1990.
- Werner F. M. De Bondt and Richard Thaler, "Does the Stock Market Overreact?", Journal of Finance, 1985.
- Eugene F. Fama and Kenneth R. French, "Dissecting Anomalies", Journal of Finance, 2008.
- Kent Daniel and Tobias J. Moskowitz, "Momentum Crashes", Journal of Financial Economics, 2016.
- Nicholas Barberis, Andrei Shleifer and Robert Vishny, "A Model of Investor Sentiment", Journal of Financial Economics, 1998; Harrison Hong and Jeremy C. Stein, "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets", Journal of Finance, 1999.
- Our tests: "Momentum across coins: buy the strongest, sell the weakest", "Momentum across coins, tested again on 2018–2020", and "Momentum among blue chips only: Bitcoin and the top 10".
- PerpGremlin and Claude, Momentum: do recent winners keep winning?, TerminalGremlin, 2026: the tests, the code and the words.
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Educational research from public market data; any trading discussed is paper, never real. Nothing here is advice. The full notice.