Does 12-1 momentum still work?
Updated October 28, 2026
On QuantGPT's test from 1998 to September 2026, the library's Jegadeesh and Titman-inspired 12-1 momentum run beat the S&P 500 with dividends on both growth and Sharpe, and did not fall behind it after the walk-forward split, which QuantGPT labels DATA VALIDATED. It grew 16.5% a year against 9.4%, gross of trading costs, with a Sharpe ratio of 0.67 against 0.66.
Jegadeesh and Titman bought the top tenth of NYSE and AMEX stocks and sold the bottom tenth, 1965 to 1989, holding 3 to 12 months. This run holds only the 20 liquid US stocks with the best 12-1 month return, long only, one month at a time. Its turnover is high: net of 25 bps a side it grew 10.9% a year with a Sharpe ratio of 0.50.
The original claim
Jegadeesh & Titman (1993), Journal of Finance. Stocks that outperformed over the past 3-12 months keep outperforming over the next 3-12 months.
In plain words: each month, rank stocks on their return over the past 3 to 12 months, buy the top tenth, sell short the bottom tenth, equal weight, and hold for 3 to 12 months, so several overlapping portfolios are held at once. The 12-1 version this run uses ranks on the past twelve months' return leaving out the latest month, the form later factor studies adopted.
- Rank NYSE and AMEX stocks on their past 3, 6, 9 or 12-month return (no gap, or a one-week gap)
- Buy the top decile, sell the bottom decile, equal-weighted
- Form monthly and hold 3 to 12 months in overlapping cohorts
In 1965 to 1989 the 6-month/6-month decile strategy earned 12.01% per year compounded (about 0.95% per month); the best variant (12-month formation, 3-month hold, one-week gap) earned about 1.5% per month. Part of the gain reversed in years 2 and 3 and the strategy lost money in Januaries. Profits continued in 1990 to 1998 (Jegadeesh and Titman 2001), with severe crashes in rebounds such as 1932 and 2009 (Daniel and Moskowitz 2016).
How QuantGPT's run differs
Holds the 20 liquid US stocks with the highest 12-1 month return (the past twelve months, leaving out the latest), excluding those up more than 150%, equal weight, long only, re-formed monthly and held one month. Jegadeesh and Titman ranked NYSE and AMEX stocks, 1965 to 1989, on 3 to 12 month returns with no skip or a one-week gap, bought the top decile and sold the bottom, and held 3 to 12 months in overlapping cohorts.
- Universe: liquid US stocks: raw close $5 or more, the top 3,000 by 60-day median dollar volume, 5M shares where the count is known.
- Benchmark: the S&P 500 with dividends reinvested ($SPXTR), bought and held.
- Cash: a month with fewer than 10 qualifying stocks is held in cash. No month was: the first book formed at the end of January 1998, and every one of the 344 months held stocks.
- The engine's own notes: 2 held positions had a period return outside -95% to +200% and were cut to the bound (a guard against bad prints). If a cut return is real, the book's return, volatility and Sharpe differ from what the move would give. Delisted or halted holdings stay in the book: they exit at the last trade inside the holding period, and a holding with no trade at all after formation books -30% (Shumway 1997). In this run 64 held positions had no price at the next formation close: 59 exited at their last trade in the period and 5, with no trade at all, booked -30%.
- In short: Drops stocks up more than 150%, a cap the paper does not have, and holds 20 names for one month, not deciles for 3 to 12 months.
The result
Over this window the criteria beat the benchmark on both growth and Sharpe, and did not fall behind it after the walk-forward split, which QuantGPT labels DATA VALIDATED: a description of what the data did, not a prediction and not a recommendation.
| Strategy | S&P 500 with dividends | |
|---|---|---|
| Growth a year, 1998 to September 2026 | 16.5% | 9.4% |
| Worst fall from a peak | -64.1% | -50.9% |
| Sharpe ratio | 0.67 | 0.66 |
| Volatility a year | 29.6% | 15.4% |
| Share of winning months | 60.8% | 64.5% |
| Growth a year, before 2015 | 16.8% | 6.2% |
| Sharpe ratio, before 2015 | 0.66 | 0.47 |
| Growth a year, from 2015 | 16.1% | 14.2% |
| Sharpe ratio, from 2015 | 0.68 | 0.97 |
| Net of 25 bps a side: growth a year | 10.9% | not modelled (bought and held) |
| Net of 25 bps a side: Sharpe ratio | 0.50 | |
| Net of 25 bps a side: worst fall | -68.9% |
| Cost a side | Growth a year | Sharpe ratio | Worst fall | Cost a year |
|---|---|---|---|---|
| 5 bps | 15.4% | 0.63 | -65.1% | 115 bps |
| 10 bps | 14.2% | 0.60 | -66.1% | 229 bps |
| 25 bps | 10.9% | 0.50 | -68.9% | 564 bps |
| 50 bps | 5.5% | 0.33 | -73.1% | 1104 bps |
Every year
Losing years for the strategy: 2000 (-20.5%), 2002 (-10.7%), 2008 (-44.1%), 2009 (-7.5%), 2018 (-16.5%), 2022 (-1.5%).
