What Is Abnormal Return?
Abnormal return is actual return minus the return a model said to expect, so it measures what the model failed to account for rather than what was earned. Because it depends entirely on the model chosen, changing the model changes the abnormality without anything about the investment changing.
Abnormal return is the most commonly misread term in finance research, because the word “abnormal” means something specific and narrow that has nothing to do with how it sounds.
How it works
Take what actually happened, subtract what a model said to expect, and the remainder is abnormal. That is the entire definition.
So it has two inputs and one of them is an assumption. The actual return is a fact. The expected return is a model output, and models are choices.
A simple model leaves more unexplained. Compare a stock to the market alone and much of its movement is abnormal. Add size and value factors and a great deal of that abnormality disappears — into the model, not into the market.
Nothing about the investment changed. Only the explanation did.
What it is actually for
Event studies. The question is whether an announcement moved a price beyond what the market would have done anyway — a merger, an earnings surprise, a drug trial result.
The method is to model the expected return, then measure the gap around the event date. That gap, cumulated over a few days, is the event’s estimated effect.
This is genuinely useful and it is research rather than trading. By the time abnormal return around an event has been measured, the event has happened and the price has moved.
A worked example
Take this site’s shared series. Direction runs average 2.01 bars with a longest of 11, and 54% of 566 ten-bar windows finished higher.
Suppose a model predicts a 54% chance of a higher close. A stretch that rises is not abnormal — it is the base rate arriving.
A stretch that rises much further than the model allowed is abnormal — and with average runs of two bars and a longest of eleven, an eleven-bar run is exactly the kind of observation that shows up as abnormal while being a measured feature of the series.
So the abnormality may be the model failing to account for clustering, not the market doing something extraordinary. The residual is real; the interpretation is where the work is.
The joint hypothesis problem
Any test of abnormal return is testing two things at once. Whether the market behaved unusually, and whether the model of normal was correct.
A significant abnormal return means one of those failed — and the method cannot tell you which.
That is not a technicality. It is the reason decades of research finding “anomalies” remains contested: every anomaly is either a market inefficiency or a missing factor in the model, and the data alone does not separate them.
The original data
On this site’s shared series: 54% of 566 ten-bar windows finished higher. Direction runs average 2.01 bars with a longest of 11. A round trip costs 0.0098, about 2% of the median bar range of 0.493.
That base rate is what a null model would predict, and any strategy result has to be measured against it rather than against zero. A method producing 54% winners has produced exactly nothing abnormal.
And costs are the reason abnormality rarely survives contact with an account. A measured abnormal return of a fraction of a percent is real in a study and gone after 0.0098 per round trip.
The models people actually use
The simplest is the market model: expected return is some constant plus a multiple of the market’s return. Everything not explained by the market is abnormal.
Adding factors shrinks the residual. Size and value were the first two widely adopted, and each addition explains more of what was previously called abnormal — which is progress in modelling and looks like anomalies disappearing.
Every added factor is also a researcher degree of freedom. With enough factors almost any return becomes explained, and the line between a genuine risk factor and a curve fit is not sharply drawn.
So the honest reading of an abnormal return is conditional. Not “this was unusual” but “this was unusual given a particular model of normal, which is itself a hypothesis.” Stated that way the finding is weaker and considerably more accurate.
When it fails
The characteristic failure is reading a published abnormal return as a tradeable edge. A study identifies a statistically significant residual around some event, the finding is real within its model, and the effect is a fraction of a percent measured across hundreds of cases. Trading it means paying the spread on every one of those cases, so a result that is genuinely abnormal in the data is negative after costs. The research was not wrong; it was answering a question about markets rather than about accounts.
A second failure is not stating the model. An abnormal return without its model specified is an uninterpretable number.
A third is testing many events and reporting the significant ones, which manufactures abnormality from multiple comparisons.
A fourth is confusing it with active return, which is a plain benchmark difference with no model of expected return behind it.
And a fifth is treating “abnormal” as praise. It is a residual. A perfectly explained excellent return is still an excellent return.
One practical note on the window. Event studies pick a period around the event — often a few days either side — and the choice of window changes the result. Too narrow and the effect is missed; too wide and unrelated news contaminates it. That choice is made by the researcher, before the answer is known, and it is rarely reported as the judgement call it is.
Related
Active return covers the benchmarked version without a model. Absolute return covers the figure you actually spend. And expectancy covers how many observations are needed before any average means anything.
Abnormal is a technical word that sounds like a judgement. It does not mean good or unusual in any everyday sense — it means a model did not predict it, and models are wrong constantly, which makes abnormality as much a statement about the model as about the market.
— Michael Whitman
This page is educational, not financial advice. Test every idea on your own charts before risking money.