Short-term reversal
Short-term reversal
In plain words
Stocks that fell the most in a week often bounce a little the following week, and those that rose the most give some back. Short-term reversal buys the former, sells the latter and holds the positions for five sessions.
A short-term reversal signal, number by number
This is what a short-term reversal signal looks like in PairScanner. The stock and the figures are fictional.
Click a number to learn its role
Expected move+1.9%over 5 sessions, after costs
My role: tell you what this kind of signal earned on average in the past, once costs are paid. Here +1.9% over 5 sessions.
80% range-2.1% to +5.8%median +1.5%
My role: show the range of the most common outcomes. Eight similar signals out of ten ended inside it. The narrower it is, the more predictable the outcome.
Probability60%of a positive outcome
My role: estimate the chance that this signal ends positive, after costs. 50% would be a coin flip; 60% means about six similar signals out of ten ended positive.
Sample1,510 / 2,520positive cases, 2019 to 2026
My role: tell you how many past situations the figures rest on. 2,520 cases make a solid base: the figures do not hang on a few lucky trades.
Worst decile-3.4%one outcome in ten did worse
My role: give the size of an unfavorable outcome, so you can size the position knowingly.
ValiditySep 29, 2026expires Oct 6, 2026
My role: date the data used and tell you how long the signal counts. An expired signal is no longer tracked.
What PairScanner does
Every evening after the US close, the algorithm computes the last five sessions' performance of every liquid stock in its universe, then ranks them. The 10% that fell the most give a buy signal, the 10% that rose the most a sell signal.
For each signal, it computes over a five-session horizon the expected move after costs, the estimated probability of a positive outcome, the range of plausible results and the size of an unfavorable outcome, from replaying the same rule on history. A filter leaves out thinly traded stocks so the figures stay realistic.
You code nothing and calculate nothing: you open the Short-term reversal tab of the dashboard and read today's result.
The idea in detail
Over very short periods, prices often overshoot: a hurried seller, a fund that must cut positions, misread news. When the pressure stops, part of the move fades.
The rule is simple: every day, rank stocks by their weekly performance, buy the tenth that fell the most, sell the tenth that rose the most, and hold for five sessions. It is a numbers game: each position is small, and what matters is the average over many stocks.
Not to be confused with relative reversal, which first removes the effect of the market and the sector. And it is the opposite of momentum, which plays the trend over several months.
A worked example
Take ten fictional stocks. With ten stocks, each tenth holds a single one: buy the one that fell the most and sell the one that rose the most. Across several hundred stocks, each group holds dozens.
| Stock | 5-session performance | Decision |
|---|---|---|
| A | -12% | Bought |
| B | -9% | Ignored |
| C | -5% | Ignored |
| D | -2% | Ignored |
| E | 0% | Ignored |
| F | +1% | Ignored |
| G | +3% | Ignored |
| H | +6% | Ignored |
| I | +9% | Ignored |
| J | +14% | Sold |
If the next week A gains back 2% and J slips 1%, the long side gains 2% and the short side 1%, about 1.5% across the capital committed on both sides, before costs.
Fictional example for teaching purposes. It is neither a signal nor a recommendation.
Why it would work
In 1990, Narasimhan Jegadeesh showed that by ranking US stocks on a forecast built from their past returns, the abnormal return gap between the two extreme deciles reached about 2.5% per month, from 1934 to 1987.
The same year, Bruce Lehmann observed the effect at the weekly scale: one week's losers outperform the winners the following week.
The most common explanation: when investors sell in a hurry, whoever buys provides them with liquidity and gets paid for it when prices recover.
What research says today
In 2012, Stefan Nagel showed that short-term reversal returns are higher when markets are turbulent, precisely when liquidity is scarce and hurried sellers are many.
In 2014, Zhi Da, Qianqiu Liu and Ernst Schaumburg showed that the effect gets even clearer once moves tied to the sector and to genuine news are removed: that is the path taken by relative reversal.
Sources
Studies cited on this page:
- Narasimhan Jegadeesh (1990). Evidence of Predictable Behavior of Security Returns. The Journal of Finance, 45(3), 881-898.
- Bruce N. Lehmann (1990). Fads, Martingales, and Market Efficiency. The Quarterly Journal of Economics, 105(1), 1-28.
- Stefan Nagel (2012). Evaporating Liquidity. The Review of Financial Studies, 25(7), 2005-2039.
- Zhi Da, Qianqiu Liu and Ernst Schaumburg (2014). A Closer Look at the Short-Term Return Reversal. Management Science, 60(3), 658-674.
PairScanner promises no results: the figures describe what the strategies did in the past. Educational content, not investment advice; PairScanner places no orders.
See today's short-term reversal signals