How much does a biotech stock move on a clinical trial readout?

Less than most people expect. Across 1,752 readouts (2022-2026) the median absolute move from the pre-readout close through the next trading day was 3.8%, and 56.5% of readouts landed within plus or minus 5%. But the tails are real: 7.6% crashed 30% or more and 3.1% ran 50% or more.

Do small-cap biotechs move more on a readout than large-caps?

Dramatically. For a large-cap, 79% of readouts land within plus or minus 5% and only 3.9% crash. For a micro-cap, just 32% are that quiet and 11.7% crash 30% or more. The same event is a non-event for one and a coin-flip for the other.

Do biotech stocks run up before a clinical trial readout?

No systematic run-up. The median move from 30 trading days before the readout to the last close before it was -0.07%. We find the same across FDA decisions and medical-conference presentations: across three independent catalyst types and roughly 4,900 events, there is no systematic pre-catalyst drift.

Is this investment advice?

No. Historical statistics only. No forecast for any company or trial, and no entry, exit or position-sizing guidance.

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Home › Research › Readout reaction

What actually happens when a trial reads out

Updated August 10, 2026·next FDA decision on the calendar

Retail treats every clinical-trial readout as a lottery ticket. We matched 1,752 phase readouts to real stock returns (2022-2026) to see what actually happens. Facts, not advice.

Most readouts are duds, but not as many as you’d be told.

The median readout moved the stock 3.8%, and 56.5% landed within ±5%. That is the headline. The tails are what matter: 7.6% crashed 30%+ and 3.1% ran 50%+. A readout is not a coin flip; it is a mostly-nothing event with a fat, ugly left tail.

Distribution of biotech stock moves on clinical trial readouts, by market cap, and pre-readout run-up across three catalyst types — Cumulative % move from the pre-readout close through the next trading day. Every figure carries its sample size.

1. The distribution

How far did the stock move?	Share of readouts
Within ±2%: a genuine non-event	35.1%
Within ±5%	56.5%
Within ±10%	70.8%
Crashed 30% or more	7.6%
Ran 50% or more	3.1%

A note on honesty. Our own internal tiering calls 68% of these readouts “FLAT”, and it would have been an easy, punchy stat to publish. We didn’t, because that bucket spans −14.9% to +5.0%; it is asymmetric, and a 14% decline is not “nothing.” The honest number is 56.5% within ±5%. If a statistic needs a generous bucket to sound impressive, it isn’t the statistic.

This is corroborated by the peer-reviewed literature.

A published event study in PLOS One (“The reaction of sponsor stock prices to clinical trial outcomes”) finds median cumulative abnormal returns of roughly +0.8% for positive events and −2.0% for negative: i.e. the median readout barely moves the stock. Our retail-scale finding lands in the same place. We are not claiming a discovery; we are putting a well-established academic result in front of the people who need it.

2. The same event is a non-event for a large-cap and a coin-flip for a micro

Cap tier	n	Median \|move\|	Within ±5% (a dud)	Crashed 30%+
Micro	350	8.71%	32%	11.7%
Small	424	6.16%	45%	10.8%
Mid	543	2.99%	63%	5.3%
Large	435	1.67%	79%	3.9%

For a large-cap, 79% of readouts are duds and only 3.9% crash. For a micro-cap, just 32% are quiet and 11.7% crash 30%+: a 3× higher wipe-out rate. Same catalyst class, completely different risk.

3. Is there a run-up into a readout? No.

Window	n	Median	25th pct	75th pct
D-30 → D-1	1748	-0.07%	-7.69%	+8.64%
D-10 → D-1	1750	+0.00%	-4.22%	+4.55%
D-5 → D-1	1751	+0.23%	-2.12%	+2.84%

Three independent catalyst types. The same answer.

Catalyst	n	Median run-up (D-30 → D-1)
FDA decisions (PDUFA)	1,661	+0.43%
Conference presentations	1,425	−0.03%
Clinical trial readouts	1,748	−0.07%

Over 4,834 events across three catalyst classes that share almost nothing, different regulators, different information, different timelines, and all three land within half a percent of zero. No systematic pre-catalyst run-up. The “buy the run-up” folklore that dominates biotech retail is not visible in the data. Individual names run; the population does not.

Corrected 2026-07-13. We previously printed the PDUFA cell as +0.57% (n=1,792). That figure was computed on a panel containing 202 duplicate rows. Deduplicated, it is +0.43% (n=1,661). The conference row was also rebuilt on a wider sampling frame we have since reverted (see the conference study corrections). Neither change alters the conclusion, all three classes still land within half a percent of zero, but we print what the data says, not what we said last week.

We could find no published study of pre-announcement drift for biotech catalysts: the event-study literature concentrates on announcement-day returns. We state that as a gap we could not fill by searching, not as a claim to be first.

Method

Universe. 1,752 clinical-trial readouts by US-listed biotechs, 2022-2026, matched to daily closing prices. Measure. The cumulative % move from the last close before the readout through the first trading day after; readouts are frequently released outside market hours, so a same-day-only measure would miss most of the reaction. Run-up. Trading-day windows to the last pre-readout close. Statistics. Median and interquartile range; we do not report a mean (a handful of 500%+ moves would dominate it) and we never report a “win rate.”

Caveats.

1. Outcome labels are not used here. This page measures price reaction, not whether a trial succeeded. We deliberately do not publish trial success rates from our own labels; 65% of our readouts are unlabelled, and a rate computed on the labelled subset would be biased. For phase success probabilities, cite Wong, Siah & Lo (2019) and BIO/Informa, who measure it properly.

2. Selection. Our universe is readouts we track, weighted toward companies where the readout was material enough to disclose.

3. Dispersion, not prediction. Nothing here forecasts any individual trial. The distribution is wide, and the left tail is the part that ends companies.

Cite this dataset. CC BY 4.0.
“pdufa.bio, Biotech stock reaction to clinical trial readouts (2022-2026), n=1,752, https://www.pdufa.bio/research/readout-reaction”

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