| Year | Strategy | S&P 500 with dividends |
|---|---|---|
| 1998 February to December | 15.3% | 27.2% |
| 1999 | 153.3% | 21.0% |
| 2000 | -20.5% | -9.1% |
| 2001 | 42.0% | -11.9% |
| 2002 | -10.7% | -22.1% |
| 2003 | 115.5% | 28.7% |
| 2004 | 12.1% | 10.9% |
| 2005 | 5.3% | 4.9% |
| 2006 | 4.4% | 15.8% |
| 2007 | 1.8% | 5.5% |
| 2008 | -44.1% | -37.0% |
| 2009 | -7.5% | 26.5% |
| 2010 | 55.9% | 15.1% |
| 2011 | 8.3% | 2.1% |
| 2012 | 23.9% | 16.0% |
| 2013 | 46.5% | 32.4% |
| 2014 | 6.3% | 13.7% |
| 2015 | 2.4% | 1.4% |
| 2016 | 5.4% | 12.0% |
| 2017 | 30.1% | 21.8% |
| 2018 | -16.5% | -4.4% |
| 2019 | 30.4% | 31.5% |
| 2020 | 76.9% | 18.4% |
| 2021 | 11.7% | 28.7% |
| 2022 | -1.5% | -18.1% |
| 2023 | 5.3% | 26.3% |
| 2024 | 2.7% | 25.0% |
| 2025 | 46.2% | 17.9% |
| 2026 January to September | 27.2% | 12.7% |
Run it yourself
Open it in the terminal from the strategy library (RUN IT on its entry), or read the public result page at terminal.quantgpt.co/study/jegadeesh-titman-1993. Change the start year, the cutoffs, the number of stocks or the cadence and run it again. Or ask your AI, connected over MCP:
Use QuantGPT to backtest 12-1 momentum, the top 20 stocks re-formed monthly, from 1998 with costs at 25 bps a side, and show the losing years.
Questions
Does momentum investing still work?
On QuantGPT's test it beat the market gross of costs, and trading costs took most of the gap. The library's run, the 20 liquid US stocks with the best 12-1 month return (leaving out those up more than 150%), re-formed monthly, grew 16.5% a year from 1998 to September 2026 against 9.4% for the S&P 500 with dividends, with a Sharpe of 0.67 against 0.66. From 2015 it grew 16.1% a year against 14.2%, with a Sharpe of 0.68 against 0.97. Over this window the criteria beat the benchmark on both growth and Sharpe, and did not fall behind it after the walk-forward split, which QuantGPT labels DATA VALIDATED. Net of 25 bps a side it grew 10.9% a year with a Sharpe of 0.50.
What is 12-1 momentum?
A stock's return over the past twelve months, leaving out the most recent month. Jegadeesh and Titman (1993) ranked stocks on their past 3 to 12 month returns and found that the winners kept beating the losers over the next 3 to 12 months: from 1965 to 1989 the 6-month formation, 6-month hold decile strategy earned 12.01% a year compounded. Later factor studies rank on the twelve months before the latest one, because the latest month tends to reverse.
What were momentum's worst years?
In QuantGPT's run the losing calendar years were 2000 (-20.5%), 2002 (-10.7%), 2008 (-44.1%), 2009 (-7.5%), 2018 (-16.5%), 2022 (-1.5%). Its worst fall from a peak, at month-end closes, was -64.1% against -50.9% for the S&P 500 with dividends. In 2009 the index rose 26.5% while the run lost 7.5%, the kind of rebound Daniel and Moskowitz (2016) call a momentum crash. Gross of trading costs.
How much do trading costs hurt a momentum strategy?
A lot, because the book changes most months: the run's average monthly turnover was 83.3%. Charged per side on that turnover, it grew 15.4% a year at 5 bps, 14.2% at 10 bps, 10.9% at 25 bps and 5.5% at 50 bps, against 16.5% gross and 9.4% for the S&P 500 with dividends, which was bought and held with no cost modelled.
Where can I backtest momentum?
On QuantGPT: from the strategy library with one click (RUN IT on the Jegadeesh and Titman entry opens it in the terminal), or from Claude or ChatGPT connected over MCP, with a question such as: Use QuantGPT to backtest 12-1 momentum, the top 20 stocks re-formed monthly, from 1998 with costs at 25 bps a side, and show the losing years. You can change the lookback, the number of stocks, the cap on past winners, the cadence and the costs. QuantGPT's library documents 175 strategies, 55 of them runnable in one click on decades of US market history. QuantGPT is $49 a month and QuantGPT Pro $149, each with a 3-day free trial.
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Backtests show past results, gross of trading costs unless stated. A backtest is not a promise. QuantGPT is research software and nothing here is investment advice. The disclosures and terms govern.