Relative reversal
Relative reversal
In plain words
When a stock moves far more than its market and sector, for no lasting reason, part of the gap tends to fade over the following days. Relative reversal isolates that stock-specific move and takes the opposite bet.
A relative reversal signal, number by number
This is what a relative reversal signal looks like in PairScanner. The stock and the figures are fictional.
Click a number to learn its role
Expected move+1.6%over 5 sessions, versus market and sector, after costs
My role: tell you what this kind of signal earned on average in the past, once costs are paid. Here +1.6% over 5 sessions, on top of what the market and the sector did.
80% range-1.4% to +4.5%median +1.3%
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.
Probability61%of a positive outcome
My role: estimate the chance that this signal ends positive, after costs. 50% would be a coin flip; 61% means about six similar signals out of ten ended positive.
Sample1,124 / 1,840positive cases, 2019 to 2026
My role: tell you how many past situations the figures rest on. 1,840 cases make a solid base: the figures do not hang on a few lucky trades.
Worst decile-2.6%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 analyzes every stock in its universe. For each one, it estimates over the past year, 252 sessions, how much of its moves is explained by the market, represented by SPY, and by its sector, represented by the sector ETF. What is left is the stock's own move.
When today's own move exceeds two standard deviations of its usual variations, a signal appears: buy after an unexplained drop, sell after an unexplained rise. The algorithm then computes, for 1, 5 and 20 session horizons, the expected move after costs, the estimated probability of a positive outcome, the range of plausible results and the size of an unfavorable outcome. These figures come from replaying the same rule on history.
A variant removes the market only, for stocks whose sector is unknown.
You code nothing and calculate nothing: you open the Relative reversal tab of the dashboard and read today's result.
The idea in detail
Every day a stock's price moves for three reasons: the whole market moves, its sector moves, and something specific happens to the company. The first two parts are easy to measure. The third, the stock's own move, is what this strategy cares about.
When that own move is exceptionally strong, it often reflects an overreaction: hurried sellers, a large order, a misread rumor. Part of the gap then tends to close over the following days.
Not to be confused with short-term reversal, which ranks stocks on their raw weekly performance: relative reversal only looks at what neither the market nor the sector explains.
A worked example
On a given day, the market is flat and banks are up 1%. One bank, however, drops 7%.
| Step | Value |
|---|---|
| Move of the day | -7% |
| Part explained by market and sector | +1% |
| Own move | -8% |
| Usual own variation | 2% per day |
| Gap in standard deviations | -4.0 |
| Decision | Buy |
The own move is four times larger than usual: a buy signal appears. If over the next five sessions the stock gains 3% more than its market and sector, that relative gain is what the strategy measures.
Fictional example for teaching purposes. It is neither a signal nor a recommendation.
Why it would work
Short-term reversal has been documented since 1990: Narasimhan Jegadeesh and Bruce Lehmann showed that stocks that fell the most over a month or a week then outperform those that rose the most.
In 2013, David Blitz, Joop Huij, Simon Lansdorp and Marno Verbeek showed that by first removing the influence of common market factors, so-called residual reversal earned a risk-adjusted return about twice that of classic reversal. That is the principle of relative reversal.
The most common explanation: whoever buys when others sell in a hurry provides liquidity, and gets paid for it when prices come back.
What research says today
In 2014, Zhi Da, Qianqiu Liu and Ernst Schaumburg showed that reversal is clearest once moves tied to the sector and to genuine company news are removed, exactly what this strategy isolates.
In 2012, Stefan Nagel showed that reversal returns are higher when markets are turbulent: the strategy is paid more when liquidity is scarce.
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.
- David Blitz, Joop Huij, Simon Lansdorp and Marno Verbeek (2013). Short-Term Residual Reversal. Journal of Financial Markets, 16(3), 477-504.
- Zhi Da, Qianqiu Liu and Ernst Schaumburg (2014). A Closer Look at the Short-Term Return Reversal. Management Science, 60(3), 658-674.
- Stefan Nagel (2012). Evaporating Liquidity. The Review of Financial Studies, 25(7), 2005-2039.
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 relative reversal signals